Data Science

Top 10 Data Science podcasts you must listen
Jenny Han
| December 1, 2022

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. 

data science podcast
10 data science podcasts

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!

  

 

Data preprocessing –The foundation of data science solution 
Shehryar Mallick
| November 21, 2022

This blog explores the important steps one should follow in the data preprocessing stage such as eradicating duplicates, fixing structural errors, detecting, and handling outliers, type conversion, dealing with missing values, and data encoding. 

What is data preprocessing 

A common mistake that many novice data scientists make is that they skip through the data wrangling stage and dive right into the model-building phase, which in turn generates a poor-performing machine learning model. 

data preprocessing | Data Science Dojo

data pre-processing
Data pre-processing

This resembles a popular concept in the field of data science called GIGO (Garbage in Garbage Out). This concept means inferior quality data will always yield poor results irrespective of the model and optimization technique used. 

Hence, an ample amount of time needs to be invested in ensuring the quality of the data is up to the standards. In fact, data scientists spend around 80% of their time just on the data pre-processing phase. But fret not, because we will investigate the various steps that you can follow to ensure that your data is preprocessed before stepping ahead in the data science pipeline. 

Let’s look at the steps of data pre-processing to understand it better: 

Removing duplicates: 

You may often encounter repeated entries in your dataset, which is not a good sign because duplicates are an extreme case of non-random sampling, and they tend to make the model biased. Including repeated entries will lead to the model overfitting this subset of points and hence must be removed. 

We will demonstrate this with the help of an example. Let’s say we had a movie data set as follows: 

As we can see, the movie title: “The Dark Knight” is repeated at the 3rd index (fourth entry) in the data frame and needs to be taken care of. 

 Data frame

Using the code below, we can remove the duplicate entries from the dataset based on the “Title” column and only keep the first occurrence of the entry. 

Code

Data frame

 

Just by writing a few lines of code, you ensure your data is free from any duplicate entries. That’s how easy it is! 

Fix structural errors: 

Structural errors in a dataset refer to the entries that either have typos or inconsistent spellings: 

data set

Here you can easily spot the different typos and inconsistencies but what if the dataset was huge? You can check all the unique values and their corresponding occurrence using the following code: 

data frame

Once you identify the entries to be fixed, simply replace the values with the correct version. 

code

Voila! That is how you fix the structural errors. 

 

Detecting and handling outliers: 

Before we dive into detecting and handling outliers let’s discuss what an outlier is.  

“Outlier is any value in a dataset that drastically deviates from the rest of the data points.” 

Let’s say we have a dataset of a streaming service with the ages of users ranging from 18 to 60, but there exists a user whose age is registered as 200. This data point is an example of an outlier and can mess up our machine learning model if not taken care of. 

There are numerous techniques that can be employed to detect and remove outliers in a data set but the ones that I am going to discuss are: 

  1. Box plots 
  1. Z- Score 

Let’s assume the following data set: 

data set

(Note: Dataset available on Kaggle: https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction/discussion/320796 ) 

If we use the describe function of pandas on the Age column, we can analyze the five number summary along with count, mean, and standard deviation of the specified column, then by using the domain specific knowledge like for the above instance we know that significantly large values of age can be a result of human error we can deduce that there are outliers in the dataset as the mean is 38.92 while the max value is 92. 

dataset outliers

As we have got some idea about what outliers are, let’s see some code in action to detect and remove the outliers 

Box Plots: 

Box plots or also called “Box and Whiskers Plot” show the five number summary of the features under consideration and are an effective way of visualizing the outlier. 

outlier data points

As we can see from the above figure, there are number of data points that are outliers. So now we move onto Z-Score, a method through which we are going to set the threshold and remove the outlier entries from our dataset. 

Z- Score: 

A z-score determines the position of a data point in terms of its distance from the mean when measured in standard deviation units. 

We first calculate the Z-score of the feature column: 

z score

The standard normal curve (Z-score) for a set of values represents 99.7% of the data points within the range of –3 and +3 scores, so in practice often the threshold is set to be 3 and anything beyond that is deemed an outlier and hence removed from the dataset if problematic or not a legitimate observation. 

code

Type Conversion: 

Type conversion refers to when certain columns are not of valid data type, for instance in the following data frame three out of four columns are of object data type: 

data frame

Well, we don’t want that right? Because it would produce unexpected results and errors. We are going to convert Title and Director to string data types, and Duration_mins to integer data type. 

