data science

Every eCommerce business depends on information to improve its sales. Data science can source, organize and visualize information. It also helps draw insights about customers, marketing channels, and competitors.

Every piece of information can serve different purposes. You can use data science to improve sales, customer service, user experience, marketing campaigns, purchase journeys, and more.

How to use Data Science to boost eCommerce sales

Sales in eCommerce depend on a variety of factors. You can use data to optimize each step in a customer’s journey to gain conversions and enhance revenue from each conversion.

Analyze Consumer Behavior

Data science can help you learn a lot about the consumer. Understanding consumer behavior is crucial for eCommerce businesses as it dictates the majority of their decisions.

Consumer behavior analysis is all about understanding the relationship between things you can do and customers’ reactions to them. This analysis requires data science as well as psychology. The end goal is not just understanding consumer behavior, but predicting it.

For example, if you have an eCommerce store for antique jewelry, you will want to understand what type of people buy antique jewelry, where they search for it, how they buy it, what information they seek before purchasing, what occasions they buy it for, and so on.

You can extract data on consumer behavior on your website, social media, search engines, and even other eCommerce websites. This data will help you understand customers and predict their behavior. This is crucial for audience segmentation.

Data science can help segment audiences based on demographics, characteristics, preferences, shopping patterns, spending habits, and more. You create different strategies to convert audiences of different segments.

Audience segments play a crucial role in designing purchase journeys, starting from awareness campaigns all the way to purchase and beyond.

Optimize digital marketing for better conversion

You need insights from data analytics to make important marketing decisions. Customer acquisition information can tell you where the majority of your audience comes from. You can also identify which sources give you maximum conversions.

You can then use data to improve the performance of your weak sources and reinforce the marketing efforts of high-performing sources. Either way, you can ensure that your marketing efforts are helping your bottom line.

Once you have locked down your channels of marketing, data science can help you improve results from marketing campaigns. You can learn what type of content or ads perform the best for your eCommerce website.

Data science will also tell you when the majority of your audience is online on the channel and how they interact with your content. Most marketers try to fight the algorithms to win. But with data science, you can uncover the secrets of social media algorithms to maximize your conversions.

Suggest products for upselling & cross-selling

Upselling & Cross-selling are some of the most common sales techniques employed by ecommerce platforms. Data science can help make them more effective. With Market Basket or Affinity Analysis, data scientists can identify relationships between different products. 

By analyzing such information of past purchases and shopping patterns you can derive criteria for upselling and cross-selling. The average amount they spend on a particular type of product tells you how high you can upsell. If the data says that customers are more likely to purchase a particular brand, design, or color; you can upsell accordingly. 

Similarly, you can offer relevant cross-selling suggestions based on customers’ data. Each product opens numerous cross-selling options.

Instead of offering general options, you can use data from various sources to offer targeted suggestions. You can give suggestions based on individual customers’ preferences. For instance, A customer is more likely to click on a suggestion saying “A Red Sweater to go with your Blue Jeans’ ‘ if their previous purchase shows an inclination for the color red.

This way data science can help increase probability of upsold & cross-sold purchases so that eCommerce businesses get more revenue from their customers.

Analyze consumer feedback

Consumers provide feedback in a variety of ways, some of which can only be understood by learning data science. It is not just about reviews and ratings. Customers speak about their experience through social media posts, social shares, and comments as well.

Feedback data can be extracted from several places and usually comes in large volumes. Data scientists use techniques like text analytics, computational linguistics, and natural language processing to analyze this data.

For instance, you can compare the percentage of positive words and negative words used in reviews to get a general idea about customer satisfaction.

But feedback analysis does not stop with language. Consumer feedback is also hidden in metrics like time spent on page, CTR, cart abandonment, clicks on page, heat maps and so on. Data on such sublime behaviors can tell you more about the customer’s experience with your eCommerce website than reviews, ratings and feedback forms.

This information helps you identify problem areas that cause your customers to turn away from a purchase.

Personalize customer experience

To create a personalized experience, you need information about the customer’s behavior, previous purchases, and social activity. This information is scattered across the web, and you need lessons in data science to bring it to one place. But, more importantly, data science helps you draw insights from information.

With this insight you can create different journeys for different customer segments. You utilize data points to map a sequence of options that would lead a customer to conversion. 80% customers are more likely to purchase if the eCommerce website offers a personalized experience.

For example: Your data analytics say that a particular customer has checked out hiking boots but has abandoned most purchases at the cart. Now you can focus on personalizing this customer’s experience by focusing on cart abandonment issues such as additional charges, postage shipping cost, payment options etc.

Several eCommerce websites use data to train their chatbots to serve as personal shopping assistants for their customers. These bots use different data points to give relevant shopping ideas.

You can also draw insights from data science to personalize offers, discounts, landing pages, product gallery, upselling suggestions, cross-selling ideas and more. 

Use data science for decision making & automation

The information provided by data science serves as the foundation for decision-making for eCommerce businesses. In a competitive market, a key piece of information can help you outshine your competitors, gain more customers and provide a better customer experience.

Using data science for business decisions will also help you improve the performance of the company. An informed decision is always better than an educated guess.

In this blog, we asked ChatGPT to come up with some interesting and fun facts about the core areas related to data science, Artificial Intelligence, machine learning, and Statistics. The new AI tool surely knows the struggles of professionals and how to put it in a fun way.

Let’s take a look at some fun facts for data scientists put down by ChatGPT.

Some fun facts for Mathematicians by ChatGPT

Here are some fun facts for all statisticians to make you laugh by ChatGPT:

• Statisticians have a sense of humor too! The probability of someone laughing at a joke you tell is directly proportional to the number of statistical jokes you know.  
• A statistician’s favorite thing to do is to take a sample. 
• A statistician’s favorite drink is a “mean” martini. 
• A statistician’s favorite time of day is “meantime.” 
• Statisticians tend to “mean” well. 
• Statisticians are always looking for a “standard deviation” from the norm. 
• Statisticians are always looking for a “means” to get ahead. 
• Statisticians are never “mean” to each other – they are always “normally distributed.”
• Mathematicians are always on the lookout for new “equations” to solve.  
• A mathematician’s favorite thing is to “prove” a theorem.  
• Mathematicians are known to have a “matrix” of connections in the industry. 
• Mathematicians have been known to “calculate” the most unlikely scenarios. 
• A mathematician’s favorite type of math is “abstract” math – it keeps things interesting! 
• Mathematicians are always looking for ways to “optimize” their equations. 
• Mathematicians have a saying: “The more complex the problem, the better!” 
• Mathematicians are known to be “precise” – they never leave room for error in their calculations.  

Some fun facts for Data Scientists by ChatGPT  

Here are a few funny facts about data scientists: 

• Data scientists have been known to “mine” for data in the most unlikely places. 
• A data scientist’s favorite type of data is “big data” – the bigger, the better! 
• A data scientist’s favorite tool is the “data hammer” – they can pound any data into submission. 
• Data scientists have a saying: “The data never lies, but it can be misleading.” 
• Data scientists have been known to “data dunk” their colleagues – throwing them into a pool of data and seeing if they can swim. 
• Data scientists are always “data mining” for new insights and discovering “data gold.” 
• Data scientists are known to have “data-phoria” – a state of excitement or euphoria when they uncover a particularly interesting or valuable piece of data. 
• Data scientists have been known to “data mash” – combining different datasets to create something new and interesting. 

 Enroll in our Data Science Bootcamp course to become a Data Scientist today

Some fun facts for Machine Learning professionals by ChatGPT 

Here are some fun facts about machine learning professionals   

• Machine learning professionals are always on the lookout for new “learning opportunities.” 
• A machine learning professional’s favorite thing is to “train” their algorithms. 
• Machine learning professionals are known to have a “neural network” of friends in the industry. 
• Machine learning professionals have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• A machine learning professional’s favorite type of data is “clean” data – it makes their job much easier! 
• Machine learning professionals are always looking for ways to “optimize” their algorithms. 
• Machine learning professionals have a saying: “The more data, the merrier!” 
• Machine learning professionals are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

Some fun facts for AI experts by ChatGPT 

Here are a few funny facts about artificial intelligence experts:   

• AI experts are always on the lookout for new “intelligent” ideas. 
• AI experts have been known to “teach” their algorithms to do new tasks. 
• AI experts are known to have a “neural network” of connections in the industry. 
• AI experts have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• AI experts are always looking for ways to “optimize” their algorithms. 
• AI experts have a saying: “The more data, the smarter the AI!” 
• AI experts are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 
• AI experts are always looking for ways to make their algorithms more “human-like.”  
• The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy. 
• The first recorded instance of artificial intelligence was in the early 1800s when mathematician Charles Babbage designed a machine that could perform basic mathematical calculations. 
• One of the earliest demonstrations of artificial intelligence was the “Turing Test,” developed by Alan Turing in 1950. The test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. 
• The first self-driving car was developed in the 1980s by researchers at Carnegie Mellon University. 
• In 1997, a computer program called Deep Blue defeated world chess champion Garry Kasparov, marking the first time a computer had beaten a human at chess. 
• In 2011, a machine translation system developed by Google called Google Translate was able to translate entire documents from Chinese to English with near-human accuracy. 
• In 2016, a machine learning algorithm developed by Google DeepMind called AlphaGo defeated the world champion at the ancient Chinese board game Go, which is considered to be much more complex than chess. 
• Artificial intelligence has the potential to revolutionize a wide range of industries, including healthcare, finance, and transportation.  

Some fun facts for Data Engineers by ChatGPT 

Here are a few funny facts about data engineers by ChatGPT: 

• Data engineers are always on the lookout for new “pipelines” to build. 
• A data engineer’s favorite thing is to “ingest” large amounts of data. 
• Data engineers are known to have a “data infrastructure” of connections in the industry. 
• Data engineers have been known to “scrape” the internet for new data sources. 
• A data engineer’s favorite type of data is “structured” data – it makes their job much easier! 
• Data engineers are always looking for ways to “optimize” their data pipelines. 
• Data engineers have a saying: “The more data, the merrier!” 
• Data engineers are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

Do you have a more interesting answer by ChatGPT?

People across the world are generating interesting responses using ChatGPT. The new AI tool has an immense contribution to the knowledge of professionals associated with different industries. Not only does it produce witty responses but also shares information that is not known by many. Share with us your use of this amazing AI tool as a Data Scientist.

In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand.   

Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career.   

To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.   

If you are planning to add value to your data science skillset, check out our Python for Data Science training.  

Let’s get started with the list: 

 1. A day in the life of a data scientist

 This talk will introduce you to what a typical data scientist’s job looks like. It will familiarize you with the day-to-day work that a data scientist does and differentiate between the different roles and responsibilities that data scientists have across companies.   

This talk will help you understand what a typical day in the data scientist’s life looks like and assist you to decide if data science is the perfect choice for your career.   

2. Data mining crash course

Data mining has become a vital part of data science and analytics in today’s world. And, if you planning to jumpstart your career in the field of data science, it is important for you to understand data mining. Data mining is a process of digging into different types of data and data sets to discover hidden connections between them.

The concept of data mining includes several steps that we are going to cover in this course.  In this talk, we will cover how data mining is used in feature selection, connecting different data attributes, data aggregation, data exploration, and data transformation.

Additionally, we will cover the importance of checking data quality, reducing data noise, and visualizing the data to demonstrate the importance of good data.  

3. Intro to data visualization with R & ggplot2 

While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. Thereby, it is important for one to learn about data visualization with R & ggplot2.   

In this tutorial, you will get a brief introduction to data visualization with the ggplot2 package. The focus of the tutorial will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.   

 4. Crash course in data visualization: Tell a story with your data

Telling a story with your data is more important than ever. The best insights and machine learning models will not create an impact unless you are able to effectively communicate with your stakeholders. Hence, it is very important for a data scientist to have an in-depth understanding of data visualization.   

 In this course, we will cover chart theory and pair programs that will help us create a chart using Python, Pandas, and Plotly.   

5. Feature engineering 

To become a proficient data scientist, it is significant for one to learn about feature engineering. In this talk, we will cover ways to do feature engineering both with dplyr (“mutate” and “transmute”) and base R (“ifelse”). Additionally, we’ll go over four different ways to combine datasets.   

With this talk, you will learn how to impute missing values as well as create new values based on existing columns.  

6. Intro to machine learning with R & caret 

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open-source machine-learning algorithms. If you are a data scientist working with R, the caret package (short for Classification and Regression Training) is a must-have tool in your toolbelt.   

In this talk, we will provide an introduction to the caret package. The focus of the talk will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work.   

7. Building robust machine learning models 

Modern machine learning libraries make the model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent.   

Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Thereby, our aim here is to take you through the fundamentals of building robust machine-learning models.  

8. Text analytics crash course with R

 Industries across the globe deal with structured and unstructured data. To generate insights companies, work towards analyzing their text data. The data pipeline for transforming unstructured text into valuable insights consists of several steps that each data scientist must learn about.   

This course will take you through the fundamentals of text analytics and teach you how to transform text data using different machine-learning models.   

9. Translating data into effective decisions

As data scientists, we are constantly focused on learning new ML techniques and algorithms. However, in any company, value is created primarily by making decisions. Therefore, it is important for a data scientist to embrace uncertainty in a data-driven way.   

 In this talk, we present a systematic process where ML is an input to improve our ability to make better decisions, thereby taking us closer to the prescriptive ideal.   

10. Data science job interviews 

Once you are through your data science learning path, it is important to work on your data science interviews in order to uplift your career. In this talk, you will learn how to solve SQL, probability, ML, coding, and case interview questions that are asked by FAANG + Wall Street.  

 We will also share the contrarian job-hunting tips that can help you to find a job at Facebook, Google, or an ML startup.  

Step up to the data science pathway today!

We hope that the aforementioned 12 talks assist you to get started with your data science learning path. If you are looking for a more detailed guide, then do check out our Data Science Roadmap. 

If you want to receive data science blogs, infographics, cheat sheets, and other useful resources right into your inbox, subscribe to our weekly & monthly newsletter. 

Whether you are new to data science or an expert, our upcoming talks, tutorials, and crash courses can help you learn diverse data science & engineering concepts, so make sure to stay tuned with us. 

The finance industry deals with large volumes of data. With the increase in data and the accessibility of AI, financial institutions can’t ignore the benefits of data science. They have to use data science to improve their services and products. It helps them make better decisions about customer behavior, product development, marketing strategies, etc.

From using machine learning algorithms to Python for Data Science, there are several key methods of applications of data science in finance. Listed below are the top eight examples of data science being used in the finance industry.

1. Trend forecasting

Data science plays a significant role in helping financial analysts forecast trends. For instance, data science uses quantitative methods such as regression analysis and linear programming to analyze data. These methods can help extract hidden patterns or features from large amounts of data, making trend forecasting easier and more accurate for financial institutions

2. Fraud detection

Financial institutions can be vulnerable to fraud because of their high volume of transactions. In order to prevent losses caused by fraud, organizations must use different tools to track suspicious activities. These include statistical analysis, pattern recognition, and anomaly detection via machine/deep learning. By using these methods, organizations can identify patterns and anomalies in the data and determine whether or not there is fraudulent activity taking place.

For example, financial institutions often use historical transaction data to detect fraudulent behavior. So when banks detect inconsistencies in your transactions, they can take action to prevent further fraudulent activities from happening.

3. Market research

Tools such as CRM and social media dashboards use data science to help financial institutions connect with their customers. They provide information about their customers’ behavior so that they can make informed decisions when it comes to product development and pricing.

Remember that the finance industry is highly competitive and requires continuous innovation to stay ahead of the game. Data science initiatives, such as a Data Science Bootcamp or training program, can be highly effective in helping companies develop new products and services that meet market demands.

4. Investment management

Investment management is another area where data science plays an important role. Companies use data-driven approaches to optimize investment portfolios. They also use predictive models, such as financial forecasting, to estimate future returns based on past performance. Such predictions allow investors to maximize profits and minimize risks when it comes to investing. In addition to providing valuable insight into the future, data science also provides guidance on how to best allocate capital and reduce risk exposure.

5. Risk analysis

Risks are unavoidable in any organization. However, managing those risks requires understanding their nature and causes. In the finance industry, companies use data science methods such as risk assessment and analysis to protect themselves against potential losses.

For example, they can tell you which products are likely to fail, and which assets are most susceptible to theft and other types of loss. And when applied properly, these tools can help an organization improve security, efficiency, and profitability.

6. Task automation

One of the greatest challenges faced by many firms today is the need to scale up operations while maintaining efficiency. To do so, they must automate certain processes. One way to achieve this goal is through the use of data science. Data scientists can develop tools that improve existing workflows within the finance industry.

Examples of these tools include speech-to-text, image recognition, and natural language processing. The finance industry uses insights from data science to automate systems that eliminate human error and accelerate operational efficiency.

7. Customer service

It’s no surprise that customer satisfaction affects revenue growth. As a result, companies spend large amounts of money to ensure that their customers receive top-notch service. Data science initiatives can help financial services providers deliver a superior experience to their customers. Whether it’s improving customer support apps or streamlining internal communications, financial companies can leverage this technology to transform their operations.

For instance, financial institutions can track consumer behavior to provide better customer service. A company may use data analytics to identify the best time to contact consumers by analyzing their online behavior. Companies can also monitor social media conversations and other sources for signs of dissatisfaction regarding their services to improve customer satisfaction.

8. Scalability

For certain financial institutions, the ability to scale up could mean the difference between success and failure. The good news is that data science offers solutions and insight that help companies identify what areas need to be scaled. These insights help them decide whether they should hire additional staff or invest in new equipment, among other things.

A good example of using data analytics for scalability is IBM’s HR Attrition Case Study. IBM, one of the world’s leading technology firms, has been able to use data science to solve its own scaling challenges by using it to analyze trends and predict future outcomes. This study shows how data scientists used predictive analytics to understand why employees quit their jobs at IBM.

Data science revolutionizing finance industry

There’s no doubt that data science will revolutionize almost all aspects of the financial industry. By using different data science tools and methods, financial companies can gain competitive advantages. The great thing about data science is that it can be learned through various methods.

Data science bootcamps, online courses, and books offer all the tools necessary to get started. As a result, anyone who works in finance—whether they are junior analysts or senior executives—can learn how to incorporate data science techniques in their industry.

There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out. 

1. Analytics Power Hour 

Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show. 

Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen. 

2. DataFramed

This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.   

There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more. 

3. Lex Fridman Podcast

If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for. 

You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now. 

4. The Artists of Data Science

This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead. 

There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals. 

5. Not So Standard Deviations

This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.

Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms. 

Find out other exciting  18 Data Science podcasts

6. Gradient Dissent  

Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before. 

Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts. 

7. In Machines We Trust

This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in. 

“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 mins long each, so it won’t take long to listen and get the latest info that you’re looking for. 

8. More or Less

This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.   

For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy. 

9. Data Engineering Podcast

This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face. 

Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips. 

10. Data viz Today

This show doesn’t need a lot of commitment from you, as they release 30 min episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in. 

Share with us exciting Data Science podcasts

These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!

These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.

Most people have heard the terms “data science” and “AI” at least once in their lives. Indeed, both of these are extremely important in the modern world, as they are technologies that help us run quite a few of our industries. 

But even though data science and Artificial Intelligence are somewhat related to one another, they are still very different. There are things they have in common, which is why they are often used together, but it is crucial to understand their differences as well. 

What is Data Science? 

As the name suggests, data science is a field that involves studying and processing large quantities of data using a variety of technologies and techniques to detect patterns, make conclusions about the data, and aid in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries. 

The standard data science lifecycle includes capturing data and then maintaining, processing, and analyzing it before finally communicating conclusions about it through reporting. This makes data science extremely important for analysis, prediction, decision-making, problem-solving, and many other purposes. 

What is Artificial Intelligence? 

Artificial Intelligence is the field that involves the simulation of human intelligence and the processes within it by machines and computer systems. Today, it is used in a wide variety of industries and allows our society to function as it currently does by using different AI-based technologies. 

Some of the most common examples in action include machine learning, speech recognition, and search engine algorithms. While AI technologies are rapidly developing, there is still a lot of room for their growth and improvement. For instance, there is no powerful enough content generation tool that can write texts that are as good as those written by humans. Therefore, it is always preferred to hire an experienced writer to maintain the quality of work.  

What is Machine Learning? 

As mentioned above, machine learning is a type of AI-based technology that uses data to “learn” and improve specific tasks that a machine or system is programmed to perform. Though machine learning is seen as a part of the greater field of AI, its use of data puts it firmly at the intersection of data science and AI. 

Similarities between Data Science and AI 

By far the most important point of connection between data science and Artificial Intelligence is data. Without data, neither of the two fields would exist, and the technologies within them would not be used so widely in all kinds of industries. In many cases, data scientists and AI specialists work together to create new technologies, improve old ones, and find better ways to handle data. 

As explained earlier, there is a lot of room for improvement when it comes to AI technologies. The same can be somewhat said about data science. That’s one of the reasons businesses still hire professionals to accomplish certain tasks, like custom writing requirements, design requirements, and other administrative work.  

Differences between Data Science and AI 

There are quite a few differences between both. These include: 

• Purpose – It aims to analyze data to make conclusions, predictions, and decisions. Artificial Intelligence aims to enable computers and programs to perform complex processes in a similar way to how humans do. 
• Scope – This includes a variety of data-related operations such as data mining, cleansing, reporting, etc. It primarily focuses on machine learning, but there are other technologies involved too such as robotics, neural networks, etc. 
• Application – Both are used in almost every aspect of our lives, but while data science is predominantly present in business, marketing, and advertising, AI is used in automation, transport, manufacturing, and healthcare. 

Examples of Data Science and Artificial Intelligence in use 

To give you an even better idea of what data science and Artificial Intelligence are used for, here are some of the most interesting examples of their application in practice: 

• Analytics – Analyze customers to better understand the target audience and offer the kind of product or service that the audience is looking for. 
• Monitoring – Monitor the social media activity of specific types of users and analyze their behavior. 
• Prediction – Analyze the market and predict demand for specific products or services in the nearest future. 
• Recommendation – Recommend products and services to customers based on their customer profiles, buying behavior, etc. 

• Forecasting – Predict the weather based on a variety of factors and then use these predictions for better decision-making in the agricultural sector. 
• Communication – Provide high-quality customer service and support with the help of chatbots. 
• Automation – Automate processes in all kinds of industries, from retail and manufacturing to email marketing and pop-up on-site optimization. 
• Diagnosing – Identify and predict diseases, give correct diagnoses, and personalize healthcare recommendations. 
• Transportation – Use self-driving cars to get where you need to go. Use self-navigating maps to travel. 

• Assistance – Get assistance from smart voice assistants that can schedule appointments, search for information online, make calls, play music, and more. 
• Filtering – Identify spam emails and automatically get them filtered into the spam folder. 
• Cleaning – Get your home cleaned by a smart vacuum cleaner that moves around on its own and cleans the floor for you. 
• Editing – Check texts for plagiarism, proofread, and edit them by detecting grammatical, spelling, punctuation, and other linguistic mistakes. 

It is not always easy to tell which of these examples is about data science and which one is about Artificial Intelligence because many of these applications use both of them. This way, it becomes even clearer just how much overlap there is between these two fields and the technologies that come from them. 

What is your choice?

At the end of the day, data science and AI remain some of the most important technologies in our society and will likely help us invent more things and progress further. As a regular citizen, understanding the similarities and differences between the two will help you better understand how data science and Artificial Intelligence are used in almost all spheres of our lives. 

In this blog, we will discuss how companies apply data science in business and use combinations of multiple disciplines such as statistics, data analysis, and machine learning to analyze data and extract knowledge. 

If you are a beginner or a professional seeking to learn more about concepts like Machine Learning, Deep Learning, and Neural Networks, the overview of these videos will help you develop your basic understanding of Data Science.  

Overview of the data science course for beginners 

If you are an aspiring data scientist, it is essential for you to understand the business problem first. It allows you to set the right direction for your data science project to achieve business goals.  

As you are assigned a data science project, you must assure yourself to gather relevant information around the scope of the project. For that you must perform three steps: 

1. Ask relevant questions from the client 
2. Understand the objectives of the project 
3. Defines the problem that needs to be tackled 

As you are now aware of the business problem, the next step is to perform data acquisition. Data is gathered from multiple sources such as: 

• Web servers 
• Logs 
• Databases 
• APIs 

• Online repositories 

1. Getting Started with Python and R for Data Science 

Python is an open source, high-level, object-oriented programming language that is widely used for web development and data science. It is a perfect fit for data analysis and machine learning tasks, as it is easy to learn and offers a wide range of tools and features.  

Python is a flexible language that can be used for a variety of tasks, including data analysis, programming, and web development. Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. 
 

Getting started with Python and R for Data Science

Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set. Python is an ideal language for data scientists who are looking to learn more about data analysis and machine learning. It is used to accomplish a variety of tasks, including data analysis, programming, and web development.  

Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set.  

2. Intro to Big Data, Data Science & Predictive Analytics 

Big data is a term that has been around for a few years now, and it has become increasingly important for businesses to understand what it is and how it can be used. Big data is basically any data that is too large to be stored on a single computer or server and instead needs to be spread across many different computers and servers in order to be processed and analyzed.  

The main benefits of big data are that it allows businesses to gain a greater understanding of their customers and the products they are interested in, which allows them to make better decisions about how to market and sell their products. In addition, big data also allows businesses to take advantage of artificial intelligence (AI) technology, which can allow them to make predictions about the future based on the data they are collecting. 

Intro to Big Data, Data Science & Predictive Analytics 

The main areas that businesses need to be aware of when they start using big data are security and privacy. Big data can be extremely dangerous if it is not properly protected, as it can allow anyone with access to the data to see the information that is being collected. In addition, big data can also be extremely dangerous if it is not properly anonymized, as it can allow anyone with access to the data to see the information that is being collected. 

One of the best ways to protect your data is by using encryption technology. Encryption allows you to hide your data from anyone who does not have access to it, so you can ensure that no one but you have access to your data. However, encryption does not protect 

 3. Intro to Azure ML & Cloud Computing 

Cloud computing is a growing trend in IT that allows organizations to perform delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence. Cloud offers a number of benefits, including reduced costs and increased flexibility.  

Organizations can take advantage of the power of the cloud to reduce their costs and increase flexibility, while still being able to stay up to date with new technology. In addition, organizations can take advantage of the flexibility offered by the cloud to quickly adopt new technologies and stay competitive. 

Intro to Azure ML & Cloud Computing 

In this intro to Azure Machine learning & Cloud Computing, we’ll cover some of the key benefits of using Azure and how it can help organizations get started with machine learning and cloud computing. We’ll also cover some of the key tools that are available in Azure to help you get started with your machine learning and cloud computing projects. 

Start your Data Science journey today 

If you are afraid of spending hundreds of dollars to enroll in a data science course, then direct yourself to the hundreds of free videos available online. Master your Data Science learning and step into the world of advanced technology. 

Data science is used in different fields and industries. And believe it or not, it also plays a significant role in digital marketing. In this post, that is what we’re going to be discussing. 

Data science is a big field, and it is employed extensively in different industries, from healthcare and transport to education and commerce. In fact, it is the cornerstone of groundbreaking technologies such as AI-based virtual assistants and self-driving cars. 

The definition of data science proffered by The Journal of Data Science is: 

“By ‘Data Science’, we mean almost everything that has something to do with data.” 

Looking at this definition, it’s easy to appreciate the fact that there is virtually no field or industry that does not utilize data science in some capacity. It’s everywhere, albeit in varying degrees. 

And as such, it’s also utilized in digital marketing. 

At a glance, it can be a little difficult to understand just how data science plays a role in digital marketing and how it benefits the same. But don’t worry. That’s what we’re going to be clearing up in this post. 

What is Data Science? 

We want to start off with the basics, so let’s look at what data science is. Although we did start off with a definition from The Journal of Data Science, it’s not very explanatory. 

Data science can be defined as the field or study that deals with finding and extracting useful and meaningful statistics and insights from a collection of structured and unstructured data. 

If we wanted to, we could go a little sophisticated and step into the shoes of some sage from the Middle Ages to define data science as “…to make ordered, that which is unordered…”. It’s a bit much, but it conveys the idea nicely. 

The process involved in data science is divided into various steps, which are collectively known as the Data Science Life Cycle. There aren’t any specific steps that can be universally enumerated as being part of the Data Science life cycle but, generally, it involves the following: 

• Data collection 
• Data organization 
• Data processing i.e., data mining, data modeling etc. 
• Data analysis 
• Finalization of results 

If you want, you can learn more about data science by taking this course. 

How Data Science is useful in digital marketing 

Now that we’re done with this preamble, let’s move on to discuss how data science can be useful in digital marketing. 

1. Keyword research 

One of the main benefits of data science in digital marketing is providing help with keyword research. Actually, before moving on, let’s clear up how exactly keyword research is related to digital marketing. 

Keyword research is a vital and necessary part of Search Engine Optimization (SEO). And SEO itself is a major branch of digital marketing. That’s basically how these two are connected. 

Let’s get back to the point. 

Whenever a digital marketing expert wants to work on the SEO of their website, they first have to create a keyword strategy for the content. The keyword strategy basically describes the short-tail and long-tail keywords that have to be featured in the website’s content and metadata. It also describes the number of times that the keywords have to be used and so on. 

Now, there is no limit to the number of keywords that are (and can be) searched by online users. They literally run into trillions. When someone has to select a few from this vast and virtually endless trove of keywords, they have to employ data science. 

Read more about marketing analytics features

6 marketing analytics features to drive greater revenue

Here is how data science can work in keyword research: 

• For the first phase, the digital marketer (or the SEO specialist) will narrow the keywords down to the ones related to their niche. This is, as we mentioned above, the “data collection” step. 
• Then, from this collection of keywords, the ones with high search volumes will be prioritized and short-listed. This is the “data organization” step. 
• After this, the specialist will have to find those long-tail and short-tail keywords that have a manageable ranking difficulty. In other words, this step will entail going through the shortlisted keywords and handpicking the most suitable ones. 
• Then, the selected keywords will be refined even more until the finalized list is prepared. This can be referred to as the “data analysis” step. 
• And once all the above is done, the list of keywords will be prepared in a document and given to the relevant personnel. This is the last step of the data science life cycle. 

So, taking a look from the first step of the process to the last one, we can observe that from a list of infinite keywords, a selected number of them were handpicked and finalized. Again, this is basically what data science is. To find patterns and useful insights from unsorted or sorted data. 

2. Analysis of website performance metrics 

This is yet another instance of digital marketing where data science can be highly beneficial. 

Basically, digital markers have to keep an eye on the performance of their website or online platform. They have to see how users are interacting with the various web pages and how much traffic the website(s) is/are generating. 

To measure website performance, there are actually a lot of different stats and metrics. For example, some of them include: 

• Dwell time 
• Bounce rate 
• Amount of traffic 
• Requests per second 
• Error rate 

By employing data science strategies to gather and analyze the various metrics, digital marketers can easily understand how well their website is working and how users are interacting with it. 

Similarly, by analyzing these metrics, they can also easily find out if the website (or a particular webpage) has been hit by a search engine penalty. This is actually a very useful benefit of keeping on top of website performance metrics. 

There are different types of violations that can bring about a penalty from the search engine, or that can just simply reduce the traffic/popularity of a certain webpage. 

For one, if a page takes a lot of time to load, it can get abandoned by a lot of users. This can be detected if there is a rise in the bounce rate and a decrease in the dwell time. Incidentally, the loading time itself is a website performance metric on its own. 

To improve the loading time, methods such as code beautification and minimization can be used. Similarly, the images and effects featured on the page can be toned down etc. 

Plagiarism is also a harmful factor that can get websites penalized. These types of penalties can either reduce a website’s rank or get it completely de-listed. 

To avoid this, webmasters always have to check plagiarism before finalizing any content for their websites. 

This is usually done with the help of plagiarism-checking tools that can scan the given content against the internet in order to find any duplication that may exist in the former. 

3. Monitoring website ranking statistics 

Just as monitoring website performance by analyzing statistics like the bounce rate, dwell time etc., is important, staying on top of the ranking statistics is equally necessary. 

By staying up-to-date with the website ranking in the SERPs, digital marketers are able to adjust and manage their SEO strategies. If upon taking a certain step, the rank of the site drops, then it means that it (the step) should not be taken in future. On the other hand, if the rank rises after making some changes to the website, then it is a signal indicating that the changes are beneficial rather than harmful. 

Data science can be employed for keeping up with this information as well. 

Grow digital marketing with Data Science

There are actually a lot of other ways in which data science can be useful in digital marketing. But, since we want to stick to brevity, we’ve listed some common and main ones above. 

Get hired as a Data Analyst by confidently responding to the most frequently asked interview questions. No matter how qualified or experienced you are, if you stumble over your thoughts while answering the interviewer, it might take away some of your chances of getting onboard. 

In this blog, you will find the top data analysts interview questions covering both technical and non-technical areas of expertise.  

List of Data Analysts interview questions 

1. Share about your most successful/most challenging data analysis project? 

In this question, you can also share your strengths and weaknesses with the interviewer.   

When answering questions like these, data analysts must attempt to share both their strengths and weaknesses. How do you deal with challenges and how do you measure the success of a data project? You can discuss how you succeeded with your project and what made it successful.  

Take a look at the original job description to see if you can incorporate some of the requirements and skills listed. If you were asked the negative version of the question, be honest about what went wrong and what you would do differently in the future to fix the problem. Despite our human nature, mistakes are a part of life. What’s critical is your ability to learn from them. 

Further talk about any SAAS platforms, programming languages, and libraries. Why did you use them and how did you use them to accomplish yours?

Discuss the entire pipeline of your projects from collecting data, to turning it into valuable insights. Describe the ETL pipeline, including data cleaning, data preprocessing, and exploratory data analysis. What were your learnings and what issues did you encounter, and how did you deal with them. 

Enroll in Data Science Bootcamp today to begin your journey

2. Tell us about the largest data set you’ve worked with? Or what type of data have you worked with in the past? 

What they’re really asking is: Can you handle large data sets?  

Data sets of varying sizes and compositions are becoming increasingly common in many businesses. Answering questions about data size and variety requires a thorough understanding of the type of data and its nature. What data sets did you handle? What types of data were present? 

It is not necessary that you only mention a dataset you worked with at your job. But you can also share about varying sizes, specifically large datasets, you worked with as a part of a data analysis course, Bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.  

The more versatile your experience with datasets will be, the greater the chances there are of getting hired.  

Read more about several types of datasets here:

32 datasets to uplift your skills in data science

3. What is your process for cleaning data? 

The expected answer to this question will include details about: How you handle missing data, outliers, duplicate data, etc.?c.? 

Data analysts are widely responsible for data preparation, data cleansing, or data cleaning. Organizations expect data analysts to spend a significant amount of time preparing data for an employer. As you answer this question, share in detail with the employer why data cleaning is so important. 

In your answer, give a short description of what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set. 

 Learn about Data Science Interview Questions and begin your career as a data scientist today.

4. Name some data analytics software you are familiar with. OR what data software have you used in the past? OR What data analytics software are you trained in? 

What they need to know: Do you have basic competency with common tools? How much training will you need? 

Before you appear for the interview, it’s a good time to look at the job listing to see what software was mentioned. As you answer this question, describe how you have used that software or something similar in the past. Show your knowledge of the tool by employing associated words.  

Mention software solutions you have used for a variety of data analysis phases. You don’t need to provide a lengthy explanation. What data analytics tools you used and for what purpose will satisfy the interviewer. 

5. What statistical methods have you used in data analysis? OR what is your knowledge of statistics? OR how have you used statistics in your work as a Data Analyst? 

What they’re really asking: Do you have basic statistical knowledge? 

Data analysts should have at least a rudimentary grasp of statistics and know-how that statistical analysis helps business goals. Organizations look for a sound knowledge of statistics in Data analysts to handle complex projects conveniently. If you used any statistical calculations in the past, be sure to mention it. If you haven’t yet, familiarize yourself with the following statistical concepts: 

• Mean 
• Standard deviation 
• Variance
• Regression 
• Sample size 
• Descriptive and inferential statistics 

While speaking of these, share information that you can derive from them. What knowledge can you gain about your dataset? 

Read these amazing 12 Data Analytics books to strengthen your knowledge

12 excellent Data Analytics books you should read in 2022

6. What scripting languages are you trained in? 

In order to be a data analyst, you will almost certainly need both SQL and a statistical programming language like R or Python. If you are already proficient in the programming language of your choice at the job interview, that’s fine. If not, you can demonstrate your enthusiasm for learning it.  

In addition to your current languages’ expertise, mention how you are developing your expertise in other languages. If there are any plans for completing a programming language course, highlight its details during the interview. 

To gain some extra points, do not hesitate to mention why and in which situations SQL is used, and why R and python are used. 

7. How can you handle missing values in a dataset? 

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset. 

• Listwise Deletion 

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing. 

• Average Imputation  

Take the average value of the other participants’ responses and fill in the missing value. 

• Regression Substitution 

You can use multiple-regression analyses to estimate a missing value. 

• Multiple Imputations 

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions. 