 

code data type

  1. Dealing With Missing Values: 

Often, data set contains numerous missing values, which can be a problem. To name a few it can play a role in development of biased estimator, or it can decrease the representativeness of the sample under consideration. 

Which brings us to the question of how to deal with them. 

One thing you could do is simply drop them all. If you notice that index 5 has a few missing values, when the “dropna” command is implemented, it will drop that row from the dataset. 

data set

data frame

 

But what to do when you have a limited number of rows in a dataset? You could use different imputations methods such as the Measures of central tendencies to fill those empty cells. 

The measures include: 

  1. Mean: The mean is the average of a data set. It is “sensitive” to outliers. 
  2. Median: The median is the middle of the set of numbers. It is resistant to outliers 
  3. Mode: The mode is the most common number in a data set. 

It is better to use median instead of mean because of the property of not deviating drastically because of outliers. Allow me to elaborate this with an example 

data set

Notice how there is a documentary by the name “Hunger!” with “Duration_mins” equal to 6000 now observe the difference when I replace the missing value in the duration column with mean and with median. 

data set

data set

 

If you search on the internet for the duration of movie “The Shining” you’ll find out it’s about 146 minutes so, isn’t 152 minutes much closer as compared to 1129 as calculated by mean? 

A few other techniques to fill the missing values that you can explore are forward fill and backward fill. 

Forward will work on the principle that the last valid value of a column is passed forward to the missing cell of the dataset. 

data frame

Notice how 209 propagated forward. 

Let’s observe backward fill too 

data frame

From the above example you can clearly see that the value following the empty cell was propagated backwards to fill in that missing cell. 

The final technique I’m going to show you is called linear interpolation. What we do is we take the mean of the values prior to and following the empty cell and use it to fill the missing value. 

data set

3104.5 is the mean of 209 and 6000. As you can see this technique is too affected by outliers. 

That was a quick run-down on how to handle missing values, moving onto the next section. 

Feature scaling: 

Another core concept of data preprocessing is the feature scaling of your dataset. In simple terms feature scaling refers to the technique where you scale multiple (quantitative) columns of your dataset to a common scale. 

Assume a banking dataset has a column of age which usually ranges from 18 to 60 and a column of balance which can range from 0 to 10000. If you observe, there is an enormous difference between the values each data point can assume, and machine learning model would be affected by the balance column and would assign higher weights to it as it would consider the higher magnitude of balance to carry more importance as compared to age which has relatively lower magnitude. 

To rectify this, we use the following two methods: 

  1. Normalization 
  1. Standardization 

Normalization fits the data between the range of [0,1] but sometimes [-1,1] too. It is affected by outliers in a dataset and is useful when you do not know about the distribution of dataset. 

Standardization on the other hand is not bound to be within a certain range, it’s quite resistant to outliers and useful when the distribution is normal or Gaussian. 

Normalization:  

code

Standardization:  

code

Data encoding 

The last step of the data preprocessing stage is the data encoding. It is where you encode the categorical features (columns) of your dataset into numeric values. 

There are many encoding techniques available but I’m just going to show you the implementation of one hot encoding (Pro-tip: You should use this when the order of the data does not matter).  

For instance in the following example Gender column is nominal data meaning that the identification of your gender does not take precedence over other gender, to further clarify the concept let’s assume for the sake of argument we had a dataset of examination results of some high school class with a column of rank, the rank here is an example of ordinal data as it would follow certain order and higher-ranking students would take precedence over lower ranked ones. 

code

data set

 

If you notice in the above example, Gender column could assume one of the two options that were either male or female, what one hot encoder did was create the same number of columns as the number of options available, then for the row that had the associated possible value encoded it with one (why one? Well because one is the binary representation of true) otherwise zero (you guessed, zero represents false) 

If you do wish to explore other techniques here is an excellent resource for this purpose:

Blog: Types of categorical data encoding

 

Conclusion: 

It might have been a lot to take in, but you have now explored the crucial concept of data science that is data preprocessing.; Moreover, you are now equipped with the steps to curate your dataset in such a way that it would yield satisfactory results. 

The journey to becoming a data scientist can seem daunting, but with the right mentorship you can learn it seamlessly and take on real world problems in no time, to embark on the journey of becoming a data scientist, enroll yourself in the Data Science bootcamp and grow your career. 

External resource: 

Tableau: What is Data Cleaning? 

Data Science vs AI – What 2023 demand for?
Lafond Wanda
| November 10, 2022

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 data in big quantities using a variety of technologies and techniques to detect patterns, make conclusions about the data, and help in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries. 

Artificial Intelligence (AI) vs Data science vs Machine learning
Artificial Intelligence vs Data science vs Machine learning – Image source

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 or 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. 
  • PredictionAnalyze 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 and 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. 

Free data science course to master your learning
Ayesha Saleem
| November 9, 2022

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.  

data science free course
List of data science free courses

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. 

2023 data jobs you MUST know about to ace your career
Ayesha Saleem
| November 2, 2022

In this blog, we are going to discuss the leading data jobs in demand for the coming year along with their average annual earnings.

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How Data Science benefits digital marketing 
Eiswan Ali Kazmi
| October 27, 2022

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. 

SEO - digital marketing
SEO – Data Science benefits for digital marketing

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. 

Website analytics
Website analytics – Digital marketing

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. 

Top 12 free Data Science crash courses
Ali Haider Shalwani
| October 20, 2022

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.   

Data Science crash courses
                                                                              Free Data Science crash courses – Data Science Dojo

 

 

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!  

 

CTA - Data Science courses

Data science career growth in 2022
Dhannush Subramani
| October 19, 2022

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 science career growth
7 questions everyone asks about data science career growth

 

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.

data science career
Data scientist working on data – Data Science Dojo

 

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.

 

7 interesting Data Science applications in the eCommerce industry 
Lafond Wanda
| October 17, 2022

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.  

(more…)

Data-driven marketing for better ROI    
Muhammad Bilal Awan
| October 13, 2022

You may have heard the buzzword data-driven marketing. In this blog we will discuss what is required to really be data-driven in marketing initiatives that help us achieve a better return on marketing investment?  

We will talk about:

  • What data-driven marketing is
  • How to create and implement marketing strategies based on data
  • The challenges marketers face in a data-driven environment, and
  • Tools to help deliver a higher return on investment (ROI). 

Today’s marketing is a lot different from decades-old gut-driven marketing. Now we have the most reliable sources of information available in real-time. With tools like HubSpot, Salesforce, Zoho, and many others you can track every interaction of your lead from the initial stages of the buyer’s journey to post-purchase.  

This helps devise long-term strategies that are based on actual data rather than gut feelings. With the information available you can optimize current campaigns to achieve the best ROI. 

However, to achieve the desired results you need to first understand what’s important in terms of your customer interaction and what metrics matter the most, so you don’t get lost in an enormous amount of data. 

Become skilled in data-driven marketing with Data Science Dojo’s certificate programs. 

data driven marketing
Four key benefits of data-driven marketing

What is data-driven marketing and why is it important? 

Data-driven marketing is an approach where marketers build strategies based on the analysis of big data. This includes using tools to drive insights from raw data to turn it into actionable insights based on customer interaction. Marketers identify trends and base their long-term strategy on insights driven by data analysis. 

So, why should we invest in a data-driven marketing approach? Besides the obvious answer of clarity and efficiency of marketing processes, a data-driven approach helps identify a target audience, creates a seamless experience for customers, helps choose the best communication channels, and creates personalization. 

  • Help define the target audience 

One of the most important aspects of any marketing campaign is to have a laser-sharp definition of the target audience. With the help of insights from different marketing touchpoints collected and analyzed, we can define the target group for our products/services who are most likely to benefit. 

Once we have a clear definition of the target audience and created personas everything else falls in place to create a synergized marketing campaign to reach the right audience at the right time with the right communication. 

 

  • Create a seamless customer experience 

Successful marketing is not only about lavish advertising campaigns and promotion but to create and delivering superior value to customers. This in turn helps build the brand, create an army of loyalists, and drive positive word of mouth. 

Data-driven marketing helps analyze trends in the market and understand user interaction with marketing touchpoints to create workflows and processes. This helps improve service delivery and exceed customer expectations. Data-driven organizations are able to deliver a smooth experience over the customer lifecycle minimizing hurdles and delivering great value. 

For example, at Data Science Dojo, customer success is the most important driver of overall organizational health. We have created automated processes that are personalized to each individual customer and lead. This helps the marketing team nurture potential leads into high-value customers and delivers a seamless experience to help build a community of brand advocates. So far, we have trained more than 10,000+ professionals from around the globe and built a community of data enthusiasts. 

 

  • Optimizing communication channels 

With the help of data coming in from different marketing touchpoints, marketers can identify the best possible communication channel for each product category, target audience, and customer segment.  

Marketers no longer need to rely on one size fits all solutions but instead create personalized communication based on user behavior, demographics, psychographics, and other factors being captured through CRM and ERP systems.  