8. What is Time Series analysis? 

Data analysts are responsible for analyzing data points collected at different intervals. While answering this question you also need to talk about the correlation between the data evident in time-series data. 

Watch this short video to learn in detail:

9. What is the difference between data profiling and data mining?

Profiling data attributes such as data type, frequency, and length, as well as their discrete values and value ranges, can provide valuable information on data attributes. It also assesses source data to understand its structure and quality through data collection and quality checks. 

On the other hand, data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. 

10. Explain the difference between R-Squared and Adjusted R-Squared.

The most vital difference between adjusted R-squared and R-squared is simply that adjusted R-squared considers and tests different independent variables against the model, and R-squared does not. 

An R-squared value is an important statistic for comparing two variables. However, when examining the relationship between a single stock and the rest of the S&P500, it is important to use adjusted R-squared to determine any discrepancies in correlation. 

11. Explain univariate, bivariate, and multivariate analysis.

Bivariate analysis, which is simpler than univariate analysis, is used when the data set only has one variable and does not involve causes or effects.  

Univariate analysis, which is more complicated than bivariate analysis, is used when the data set has two variables and researchers are looking to compare them.  

When the data set has two variables and researchers are investigating similarities between them, multivariate analysis is the right type of statistical approach. 

12. How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?

Before appearing for an interview, make sure you study the company thoroughly and gain enough knowledge about it. It will leave an impression on the employer regarding your interest and enthusiasm to work with them. Also, in your answer you talk about the added value you will bring to the company by improving its business performance. 

13. What do you think are the three best qualities that great data analysts share?

List down some of the most critical qualities of a Data Analyst. This may include problem-solving, research, and attention to detail. Apart from these qualities, do not forget to mention soft skills, which are necessary to communicate with team members and across the department.    

Did we miss any Data Analyst interview questions? 

Share with us in the comments below and help each other to ace the next data analyst job. 

Data Science Dojo is offering RStudio for FREE on Azure Marketplace packaged with a pre-installed running version of R alongside other language backends to simplify Data Science. 

What is data science? 

Data Science is one of the quickest-growing areas of work in the industry. According to Harvard Business Review, it’s regarded as the “sexiest job of the 21st century”. 

Data science joins math and measurements, programming, refined analyses, machine learning and AI to reveal significant knowledge concealed in an association’s dataset. These understandings can be utilized to direct businesses in planning and decision making. The lifecycle of Data Science involves data collection (ingestion), data pre-processing and wrangling, predictive data analysis via machine learning and finally communication of outcomes for future strategies. 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science. 

Challenges faced by developers 

Individuals who were learning or pursuing Data Science and Machine Learning through R found it difficult to code and develop models using only a terminal or command line interface. Developers who wanted to perform extensive high powered ML operations but didn’t have enough computation power to do it locally was also another challenge.  

In these circumstances an interactive environment configured with R can help the users in gaining hands-on experience with machine learning, data analysis and other statistical operations. 

Working with RStudio 

RStudio is an open-source tool that gives you an effortless coding IDE in the cloud with a pre-installed R programming language to start your data mining and analytics work. It is integrated with a set of modules that make code development, scientific computing, and graphical jobs to be more productive and easier. This tool allows developers to perform a variety of technical tasks such as predictive modeling, clustering, multivariate querying, stock market rate, spam filtering, recommendation systems, malware, and anomaly detection, image recognition, and medical diagnosis. 

Key attributes 

• Provides an in-browser coding environment with syntax suggestions, autocomplete code feature and smart indentation 
• Provides the user with an easy-to-use free coding platform accessible at the local web server, powered by Azure machines 
• Apart from the primary built of R, RStudio has support for other famous interpreters as well such as Python, SQL, HTML, CSS, JS, C, Quarto and a few others 
• In-built debugging functionality by toggling breakpoints to detect and eradicate the issues or fix them quickly 
• As the computations are carried on Microsoft’s cloud servers, there is no memory or performance pressure on the company’s storage devices 
• In order to optimize the workload, the RAM and compute power can be scaled accordingly, thanks to Azure services 

What Data Science Dojo has for you 

The RStudio instance packaged by Data Science Dojo provides an in-browser coding environment with a running version of R pre-deployed in it, reducing the burden of installation. With an interactive user-friendly GUI-based application, developers can perform Machine Learning tasks with comfort and flexibility.  

• A browser based RStudio environment up and running with R pre-deployed 
• Convenient accessibility and navigation 
• Ability to work with different language scripts simultaneously 

• Rich graphics and interactive environment 
• Support for git and version control 
• Code consoles to run code interactively, with full support for rich output 
• Integrated R documentation and user help 
• Readily available cheat sheets to get started 

Our instance supports the following backends: 

• R 
• Python 
• HTML 
• CSS 

• JavaScript 
• Quarto 
• C 
• SQL 
• Shell 

• Markdown and Header files 

Conclusion 

RStudio provides customers with an easy-to-use environment to gain hands-on experience with Machine Learning and Data Science. The responsiveness and processing speed are much better than the traditional desktop environment as it uses Microsoft cloud services. It comes with built-in support for git and version control.

Several variants of the R script can be executed in RStudio. It allows users to work on a variety of language backends at the same time with smart observability of variables and values side by side. The documentation and user support are incorporated into the tool to make it easy for developers to code. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free RStudio instance dedicated specifically to Machine Learning and Data Science on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Rstudio for FREE by clicking on “Get it now”.    

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.   

In this blog, we will have a look at the list of free Data Science crash courses to help you succeed in Data Science

With more and more people entering the field, data science and data engineering are surely amongst the topmost emerging areas of work in the 21st century. Higher salaries, perks, benefits, and demand has made it a field of interest for 1000s of people.  

While a good chunk of students is opting for data science in their undergraduate and graduate programs, there are people who are opting for different Data Science Bootcamps to get started with the field.   

However, enrolling instantly in an expensive undergraduate, master’s, or data science Bootcamp might not be the correct choice for one to go with. An individual would want to explore more within the scope of data science before switching fields or making the final call. Hence, below we present a list of free data science crash courses that an individual can go through before choosing their career path.   

If you are completely new to data science or planning to switch your career, our Data Science Practicum Program should be able to help you.   

Likewise, data science is an emerging field. Just a single program or bootcamp cannot help you to excel within the domain of data science, engineering, and analytics. You will have to keep learning and update your skillsets with short courses like Python for Data Science to remain competitive in the job market. This list of free crash courses can help you acquire a number of skills like Power BI, SQL, MLOps, and many others.   

Set of Data Science free crash courses 

So, if you are the one who is already in a data science career or the one who is planning to make a transition, this set of free data science crash courses can help you all out in every possible way. Check them out:  

  1. SQL crash course for beginners:

 This crash course can help beginners with no previous experience in SQL. By the end of this course, you will understand the difference between SQL and NoSQL, what is a database, the differentiation between MySQL, Oracle, PostgreSQL, SQL Server, and SQLite, how to find data in a database by writing a SQL query, and much more. 

2. Python crash course for Excel users:

This course can assist all Excel users with no prior knowledge for Python. In this course, you will understand how Python is different from Excel as an open-source software tool, navigation & execution of codes in Jupyter Notebook, implementing useful packages for data analytics, and translating common Excel concepts such as cells, ranges, and tables to Python equivalents. 

3. Redis crash course for Artificial Intelligence and Machine Learning:

If you have no experience with Redis, then this crash course is for you. This course covers the difference between Redis and SQL databases, key machine learning concepts and use cases Redis enables, data types and structures that can be stored in Redis, Redis as an online feature store, and Redis as a vector database for embeddings & neural search. 

4. MLOps crash course for beginners:

Do you have the basic knowledge of developing machine learning models in a Jupyter notebook setting? Then this course is a perfect fit for you. We will cover what is MLOps and machine learning pipelines, why is MLOps important, how to create and deploy a fully reproducible MLOps pipeline from scratch, and Learn the basics of continuous training, drift detection, alerts, and model deployment. 

  5. Crash course on Naïve Bayes classification:

Need an introduction to Naïve Bayes Classification? Then this short course will take you through the theory and coding examples. With this course, you should be able to acquire a strong understanding of this technique. 

6. Crash course in modern Data Warehousing using Snowflake platform:

With this crash course, you can get started with the new generation of data warehouse i.e. Snowflake. We will discuss Snowflake architecture, its user interface, and the data caching feature of Snowflake. We have also included a lot of instructor-led demos to provide you with a pragmatic experience regarding the Snowflake Platform. 

7. Crash course in Data Visualization:

This crash course is planned for intermediate users with previous experience in python. In this session, introduce chart theory, outline data to visual representations, get access to a Google Colab Notebook that you’re able to code your own interactive charts with, transform data to be ingested by pandas and plotly, and customize your chart with options & properties to make it unique for your use case. 

8. Power BI crash course for beginners:

With this crash course, get started with Microsoft’s Power BI. We will walk you through how to prepare your data, analyze it and build insightful visualizations on the interactive data visualization software Power BI Desktop. By the end of the course, you will know the basics of importing data into Power BI, carrying out exploratory data analysis, cleaning, manipulating, and aggregating data, and building insightful visualizations with Power BI. 

You can also get an in-depth Introduction to Power BI with our live-instructor-led training. Do check it out.   

9. Crash course on designing a dashboard in Tableau:

 This crash course is intended for beginners. In this course, you will know what is Tableau, how to design a basic dashboard in tableau, how to include a bar chart in your dashboard, and how to create a map in tableau.   

10. Crash course in Predictive Analytics:

The uncertainty after Covid-19 has made it difficult for companies to thrive but data and analytics helped companies survive it. Companies need to work proactively with predictive and prescriptive analytics to optimize their operations and compete in a changing world. This crash course will provide an in-depth overview of predictive analytics.  

11. Crash course on Transfer Learning:

In this course, we will discuss the idea of transfer learning, learn how deep learning models communicate with each other, explore the real-world applications of transfer learning, and compare transfer learning with a human’s continuous growth model.  

Need help with your data science career? This Data Science Roadmap can navigate your way.   

12. R and Python- the best of both worlds:

One of the common data science arguments has been what language to learn, R and Python. This argument has led to a language rivalry between R and Python. The purpose of this course is to take through the main defining features of both languages and how they compare different workflows in data science and data types. We will also show what methods are available for combining both in the same workspace and demonstrate this with a case study.  

Want to learn more about free Data Science crash courses? 

Only a top few popular data science crash courses are listed here, however, these might not be sufficient enough to sustain in such a competitive environment. If you are in a search for more data science crash courses, then make sure to go through this list of free data science courses.   

If you are absolutely new to data science, then I can assure you that  our YouTube channel  can navigate your journey, do check it out!  

This blog will learn about “Data Science career growth in 2022”. It is no longer a secret that today’s economy is entirely dependent on analytics and data-driven solutions/decisions. 

Businesses, enterprises, and governments have spent the last few years collecting and analyzing massive volumes of data. If you are interested in the field of Data Science enroll in some Data Science courses offered by reputed Institutions which will be an added advantage during your job hunt. 

Data scientists are currently playing a crucial part in the success or failure of any organization, one can even consider choosing a proper Data science certification program which will help learn practically as well as theoretically. Therefore, it is not a stretch to state that “there is a data scientist behind every huge successful company.”

Overview of Data Science career

Data science is a fascinating, interesting, intriguing, forward-thinking, and lucrative profession. Importantly, unlike other traditional careers, you do not need an established degree or specialized educational background to begin your journey in Data Science.

All you need are the proper abilities, some connected experience, and a curious mind. Considering the need for data scientists in the current market trends indicate that data science course fees are growing.

In this blog, I’ll go over the ins and outs of the data scientist job path, as well as the abilities necessary for data Science. In addition, I’ll guide you on how to choose which data science career is best for you.

Alright!! Let’s dive into the topics.

Table of Contents:

• What is Data Science?

• What does a Data Scientist do?

• Is Data Science right for you?

• Why choose a career in Data Science?

• Job statistics in Data Science career

• Are you ready to become a Data Scientist?

What is Data Science?

Data science is the study of massive amounts of data using current tools and methodologies to discover previously unknown patterns, extract valuable information, and make business choices. 

Data for analysis can come from a wide range of sources and be provided in a variety of ways.

Now that you know what data science is, let’s look at what a Data Scientist will do in 2022.

What does a Data Scientist do?

Data science is a highly interdisciplinary field that works with a broad variety of data and, unlike other analytical fields, focuses on the overall perspective.

In business, the purpose of data science is to give an insight into customers and campaigns, as well as to aid organizations in building effective plans to engage their audiences and sell their products. 