For example, Starbucks, one of the leading coffee brands in the world used a data-driven approach to create an end-to-end marketing experience for their customers by using a mobile application as their primary communication channel. Marketing identified an opportunity to grow the customer base by offering a loyalty reward program based on user interaction with the brand touchpoints. According to Risenews case study today Starbucks is running the most successful loyalty program with over 24 million active users.  

 

  • Creating personalization 

Customers are skeptical about generic marketing messaging that pushes them to buy. A recent study by Marketo shows that consumers are fed up with repeated generic messages being blasted at them. 63% of the respondents said they are highly annoyed while 78% said they will only engage with the brand offers if it relates to their previous interaction. 

Personalization is not just an add-on to good messaging, but it has become a necessity to survive in a cluttered communication environment where users are receiving thousands of brand messages from multiple platforms.  

With data analysis of user queries, interaction, and common questions, and defining the entire sales process marketers can create personalized communication based on user segment and need.  

The sales team plays a vital role in delivering personalized messaging but with a data-driven approach, we can minimize redundant tasks and focus on delivering personalized messaging based on user interaction. 

 

How a data-driven approach helps improve ROI 

Now that we have a clear understanding of how significant data is to any marketing effort, we can talk about its impact on overall business goals and specific measurable marketing objectives. 

Research suggests benefits of a data-driven marketing approach are huge. We get greater customer loyalty, improved lead generation, and increased satisfaction. 

According to ZoomInfo about 78% of organizations that follow data-driven approach verifies increase in lead conversion and customer acquisition. 

Another study by Forbes reveals that 66% of marketing leaders believe that data lead to increased customer acquisition. 

To improve your return on marketing investment it is important to give the right attribution to each marketing activity. This helps us identify the best drivers of growth and invest more time and money in that particular marketing activity or channel. 

Learn more about marketing attribution in this short tutorial 

 

Challenges to a data-driven approach 

According to Campaign Monitor, 81% of marketing professionals consider the implementation of data-driven strategies extremely complicated. 

So, what are some of the challenges to achieving a data-driven marketing overhaul? 

 

1. Gathering data: many data-driven marketers are overwhelmed by the idea of collecting data without any automation. In most cases, the abundance of data makes it difficult to narrow it down to the most useful data for analysis.  

Solution: There are multiple CRM and ERP systems available at very competitive costs that deliver precise information on your customer that can be used to create a better user experience. 

 

2. Pulling data: Manually pulling and updating data regularly is a laborious task  

Solution: Creating a marketing dashboard that helps keep track of real-time data. Less time should be spent collecting data and more time analyzing and making decisions. There are multiple tools available to connect and visualize your data. Platforms like Hotjar, Adverity, and Improvado help collect, organize, and seamlessly visualize data so you as a marketer can focus on planning and making data-driven decisions. 

 

 3. Data silos: Challenge of data silos created at each departmental, functional, or team level which is not accessible to the entire team makes the marketing job difficult. A recent survey shows only 8% of the companies have a centralized data repository.  

Solution: To overcome the challenge of data silos there needs to be an organization-wide effort to modernize and embrace change. This is going to include setting up common standards, changing culture, and embracing new marketing analytics platforms. 

 

A marketing strategy based on data 

Building a data-driven strategy or just strategy itself is a vast topic with much research being conducted on the best way to do so. The only thing you need to keep in mind is your current business environment, this includes internal, external, and current organizational requirements.  

Here is a quick walkthrough of steps involved in a data-driven strategy 

Step 1: Strategy 

The first step is to identify long-term strategy, this means figuring out your long-term goals, specific and measurable objectives, and detailing down to tactics.  

Once you have a clear understanding of your overall business strategy as well as a marketing strategy you can focus on data that is relevant to your goals. 

Step 2: Identify key areas 

Data is scattered across organizations coming from all directions and multiple customer touchpoints. It is important to identify key areas of interest that align with your overall business strategy and objectives. Once we have key focus areas, we can continue investing more in building capabilities in that area. 

Step 3: Data targeting  

After identifying focus areas, it is time to target datasets that will answer all the burning questions related to your business and marketing objectives.  

This means identifying already available information and channels and figuring out the most valuable information. At this point of your data-driven strategy, the goal is to streamline data collection and presentation methods so that marketing can only focus on key areas of business value and not waste time on non-essential data reports.  

Step 4: Collecting and analyzing data 

In this step, you need to identify key stakeholders in data collection and analysis. There may be teams or individuals at each data collection and distribution point based on the size of the organization. The idea is to keep the process of collection and dissemination of data seamless, integrated, and in real-time.  