Big data, or enormous amounts of information gathered through different methods such as data mining, necessitates the use of creative thinking on the part of data scientists. So, what exactly does a data scientist do?

Data scientists use forecasting models to evaluate data and information to produce key insights that help enterprises expand their businesses in the right direction. One of the key responsibilities is to analyze large data sets of quantitative and qualitative data. 

This personnel is in charge of developing statistical learning models for data analysis and must be knowledgeable with statistical tools. They must also be knowledgeable enough to create complex prediction models.

Is Data Science right for you?

In my opinion, it is crucial to have an answer to this issue before embarking on your path in data science. Unfortunately, many blogs on the internet indicate that the area of data science is full of demand, great incomes, and respect. 

Nevertheless, the fact is that your journey to data science is not at all easy; it takes continual learning and unlearning of complicated subjects and concepts from different professions, and you must be technically knowledgeable throughout your career.

Learn more about Data Science Roadmap 

In this section, I’ll provide you with some suggestions that will take you to the answer to this question. Fundamentally, anyone can acquire and practice any data science skill if they are truly committed to it.

Simply said, if you want to learn data science, you can do so.

Why choose a career in Data Science?

Data science has been termed the “sexiest job of the twenty-first century.” I’m sure this is a significant role in your decision to pursue a career in data science. Nowadays, any company, large or little, is looking for employees who can interpret and dissect data.

Choosing a profession in data science involves respecting the numerous disciplines on which data science as a subject has been founded, such as statistics, math, and technology, among others. The variety of abilities required to become a data scientist might be considered an advantage.

Now, let me direct your attention to a few key reasons why you should pursue a career in data science;

• High prestige
• Be part of future
  Excellent pay
• Constant challenging work or NO boring work
  Exceptional growth & demand in the market
• Endless career opportunities

Data Science has shown the ability to transform companies and our society. It has become a lucrative job due to a limited supply of trained workers in Data Science and high demand.

Job statistics in Data Science career

If you’re here, I’m presuming you’ve picked or are thinking about choosing a career path. Let me direct your attention to a few more key criteria which might assist you in making your final decision.

• 650% Job growth since 2015 (Via: Linkedin)
• By 2026, 11.5 million additional jobs are expected to be created (source: U.S. Bureau of Labor Statistics)
• A data scientist earns an average annual income of $120,931. (source: Glassdoor)
• In 2020, there are expected to be 2.7 million available positions in data analysis, data science, and related fields (source: IBM).
• By 2020, there will be a 39% increase in employer demand for both data scientists and data engineers (source IBM).
• 59% of employment will be in finance, information technology (IT), insurance, and professional services. This is divided as follows: 
• 19% in banking and insurance, 18% in professional services, and 17% in information technology.
• Bachelor’s degree holders will be able to apply for 61% of data scientist and advanced analytic roles, while 39% will require a master’s or Ph.D.
• Positions in data science and data analysis are available for 5 days longer than the average for all jobs, indicating that there is less competition in these professional sectors and recruiters must work harder to locate competent individuals.
• A possible annual salary of $8,736 more than any other bachelor’s degree position (source: IBM).

 

Pro-Tip: Build up your Data Science career as a licensed Data Scientist

 

The data presented above indicates the development and need for data science specialists across various business areas, geographical regions, and even experience levels. As more businesses implement data-driven solutions, the need for data scientists will continue to rise.

So, relax, you’re on the correct track!

Are you ready to become a Data Scientist?

Data science is the most in-demand career this decade and will continue to be so in the future. With increased awareness of the industry, competition for positions among professionals is at an all-time high. If you follow this approach and do honest self-evaluation, I am confident you will make the best decision for you.

Enroll in Data Science Bootcamp today to begin your Data Science career

Remember that selecting the proper career path is only the beginning of your journey.

Artificial Intelligence and Data Science applications and technologies have penetrated our society so deeply that they are now being used in every industry let alone the eCommerce industry.

In some cases, the usage of AI and Data Science are so seamlessly integrated into the picture that you might not even be noticing them. Without further ado, here are the seven interesting applications of Data Science in the e-commerce industry. 

#1 Recommendation systems 

The first example of Data Science being used in e-commerce is that of recommendation systems. It is quite obvious that these systems largely rely on data to make their recommendations, so Data Science pretty much lies at the foundation of the recommendation systems used in e-commerce. 

Every time a customer makes a purchase (or even simply checks out a product page), their activities are recorded and then used by the system to make personalized recommendations. This way, businesses can sell more products. Such systems pretty much offer exactly the kinds of products specific customers are interested in. 

Likewise, the data collected and analyzed by recommendation systems can be used by your marketers and customer service managers to create special offers for individual customers. You can then send these offers by email, SMS, etc. to directly reach the customers and increase the chances of them making a purchase. 

Learn in detail about data-driven marketing for better ROI

#2 Predictive customer segmentation 

Another popular usage of Data Science in e-commerce is that of predictive customer segmentation. Every e-commerce store has its own target audience, but to work with this audience most effectively and efficiently, you need to segment it and target each segment separately.

In most cases, this segmentation is done manually (or to a large extent manually). However, when you are using predictive customer segmentation, the system helps you segment your target audience. By gathering data and using AI technologies, you can predict customer interest in your offer and identify different groups of customers accordingly. 

Moreover, with the help of predictive customer segmentation, you can also identify the types of users who likely won’t become your customers. You can then exclude them from your target audience and avoid wasting part of your budget in vain. Essentially, you will be making smarter decisions in terms of targeting and segmentation. 

#3 Pricing optimization 

Pricing optimization is one more way of using Data Science in e-commerce. There are so many factors that are being considered when deciding the price of a specific product. From the cost of materials to the quality of the product to its competitive edge when compared to alternatives – all of these need to be taken into account when pricing it. 

For example, you can hire an experienced writer from a writing services reviews site like Best Writers Online who will perform market research and provide you with a report on other similar products. However, you will still need to pay attention to several other factors to determine your product’s price. One of these (often overlooked factors) is the demand for your product. 

Pricing optimization solves this issue for you because the system will consider the demand for your product when setting its price. Similarly, it will consider the supply (i.e. the number of items available) when displaying the price. This way, you can sell your products at a higher price when you know your customers are willing to pay more. 

#4 AI chatbots and assistants 

AI chatbots and virtual assistants have been using AI and Data Science for what feels like ages now even though truly smart chatbots are relatively new. Such chatbots and assistants can help your customers by providing them with a more engaging and enjoyable buying process and improving their overall experience. 

For example, when a customer has questions about the products, they don’t need to send an email and wait for a response or contact the call center and wait for someone to pick up the phone. All they should do is send a text to the chatbot on your website and get an instant answer to their question or concern. 

Of course, AI chatbots are still limited, but they are already quite advanced in what they can do. A lot of chatbots use past customer data to give them suggestions, guide them in their choices, answer their questions, and so on. As this technology continues developing, chatbots will likely become even more common and helpful. 

Read more about how you can improve customer service using data science

#5 Inventory management 

While this is not something you were likely thinking of when you were considering Data Science, inventory management is still an aspect of e-commerce where Data Science is extremely helpful. This is because managing your inventory efficiently takes more than simple manual management, and e-commerce support services can help you with that.

#6 Customer sentiment analysis 

Just like target audience segmentation can be made easier with the help of AI and Data Science, so can be customer sentiment analysis. To put it simply, customer sentiment analysis is about analyzing the conversations online between your current and potential customers to determine what their opinions about and experiences with your brand are. 

Customer sentiment analysis is most commonly performed on social media platforms where conversations are abundant, but you can also perform it on forums and even by analyzing media outlets like services (though in this case, it will be non-customer sentiment analysis). Once you have performed the analysis, you can make smarter decisions about your product design, marketing, customer service, and so much more.

#7 Lifetime value prediction 

Last but not least, Data Science is also being used in e-commerce for predicting the lifetime value of customers. Essentially, the customer lifetime value is the total value of the profit you get from a specific customer over your entire relationship with that customer. 

Of course, making such predictions accurately is extremely difficult, but it isn’t completely impossible. Different systems and algorithms are used to collect and analyze a lot of data about your customers and then make predictions about their lifetime value. Then, you can make further decisions based on these predictions about your customers. 

AI and Data Science applications revolutionizing the eCommerce industry

At the end of the day, the way the e-commerce industry operates will likely continue to change soon. And even the way AI and Data Science applications are being used in e-commerce will eventually evolve. For now, it’s worth using these two technologies to their fullest and reaping the benefits they provide to online store owners. 

In this blog, we will learn the proven successful data science tips to experience exponential growth as a data scientist. There are a few key things that aspiring data scientists should keep in mind if they want to be successful in the field. Let’s learn each tip in detail:

1. Learn competitive skills through competitions

Participating in data science competitions is a great way to test your skills and learn from your peers. These competitions will also give you the chance to work on real-world datasets and solve complex problems.  Learn competitive skills through hackathons and Kaggle competitions. Sometimes Kaggle competitions can feel lonely so go to hackathons and build alongside other people to broaden your ideas and get better feedback.

On Kaggle you can learn from some of the best data scientists in the world and participate in interesting competitions with novel datasets to truly build your knowledge and data science expertise. Observable is another free, community-supported place where you can learn a great deal about all things related to data exploration. 

2. Develop an understanding of business goals

Data scientists have to be well organized, know statistics, and understand how data work connects to a business objective, not just how to code a model. There’s a popular saying that 85% of modeling projects fail and to beat the odds you have to understand how to connect your model with existing business goals and processes. Usually, this comes with experience and the ability to find creative solutions. 

3. Stay calm to tackle the complex data

Expect things to be messy. The data is hardly ever exactly what you need, it can live in many places, and is almost always messier than you thought it would be. It can be hard to estimate how long a project or model will take to build, but I found if you plan and give yourself a one or two-day buffer you’ll find better success with communicating and meeting deadlines. – Ayodele Odubela, Data Scientist, Observable 

4. Don’t neglect the basics

It is important to have a strong foundation in mathematics and statistics. This will give you the ability to understand and work with complex data sets. Additionally, it will also allow you to develop sophisticated models and algorithms.

5. Choosing the right model

Don’t get too caught up in modeling methods. So many data scientists are constantly worried about choosing the right model, when sometimes a model isn’t needed at all. Sometimes a rules-based system is more applicable, and sometimes a dashboard is the better deliverable for a project. 

6. Collaborate with your team

Get more comfortable collaborating with your team. You can optimize your tools so you can cooperate with the least amount of friction. Data scientists often do work for many parts of the business, so reach out to your colleagues to gain better context around the data and how the models you build may be used.  

7. Stay up to date with the latest technology

 The field of data science is constantly evolving, with new tools and techniques being developed all the time. As a result, it is important to keep up-to-date with the latest technology. This will ensure that you are able to use the best tools available to solve complex problems.  

8. Be creative

Data scientists need to be creative in order to find new ways to solve problems. This means thinking outside of the box and coming up with innovative solutions. Additionally, it is also important to be able to communicate your ideas effectively so that others can understand them.   

9. Learn data science through Bootcamps

Bootcamps are another great option for learning data science. These intensive programs will give you the opportunity to learn from experienced data scientists and work on real-world projects.  

10. Attend conferences and workshops

Attending conferences and workshops to network with other data scientists and stay up to date with the latest trends in the field. This is also a great opportunity to learn new skills and techniques.   

11. Develop strong technical skills

 As a data scientist, you will need to have strong technical skills. This includes expertise in programming languages such as Python and R, as well as experience working with databases and big data platforms. Additionally, you should also be familiar with machine learning algorithms and statistical modeling techniques.   

Technical skills are usually obvious and include core skills such as statistics, programming, mathematics, and data visualization. However, the non-technical skills are equally important if not more so. Chief amongst these is communication skills. If you can’t communicate your findings to the right audience, at the right time, in the right way then it doesn’t matter how good your technical analysis is. 

12. Possess business acumen

In addition to technical skills, it is also important to have business acumen. This will allow you to understand the needs of the business and find ways to use data to solve problems. Additionally, being able to effectively communicate with non-technical stakeholders is crucial for success in this role.  

13. Be able to use critical thinking

Data scientists need to be able to think critically in order to identify patterns and insights in data. This includes being able to ask the right questions and identify assumptions that need to be tested. Additionally, being able to think creatively is also important for coming up with innovative solutions.   Boris Jabes, Census

14. Develop a growth mindset

Developing a growth mindset helps you not to avoid failure and to instead view it as an opportunity to grow. Further, it lets you develop a self-belief that you can learn anything.; fully embrace trying new things, ideas, tools, and techniques; see feedback as a gift that will move you forward and finally to be inspired by the success of others. These attitudes will make an enormous difference to your future success as a data scientist. 

 15. Adopt a problem-solving approach 

A data scientist’s job is to solve business problems through data, AI, and ML tools. Data science is problem driven. That means a data scientists need to immerse themselves in learning what the business does and how the business works. Otherwise, the data scientist’s work just because a science experiment in a vacuum. 

16. Improve your interpersonal skills

To get anything done, data scientists need access to data. To secure access to data, they need to learn who to ask and how to ask for data. Downloading a dataset from Kaggle is easy. Figuring out who has the previous five years of company sales data, and how to request that data is an underappreciated skill. 

17. Evaluate technology on a periodic basis

Never put all your eggs in one tool, one platform, or one framework. Expect technology to change and learn how to adapt to new tools. At the same time, don’t just adopt new tools for the sake of having the latest toys. Do your due diligence and evaluate technology vendors on a periodic basis, to learn which tools are likely to become the next standard, and which are likely to remain niche products. – David, Coda Strategy 

18. Prove to be the right fit for the job

 The hiring agents are not only looking for someone having knowledge of data science but someone who is tailor-fitted for the job and one who will produce actual numbers that will be valuable for the company, like sales conversion data, audience engagement data, etc. 

If you look at the US, for example, there’s a need right now for more than 150,000 data scientists. And this need will just grow as we move towards more digital transformation. Aside from the U.S., there’s also a global shortage of data science skills and professionals in Europe and Asia.

It’s also interesting to cite research showing that 94 percent of data scientists and graduates have gotten jobs since 2011. Ninety-four percent is quite encouraging and if you are skilled in data science, you can feel amazingly comfortable that moving in this direction would offer amazing employment potential. This indicates how reliable a career option in data science is now essential moving into the future. 

19. Be curious to learn more

Lastly, an intuitive mind and someone with curiosity is what are essential in a data science job. In enormous data sets, valuable data insights are not always obvious, and a trained data scientist needs to have intuition and understand when to go beneath the surface for insightful information. One of the most important soft skills of a data scientist is the ability to ask questions on a regular basis. You can follow all of the processes of the machine learning project lifecycle if you are bored, but you will not be able to attain the final objective and justify your results.

For me, data science is still growing and evolving which means learning in this discipline never ends. One day you master these new tools and have learned a new skill set, and the following day it is run over by a more complex tool and a thirst for another important skill set. So, a data scientist must be inquisitive and always learn to adapt to these rapid changes. Victoria, MediaPeanut

20. Know the role you want

There are quite a few distinct roles within data science that are all quite different. Before you enter the career, it can be worth knowing which roles you prefer, and which suits your interests. Talk to people in the industry and ask them about what role they do and who they work with, whether you want to be a data architect or visualization expert you need to know the role suits you. Once you know your role you can fine tune what you need to know and learn to have success in the role. 

21. Consider taking a course

Even if you know a lot about data science already, taking a course can help you understand the necessary tools and techniques you need to implement in a specific role. Moreover, many of these courses are work-oriented, as far as they teach you with a career in mind rather than just teaching generic data skills. 

21. Build a portfolio

One of the important things to do is practice data analysis and science. Yet rather than just letting go of each project, try to optimize each project to show off your skills. Find a secure place to keep all your projects as your data science portfolio, once you are accepted for an interview you can demonstrate actionable skills for the prospective employer. – Peter, Lantech 

22. Work on real-world data science projects

In addition to competitions, another wonderful way to get hands-on experience is by working on real-world data science projects. There are many online repositories (such as GitHub) that contain datasets that you can use to practice your data wrangling, modeling, and visualization skills. Working on projects is also a great way to build your portfolio, which will come in handy when you’re ready to start applying for jobs. – Luke, Ever Wallpaper 

23. Obtain the confidence of your peers

As we move about, we assist various teams. We understand that a lot of managers don’t even believe their data. However, they demand brand-new monitors, data science teams, and everything else. But what’s the point? If your data isn’t even reliable. Sherlock Holmes said one of our favorite things:

“Data is the basis for the basic building block of reasoning.”

If such is the case and you have doubts about your home, it will hit you when it drops. Get your superiors to believe in your data and you! 

24. Implement a straightforward project with success first

We understand that everyone wants to create the next algorithm for Google or Facebook. Why not? They are hip, incredibly strong, and generate billions of dollars annually. However, if you want your team to flourish and they are just getting started, start small. Don’t worry; even a basic task can offer your executives incomparable value if done correctly. once you’ve achieved your first victory.

The executives will ask you to assist them with everything. You will then need to put in some effort to ensure that either only the proper projects are all being worked on, or that your projects are constantly inundated with requests. 

25. Explain the importance of your project

Being a salesman is one technique to garner support from executives. How? Explain the need for the project and create it. Considering how new data science is, many executives are unsure of its benefits and applications. Let them see! That is what you do! Show them how they can employ data science to save time, money, and other resources. – William Drow, Starlinkhow 

26. Always give details while requesting assistance

You should always be honest and direct when asking for help, whether it be information, an introduction, or a suggestion. Be direct in your request. People are more willing to help, if you ask them for a modest favor that is not too tough to give. A specific request that is within my sphere of influence makes me far more inclined to say yes when individuals seek my assistance in studying data science.

27. It’s important to follow your passions

For many personal and professional reasons, you may be considering a data science career. If, on the other hand, you’re thinking about the financial and social benefits, you should reconsider. Even if the pay and status are decent, working in this field may become challenging if you don’t enjoy it.

Data science initiatives are like any other form of experiment in that not everything turns out perfectly. You also have responsibilities to the company’s shareholders. It’s possible that you won’t always get to work on the kinds of issues that fascinate or excite you. Instead, you’ll probably have to solve issues that benefit your company. – Adam Crossling, Zenzero 

Do you have any more successful Data Science tips? Share in the comments 

Data science is a challenging but rewarding field, and I hope these tips have helped you get started on your journey. Remember to keep learning and practicing, and you’ll be well on your way to having a successful career in data science! 

Data analysis and data science are very closely related professions in many respects. If one enjoys problem-solving, data-driven decision-making, and critical thinking, both occupations are a good fit. While all alternatives draw on the same core skill set and strive toward comparable goals, there are differences in schooling, talents, daily responsibilities, and compensation ranges. 

The data science certification course offers insight into the tools, technology, and trends driving the data science revolution. We have developed this guide to enable you to go through the abilities and background required to become a data scientist or data analyst, and their corresponding course fee.

Data Scientist vs. Data Analyst

Data analysis and data science are often misunderstood since they rely on the same fundamental skills, not to mention the very same broad educational foundation (e.g., advanced mathematics, and statistical analysis). 

However, the day-to-day responsibilities of each role are vastly different. The difference, in its most basic form, is how they utilize the data they collect.

Role of a Data Analyst

A data analyst examines gathered information, organizes it, and cleans it to make it clear and helpful. Based on the data acquired, they make recommendations and judgments. They are part of a team that converts raw data into knowledge that can assist organizations in making sound choices and investments.

Role of a Data Scientist

A data scientist creates the tools that will be used by an analyst. They write programs, algorithms, and data-gathering technologies. Data scientists are innovative problem solvers who are constantly thinking of new methods to acquire, store, and view data.

Differences in the role of data scientist and data analyst

While both data analysts and data scientists deal with data, the primary distinction is what they do with it. Data analysts evaluate big data sets for insights, generate infographics, and generate visualizations to assist corporations in making better strategic choices. Data scientists, on the other hand, use models, methods, predictive analytics, and specialized analyses to create and build current innovations for data modeling and manufacturing.

Data experts and data scientists typically have comparable academic qualifications. Most have Bachelor’s degrees in economics, statistics, computer programming, or machine intelligence. They have in-depth knowledge of data, marketing, communication, and algorithms. They can work with advanced systems, databases, and Programming environments.

What is data analysis?

Data analysis is the thorough examination of data to uncover trends that can be turned into meaningful information. When formatted and analyzed correctly, previously meaningless data can become a wealth of useful and valuable information that firms in various industries can use.

Data analysis, for example, can tell a technical store what product is most successful at what period and with which population, which can then help employees decide what kind of incentives to run. Data analysis may also assist social media companies in determining when, what, and how they should promote particular users to optimize clicks.

What is data science?

Data science and data analysis both aim to unearth significant insights within piles of complicated or seemingly minor information. Rather than performing the actual analytics, data science frequently aims at developing the models and implementing the techniques that will be used during the process of data analysis.

While data analysis seeks to reveal insights from previous data to influence future actions, data science seeks to anticipate the result of future decisions. Artificial image processing and pattern recognition, which are still in their early stages, are used to create predictions based on large amounts of historical data.

Responsibilities: Data Scientist vs Data Analyst

Professionals in data science and data analysis must be familiar with managing data, information systems, statistics, and data analysis. They must alter and organize data for relevant stakeholders to find it useful and comprehensible. They also assess how effectively firms perform on predefined metrics, uncover trends, and explain the differentiated strategy. While job responsibilities frequently overlap, there are contrasts between data scientists and data analysts, and the methods they utilize to attain these goals.

Role: Data Scientist vs Data Analyst

Data Analyst job description

A data analyst, unsurprisingly, analyzes data. This entails gathering information from various sources and processing it via data manipulation and statistical techniques. These procedures organize and extract insights from data, which are subsequently given to individuals who may act on them.

Become a pro with Data Analytics with these 12 amazing books

Users and decision-makers frequently ask data analysts to discover answers to their inquiries. This entails gathering and comparing pertinent facts and stitching it together to form a larger picture. Knowledgehut looks more closely at a career path in analytics and science, and helps you determine which employment best matches your interests, experience, and ambitions.

Data Scientist job description

A data scientist can have various tasks inside a corporation, among which are very comparable to those of a data analyst, such as gathering, processing, and analyzing data to get meaningful information. 

Whereas a data analyst is likely to have been given particular questions to answer, a data scientist may indeed evaluate the same collection of data with the goal of diverse variables that may lead to a new line of inquiry. In other words, a data scientist must identify both the appropriate questions and the proper answers.

A data scientist will make designs and write algorithms and software to assist them as well as their research analyst team members with the analysis of data. A data scientist is also deeply engaged in the field of artificial intelligence and tries to push the limits and develop new methods to apply this technology in a corporate context.

How can Data Scientists become ethical hackers?

Yes, you heard it right. Data scientists can definitely become ethical hackers. There are several skills data scientists possess that can help them with the smooth transition from data scientists to ethical hackers. The skills are extensive knowledge of programming languages, databases, and operating systems. Data science is an important tool that can prevent hacking.

The necessary skills for a data scientist to become an ethical hacker include mathematical and statistical expertise, and extensive hacking skills. With the rise of cybercrimes, the need for cyber security is increasing. When data scientists become ethical hackers, they can protect an organization’s data and prevent cyber-attacks. 

Skill set required for data analysis and data science

Conclusion 

Each career is a good fit for an individual who enjoys statistics, analytics, and evaluating business decisions. As a data analyst or data scientist, you will make logical sense of large amounts of data, articulate patterns and trends, and participate in great responsibilities in a corporate or government organization.

When picking between a data analytics and a data science profession, evaluate your career aspirations, skills, and how much time you want to devote to higher learning and intensive training. Start your data analyst or data scientist journey with a data science course with nominal data science course fee to learn in-demand skills used in realistic, long-term projects, strengthening your resume and commercial viability.

FAQs

1. Which is better: Data science or data analyst?

Data science is suitable for candidates who want to develop advanced machine learning models and make human tasks easier. On the other hand, the data analyst role is appropriate for candidates who want to begin their career in data analysis. 

2. What is the career path for data analytics and data science?

Most data analysts will begin their careers as junior members of a bigger data analysis team, where they will learn the fundamentals of the work in a hands-on environment and gain valuable experience in data manipulation. At senior level, data analysts become team leaders, in control of project selection and allocation.

A junior data scientist will most likely obtain a post with a focus on data manipulation before delving into the depths of learning algorithms and mapping out forecasts. The procedure of preparing data for analysis varies so much from case to case that it’s far simpler to learn by doing. 

Once conversant with the mechanics of data analysis, data scientists might expand their understanding of artificial intelligence and its applications by designing algorithms and tools. A more experienced data scientist may pursue team lead or management positions, distributing projects and collaborating closely with users and decision-makers. Alternatively, they could use their seniority to tackle the most difficult and valuable problems using their specialist expertise in patterns and machine learning.

3. What is the salary for a data scientist and a data analyst in India?

2 to 4 years (Senior Data Analyst): $98,682 whereas the average data scientist salary is $100,560, according to the U.S. Bureau of Labor Statistics.

References

Difference Between Data Science and Data Analytics – GeeksforGeeks

Business analytics vs data science – Data Science Dojo

Data Analyst vs. Data Scientist: Key Differences Explained | Upwork

Data Analyst vs. Data Scientist: What’s the Difference? | Coursera

Data Analytics vs. Data Science: A Breakdown (northeastern.edu)

Data Analyst vs. Data Scientist: Salary, Skills, & Background (springboard.com)

Data Analyst vs. Data Scientist: Which Should You Pursue? – UT Austin Boot Camps (utexas.edu)

Today’s business landscape is more competitive than ever. The primary goal of every business is to remain relevant and stay afloat in the competition. And one of the ways to do so is to provide excellent customer service. This can be hard as firms strive to meet clients’ ever-changing needs and expectations. 

This is where data science comes in. Studies show that the percentage of data scientists employed in firms has drastically increased. With data science, firms can enhance customer service and improve customer experience. 

Data science can help firms understand their customers. Once a company knows its customers’ needs, it can cater to them better with the right tools. For instance, brands with customers communicating through more than one channel can employ a contact center service to design good customer service experiences.

Amazon customer service is one of the best examples here. The business owns exceptional customer support solutions by integrating modern data science tools. 

Also, data science can help automate specific customer service tasks. This blog post will discuss how data science can improve customer service. Keep reading to discover more.

1. Contact center solutions for smart channel integrations

Every top firm wants to improve the way it responds to client inquiries. So, they create chatbots and contact center solutions that are AI-driven using data science. As you already know, meeting clients’ needs produces quality leads. 

The chatbot now gathers client behavior data to create more real answers to queries. Also, live chat guides clients through the buying steps and offers wise advice on what to buy.

And the cloud contact center software merges the communication channels. This ensures that every client inquiry gets handled in one place. Also, the resolution time is improved for each customer query, thus resulting in quality customer service.

PRO TIP: Join our instructor-led Data Science for Business training to enhance your deep learning skills and gain better data science jobs opportunities

These data-driven, cloud-based contact center solutions can interact with all the channels. For example, they merge voice, email, SMS, Twitter, WhatsApp, and more.

Moreover, the process is automated and seamless, needing little to no maintenance. So, without the organization needing more support workers, clients get quick and easy services across their chosen platforms. 

Also, when a data science tool decodes what clients say and replies the first time, interactions go easier. Data-driven tools can reduce many clients’ pains when they try to “speak” to machines. As a result, most modern firms handle many incoming calls with tools before human agents.

2. Personalizing customer journey with relevance

Many customers are likely to buy when they get a personalized product. So, firms must use data science, and AI must provide relevant suggestions for every client. The suggestions must be tailored to meet their unique demands at every stage of their journey.

Firms can learn data science to gain insights and grasp the products linked to clients’ buying histories. Systems examine clients’ buying habits and conduct a search based on others who bought the same or a related product. This way, data science creates good products and services; thanks to data-backed insights. 

Data science helps you achieve the following:

• Collect and analyze customer data.
• Identify trends and patterns.
• Predict customer behavior. 
• Develop better self-service options. 

With the data gathered from the above operations, AI-powered contact center software can work on vast quantities of customer history records to provide meaningful insights and personalization’s to every customer uniquely. This results in achieving customer satisfaction easily.

3. Differentiating your firm from others

Every firm wants its clients to choose them before their rivals. The deciding factors for any consumer to stay loyal to a firm or brand would be:

• The quality of the goods and services, and
• The client experience.

Firms must focus on what clients enjoy about their goods and services. This becomes easier by using data science to identify those features. By doing this, your business can stay on top of the rivalry and raise client loyalty.

Data science is one of the firms’ most effective tools to know where their services stand. It helps firms know the best periods and places to market their goods and services. So, firms may meet their clients’ demands at the right time.

Also, data will reveal how your services and goods help people live better lives. It also shows how they use these services and products to address issues in their everyday lives. As such, you can find areas for growth and generate concepts for new features. 

4. Simplifying customer accounts and complaints

Every firm needs to make working with client accounts more efficient. The easiest way to achieve this is via data science. It finds needed options and automates tasks related to customer accounts. 

The common data sources are clients’ spending and saving patterns, risk profiles, demography, purchase history, etc. With data accumulated over time, brands can examine patterns to get a holistic view of their clients. Data-driven insights will help brands decide what works and what doesn’t.

For instance, a contact center support agent may want to know a client’s most recent complaints and interactions. This will help the agent be aware of the context of the current complaint. Hence, they can handle the situation better and avoid frustrations. 

Meaningful handling of customer complaints will reduce the level of unsatisfied clients.

5. Tackling issues before they arise

It is now more crucial than ever to fix client disputes. This is because a bad story can go viral on social media. So, keep disputes with clients very low or not at all should they exist.

Data science can improve client services by pointing out issues that no person can see. For instance, several contact center care agents might each get a single call about the same issue and overlook it. 

But a data science-focused system might be able to see the issue across many call logs. Hence it will notify someone to look into it right away. Correcting flaws before they become serious can help you save money. 

With data-driven predictions, firms can proactively spot errors in their strategy before sales and reputation take a hit. As such, they can provide good customer services and save time and money.

Is your organization taking advantage of Data Science to improve customer service?

Today, data is every firm’s most important asset. Data science can improve service quality and raise ROI over the long term. Also, it can add value to your brand.

Data science tracks data from many sources via the buying process. This data now gives insights to help the brand offer the best services to clients. 

With these analytics and the right tools, like hosted contact center software, brands can promote tailor-made client services. They can also provide more relevant ads and advice to enhance both the customer journey and customer satisfaction.

Data is growing at an exponential rate in the world. It is estimated that the world will generate 181 zettabytes of data by 2025. With this increase, we are also seeing an increase in demand for data-driven techniques and strategies.

According to Forbes, 95% of businesses expressed the need to manage unstructured data as a problem for their business. In fact, Business Analytics vs Data Science is one of the hottest debates among data professionals nowadays.

Many people might wonder – what is the difference between Business Analytics and Data Science? Or which one should they choose as a career path? If you are one of those keep reading to know more about both these fields!

First, we need to understand what both these fields are. Let’s take a look. 

What is Business Analytics? 

Business Analytics is the process of deriving insights from business data to inform business decisions. It is the process of collecting data and doing analysis for the business to make better decisions. It provides a lot of insight that can be used to make better business decisions. It helps in optimizing processes and improving productivity.

It also helps in identifying potential risks, opportunities, and threats. Business Analytics is an important part of any organization’s decision-making process. It is a combination of different analytical activities like data exploration, data visualization, data transformation, data modeling, and model validation. All of this is done by using various tools and techniques like R programming, machine learning, artificial intelligence, data mining, etc.

Business analytics is a very diverse field that can be used in every industry. It can be used in areas like marketing, sales, supply chain, operations, finance, technology and many more. 

Now that we have a good understanding of what Business Analytics is, let’s move on to Data Science. 

What is Data Science? 

Data science is the process of discovering new information, knowledge, and insights from data. They apply different machine-learning algorithms to any form of data from numbers to text, images, videos, and audio, to draw various understandings from them. Data science is all about exploring data to identify hidden patterns and make decisions based on them.

It involves implementing the right analytical techniques and tools to transform the data into something meaningful. It is not just about storing data in the database or creating reports about the same. Data scientists collect and clean the data, apply machine learning algorithms, create visualizations, and use data-driven decision-making tools to create an impact on the organization.

Data scientists use tools like programming languages, database management, artificial intelligence, and machine learning to clean, visualize, and explore the data.

Pro tip: Learn more about Data Science for business 

What is the difference between Business Analytics and Data Science? 

Technically, Business analytics is a subset of Data Science. But the two terms are often used interchangeably because of the lack of a clear understanding among people. Let’s discuss the key differences between Business Analytics and Data Science. Business Analytics focuses on creating insights from existing data for making better business decisions.

While Data Science focuses on creating insights from new data by applying the right analytical techniques. Business Analytics is a more established field. It combines several analytical activities like data transformation, modeling, and validation. Data Science is a relatively new field that is evolving every day. Business Analytics is more of a hands-on approach to manage the data whereas Data Science is more focused on the development of the data.

Both the fields also differ a bit in their required skills. Business Analysts mostly use Interpretation, Data visualization, analytical reasoning, statistics, and written communication skills to interpret and communicate their work. Whereas Data Scientists utilize statistical analysis, programming skills, machine learning, calculus and algebra, and data visualization to perform most of their work.

Which should one choose? 

Business analytics is a well-established field, whereas data science is still evolving. If you are inclined towards decisive and logical skills with little or no programming knowledge or computer science skills, you can take up Business Analytics. It is a beginner friendly domain and is easy to catch on to.

But if you are interested in programming and are familiar with machine learning algorithms or even interested in data analysis, you can opt for Data Science. We hope this blog answers your questions about the differences between the two similar and somewhat overlapping fields and helps you make the right data-driven and informed decision for yourself! 

Listed below are the pre-installed python libraries related to Deep learning and the sources of repositories of Deep Learning books provided by this offer:

Python libraries:

• NumPy
• Matplotlib
• Pandas
• Seaborn
• TensorFlow
• Tflearn
• PyTorch
• Keras
• Scikit Learn
• Lasagne
• Leather
• Theano
• D2L
• OpenCV

Repositories:

• GitHub repository of book Deep Learning with Python 2nd Edition, by author François Chollet.
• GitHub repository of book Hands-on Deep Learning Algorithms with Python, by author Sudharsan Ravichandran.
• GitHub repository of book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by author Geron Aurelien.
• GitHub repository of collection on Deep Learning Models, by author Sebastian Raschka.

Conclusion:

Jupyter Hub for Deep Learning using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through this offer, a user can work on a variety of Deep Learning applications self-driving cars, healthcare, fraud detection, language translations, auto-completion of sentences, photo descriptions, image coloring and captioning, object detection, and localization.

This Jupyter Hub for Deep Learning instance is ideal to learn more about Deep Learning without the need to worry about configurations and computing resources.

The heavy resource requirement to deal with large datasets and perform the extensive model training and analysis for these applications is no longer an issue as heavy computations are now performed on Microsoft Azure which increases processing speed.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data.

We are therefore adding a free Jupyter Notebook Environment dedicated specifically to Deep Learning using Python. Install the Jupyter Hub offer now from the Azure Marketplace, your ideal companion in your journey to learn data science!

In this blog, we will introduce you to the highly rated data science statistics books on Amazon. As you read the blog, you will find 5 books for beginners and 5 books for advanced-level experts. We will discuss what’s covered in each book and how it helps you to scale up your data science career. 

Advanced statistics books for data science 

1. Naked Statistics: Stripping the Dread from the Data – By Charles Wheelan 

The book unfolds the underlying impact of statistics on our everyday life. It walks the readers through the power of data behind the news. 

Mr. Wheelan begins the book with the classic Monty Hall problem. It is a famous, seemingly paradoxical problem using Bayes’ theorem in conditional probability. Moving on, the book separates the important ideas from the arcane technical details that can get in the way. The second part of the book interprets the role of descriptive statistics in crafting a meaningful summary of the underlying phenomenon of data. 

Wheelan highlights the Gini Index to show how it represents the income distribution of the nation’s residents and is mostly used to measure inequality. The later part of the book clarifies key concepts such as correlation, inference, and regression analysis explaining how data is being manipulated in order to tackle thorny questions. Wheelan’s concluding chapter is all about the amazing contribution that statistics will continue to make to solving the world’s most pressing problems, rather than a more reflective assessment of its strengths and weaknesses.  

2. Bayesian Methods For Hackers – Probabilistic Programming and Bayesian Inference, By Cameron Davidson-Pilon 

We mostly learn Bayesian inference through intensely complex mathematical analyses that are also supported by artificial examples. This book comprehends Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. 

Davidson-Pilon focused on improving learners’ understanding of the motivations, applications, and challenges in Bayesian statistics and probabilistic programming. Moreover, this book brings a much-needed introduction to Bayesian methods targeted at practitioners. Therefore, you can reap the most benefit from this book if you have a prior sound understanding of statistics. Knowing about prior and posterior probabilities will give an added advantage to the reader in building and training the first Bayesian model.    

Read this blog if you want to learn in detail about statistical distributions

The second part of the book introduces the probabilistic programming library for Python through a series of detailed examples and intuitive explanations, with recent core developments and the popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough. PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy, and Matplotlib. This book is filled with examples, figures, and Python code that make it easy to get started solving actual problems.  

3. Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce  

This book is most beneficial for readers that have some basic understanding of R programming language and statistics.  

The authors penned the important concepts to teach practical statistics in data science and covered data structures, datasets, random sampling, regression, descriptive statistics, probability, statistical experiments, and machine learning. The code is available in both Python and R. If an example code is offered with this book, you may use it in your programs and documentation.  

The book defines the first step in any data science project that is exploring the data or data exploration. Exploratory data analysis is a comparatively new area of statistics. Classical statistics focused almost exclusively on inference, a sometimes-complex set of procedures for drawing conclusions about large populations based on small samples.  

To apply the statistical concepts covered in this book, unstructured raw data must be processed and manipulated into a structured form—as it might emerge from a relational database—or be collected for a study.  

4. Advanced Engineering Mathematics by Erwin Kreyszig 

Advanced Engineering Mathematics is a textbook for advanced engineering and applied mathematics students. The book deals with calculus of vector, tensor and differential equations, partial differential equations, linear elasticity, nonlinear dynamics, chaos theory and applications in engineering. 

Advanced Engineering Mathematics is a textbook that focuses on the practical aspects of mathematics. It is an excellent book for those who are interested in learning about engineering and its role in society. The book is divided into five sections: Differential Equations, Integral Equations, Differential Mathematics, Calculus and Probability Theory. It also provides a basic introduction to linear algebra and matrix theory. This book can be used by students who want to study at the graduate level or for those who want to become engineers or scientists. 

The text provides a self-contained introduction to advanced mathematical concepts and methods in applied mathematics. It covers topics such as integral calculus, partial differentiation, vector calculus and its applications to physics, Hamiltonian systems and their stability analysis, functional analysis, classical mechanics and its applications to engineering problems. 

The book includes a large number of problems at the end of each chapter that helps students develop their understanding of the material covered in the chapter. 

5. Computer Age Statistical Inference by Bradley Efron and Trevor Hastie 

Computer Age Statistical Inference is a book aimed at data scientists who are looking to learn about the theory behind machine learning and statistical inference. The authors have taken a unique approach in this book, as they have not only introduced many different topics, but they have also included a few examples of how these ideas can be applied in practice.

The book starts off with an introduction to statistical inference and then progresses through chapters on linear regression models, logistic regression models, statistical model selection, and variable selection. There are several appendices that provide additional information on topics such as confidence intervals and variable importance. This book is great for anyone looking for an introduction to machine learning or statistics. 

Computer Age Statistical Inference is a book that introduces students to the field of statistical inference in a modern computational setting. It covers topics such as Bayesian inference and nonparametric methods, which are essential for data science. In particular, this book focuses on Bayesian classification methods and their application to real world problems. It discusses how to develop models for continuous and discrete data, how to evaluate model performance, how to choose between parametric and nonparametric methods, how to incorporate prior distributions into your model, and much more. 

5 Beginner level statistics books for data science 

6. How to Lie with Statistics by Darrell Huff 

How to Lie with Statistics is one of the most influential books about statistical inference. It was first published in 1954 and has been translated into many languages. The book describes how to use statistics to make your most important decisions, like whether to buy a house, how much money to give to charity, and what kind of mortgage you should take out. The book is intended for laymen, as it includes illustrations and some mathematical formulas. It’s full of interesting insights into how people can manipulate data to support their own agendas. 

The book is still relevant today because it describes how people use statistics in their daily lives. It gives an understanding of the types of questions that are asked and how they are answered by statistical methods. The book also explains why some results seem more reliable than others. 

The first half of the book discusses methods of making statistical claims (including how to make improper ones) and illustrates these using examples from real life. The second half provides a detailed explanation of the mathematics behind probability theory and statistics. 

A common criticism of the book is that it focuses too much on what statisticians do rather than why they do it. This is true — but that’s part of its appeal! 

 7. Head-first Statistics: A Brain-Friendly Guide Book by Dawn Griffiths  

If you are looking for a book that will help you understand the basics of statistics, then this is the perfect book for you. In this book, you will learn how to use data and make informed decisions based on your findings. You will also learn how to analyze data and draw conclusions from it. 

This book is ideal for those who have already completed a course in statistics or have studied it in college. Griffiths has given an overview of the different types of statistical tests used in everyday life and provides examples of how to use them effectively. 

The book starts off with an explanation of statistics, which includes topics such as sampling, probability, population and sample size, normal distribution and variation, confidence intervals, tests of hypotheses and correlation.  

After this section, the book goes into more advanced topics such as regression analysis, hypothesis testing etc. There are also some chapters on data mining techniques like clustering and classification etc. 

The author has explained each topic in detail for the readers who have little knowledge about statistics so they can follow along easily. The language used throughout this book is very clear and simple which makes it easy to understand even for beginners. 

8. Think Stats By Allen B. Downey 

Think Stats is a great book for students who want to learn more about statistics. The author, Allen Downey, uses simple examples and diagrams to explain the concepts behind each topic. This book is especially helpful for those who are new to mathematics or statistics because it is written in an easy-to-understand manner that even those with a high school degree can understand. 

The book begins with an introduction to basic counting, addition, subtraction, multiplication and division. It then moves on to finding averages and making predictions about what will happen if one number changes. It also covers topics like randomness, sampling techniques, sampling distributions and probability theory. 

The author uses real-world examples throughout the book so that readers can see how these concepts apply in their own lives. He also includes exercises at the end of each chapter so that readers can practice what they’ve learned before moving on to the next section of the book. This makes Think Stats an excellent resource for anyone looking for tips on improving their math skills or just wanting to brush up on some statistical basics! 

9. An Introduction To Statistical Learning With Applications In R By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 

Statistical learning with applications in R is a guide to advanced statistical learning. It introduces modern machine learning techniques and their applications, including sequential decision-making, Gaussian mixture models, boosting, and genetic programming. The book covers methods for supervised and unsupervised learning, as well as neural networks. The book also includes chapters on Bayesian statistics and deep learning. 

It begins with a discussion of correlation and regression analysis, followed by Bayesian inference using Markov chain Monte Carlo methods. The authors then discuss regularization techniques for regression models and introduce boosting algorithms. This section concludes with an overview of neural networks and convolutional neural networks (CNNs). The remainder of the book deals with topics such as kernel methods, support vector machines (SVMs), regression trees (RTs), naive Bayes classifiers, Gaussian processes (GP), gradient ascent methods, and more. 

This statistics book is recommended to researchers willing to learn about statistical machine learning but do not have the necessary expertise in mathematics or programming languages 

10. Statistics in Plain English By Timothy C. Urdan 

Statistics in Plain English is a writing guide for students of statistics. Timothy in his book covered basic concepts with examples and guidance for using statistical techniques in the real world. The book includes a glossary of terms, exercises (with solutions), and web resources. 

The book begins by explaining the difference between descriptive statistics and inferential statistics, which are used to draw conclusions about data. It then covers basic vocabulary such as mean, median, mode, standard deviation, and range. 

In Chapter 2, the author explains how to calculate sample sizes that are large enough to make accurate estimates. In Chapters 3–5 he gives examples of how to use various kinds of data: census data on population density; survey data on attitudes toward various products; weather reports on temperature fluctuations; and sports scores from games played by teams over time periods ranging from minutes to seasons. He also shows how to use these data to estimate the parameters for models that explain behavior in these situations. 

The last 3 chapters define the use of frequency distributions to answer questions about probability distributions such as whether there’s a significant difference between two possible outcomes or whether there’s a trend in a set of numbers over time or space 

Which data science statistics books are you planning to get? 

Build upon your statistical concepts and successfully step into the world of data science. Analyze your knowledge and choose the most suitable book for your career to enhance your data science skills. If you have any more suggestions for statistics books for data science, please share them with us in the comments below.  

A data science portfolio is a great way to show off your skills and talents to potential employers. It can be difficult to stand out in the competitive data science job market, but with a strong data science portfolio, you will have an edge over the competition.

In this post, we will discuss three easy ways to make your data science portfolio stand out. Let’s get started!

What does a data science portfolio include?

A data science portfolio is a collection of your work that demonstrates your skills and abilities in data science. These profiles typically include a mix of scripts of code from data science projects you’ve worked on, data visualizations you made, and write-ups on personal projects you’ve completed.

When applying for data science positions, your potential employer will want to see your data science achievements. Employers use portfolios as a way to evaluate candidates, so it is important that your data science portfolio is well-crafted and showcases your best work.

Why is it important for your data science portfolio to stand out?

With data scientist jobs being highly favored among the Gen Z workforce, the competition for such data science roles is starting to heat up. With many pursuing careers in data science, you’ll need to find ways to stand out among the crowd.

Having an excellent portfolio is important for 3 main reasons:

1. It acts as an extension of your resume
2. It shows expertise in using certain tools
3. It demonstrates your problem-solving approaches

Now let me go through some ways you can make your data science portfolio stronger than most others.

What are 3 easy ways for your data science portfolio to stand out?

Your data science profile should be a reflection of your skills and experience.

With that said, here are three easy ways to make your data science portfolio stand out:

1. Make it visual
2. Include links to popular data science platforms
3. Write blog posts to complement your projects

1. Make it visual

Portfolios are one of the major component’s employers look at before starting the interview process in data science.

Much like your resume, employers are likely to spend less than one minute looking at your data science portfolio. To make an impression, your data science documentation should be heavily focused on visuals.

Some data scientists’ portfolios I love are those that use data visualizations to tell a story. Great data visualization can communicate complex information in an easily digestible format.

Here are some guidelines you can follow:

• Ensure it is visually appealing and easy to navigate
• Include screenshots, graphs, and charts to make your data science portfolio pop
• Explain any insights found in the visualizations

Including data visualizations in your profile will help you stand out from the competition and communicate your skills effectively.

Since data visualizations are a big part of data science work, I’d recommend showing off some charts and dashboards you’ve created. If you’ve used Python in any of your data analytics certificates, do include any line charts, bar graphs, and plots you have created using Plotly/Seaborn in your data science portfolio.

If you’ve created some dashboards in Tableau, do publish them on Tableau Public and link that up to your portfolio site. Or if you’re a Power BI user, do take screenshots/GIFs of the dashboard in use and include them in your portfolio.

Source: My Tableau Public profile

Having visuals to represent your work can make a huge impact and will help you stand out from the rest. This is just one example of how you can make your data science portfolio stand out with visuals.

Let’s move on.

2. Include links to popular data science platforms

A strong data science portfolio should include links to popular data science platforms as well. By having links of popular data science tools in your portfolio, your employers would perceive you as having higher credibility.

This credibility comes from the demonstration of your experience and skills since many data science hiring managers use these platforms often themselves.

Some common platforms to link and display your work include:

• GitHub
• Kaggle
• Stack Overflow
• RPubs
• Tableau Public

If you’re someone who has had several machine learning projects done in Python, do upload them to your personal GitHub account so others can read your code. By linking your GitHub repo links to your portfolio, employers can take a glimpse at your coding quality and proficiency!

One tip I’d recommend is to include a README file for your GitHub profile and customize it to showcase the data science skills and programming languages you’ve learned.

3. Write blog posts to complement your projects

The last way to create an outstanding data scientist profile is to document your portfolio projects in writing – via blog posts!

Having comprehensive and concise blog posts on your data science portfolio shows employers your thought process and how you approached each project. This is a great way to demonstrate your problem-solving skills and how you can solve business problems through analytics for your employer.

For example, if you’ve written some scripts in R for your data mining project and would like to help your employers understand the steps you took, writing an accompanying blog post would be perfect. In this case, I’d recommend trying to document everything in Rmarkdown as I did here.

If you’re interested to publish more data science content to further boost your LinkedIn profile as data scientist, do consider these platforms:

• Medium
• TowardsDataScience
• WordPress (your own blog site)

By writing blog posts, you’re able to provide more context and explanation for each data science project in your portfolio. As a result, employers would be able to appreciate your work even more.

Source: My analytics blog, AnyInstructor.com

Conclusion

By following these three easy tips, you can make your data science portfolio stand out from the competition. I hope these tips will help you in perfecting your portfolio and I wish you all the best in your data science career. 

Thanks for reading!

Author bio

Austin Chia is the Founder of Any Instructor, where he writes about tech, analytics & software. After breaking into data science without a degree, he seeks to help others learn about all things data science and tech. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.