This may require an organization to implement integrated ERP systems or CRM systems to connect data coming in from various sources based on our identified key focus areas and show relevant information to each team. 

Step 5: Turning insights into action 

The final step of a data-driven strategy is to turn insights gained from data analysis into actionable items. ROI will depend on how useful your insights are and how successfully they were implemented to achieve marketing objectives. At this step, you need to have a clear understanding and a game plan for the implementation phase, actions that will improve business and create value for customers. 

Learn how to visualize data to tell a story 

 

Become a truly data-driven marketer 

Becoming data-driven is a continuous process, if you think you are data-driven now, technology and competitive environment will change in the next 6 months making your current data-driven strategy obsolete. As a marketer you need to constantly improve and update so does your marketing strategy. With this guide, you can get started with becoming more data-driven and less gut-driven to make sound marketing decisions based on real-time data. This will not only help achieve measurable marketing objectives but improve return on marketing investment and improve overall business value. 

 

 

References 

https://www.forbes.com/forbesinsights/data_driven_and_digitally_savvy/ 

https://www.marketo.com/articles/personalization-definition/ 

https://www.campaignmonitor.com/resources/infographics/the-eye-opening-truth-about-data-driven-marketing/ 

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-big-reset-data-driven-marketing-in-the-next-normal 

https://datasciencedojo.com/blog/data-science-toolkit/ 

 

27 successful data science tips to learn before 2023
Guest Post
| October 12, 2022

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 Analyst vs Data Scientist – Career path in 2023
Hazel Jones
| October 11, 2022

Introduction

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.

data analyst vs data scientist
Key differences between a data analyst and a data scientist

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

data analyst vs data scientist job role
Job roles of data analyst and data scientist

 

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.

 

Data Analyst Data Scientist
Data analyzers are expert interpreters. They use massive amounts of information to comprehend what is going on in the industry and how corporate actions affect how customers perceive and engage with the company. They are motivated by the need to understand people’s perspectives and behaviors through data analysis.  Data scientists build the framework for capturing data and better understanding the narrative it conveys about the industry, enterprise, and decisions taken. They are designers that can create a system that can handle the volume of data required while also making it valuable for understanding patterns and advising the management team. 
Everyday data analyst tasks may involve examining both historical and current patterns and trends. Data scientists are typically responsible for the scrubbing and information retrieval.
Create operational and financial reports. Data collection statistical analysis.
Forecasting in tools such as Excel. Deep learning framework training and development.
Designing infographics. Creating architecture that can manage large amounts of data.
Data interpretation and clear communication. Developing automation that streamlines data gathering and processing chores daily.
Data screening is accomplished by analyzing documents and fixing data corruption.  Presenting insights to the executive team and assisting with data-driven decision making
Using predictive modeling to discover and impact future trends.

 

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

 

Data analysis Data science
Qualification: A Bachelor’s or Master’s degree in a related discipline, such as mathematics or statistics. Qualification: An advanced degree, such as a master’s degree or possibly a Ph.D., in a relevant discipline, such as statistics, computer science, or mathematics.
Language skills: To understand data analysis, such as Python, SQL, CQL, and R. Language skills: Demonstrate proficiency in data-related programming languages such as SQL, R, Java, and Python.
Soft skills: 

  • Written and verbal communication skills
  • Exceptional analytical skills 
  • Organizational skills
  • The ability to manage many products at the same time may be required.
Soft skills: 

  • Substantial experience with data mining 
  • Specialized statistical activities and tools
  • Generating generalized linear model regressions, statistical tests, designing data structures, and text mining. 
Technical skills: 

  • Expertise in data gathering and some of the most recent data analytics technology.
Technical skills: 

  • Experience with data sources and web services
  • Web services such as Spark, Hadoop, DigitalOcean and S3 
  • Trained to use information obtained from third-party suppliers such as Google Analytic, Crimson Hexagon, Coremetrics, Site Catalyst
Microsoft Office proficiency: 

Proficient in Microsoft Office applications, notably Excel, to properly explain their findings and translate them for others to grasp. 

Knowledge of statistical techniques and technology: Data processing technologies such as MySQL and Gurobi, as well as technological advances such as machine learning models, deep learning, artificial intelligence, artificial neural networks, and decision tree learning, will play a significant role.

 

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. 

 

  1. 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.

 

  1. 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)

5 tips to enhance customer service using data science
Dan Martin
| October 10, 2022

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.

5 tips to Improve customer service
5 tips to enhance customer service using data science

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.

Improve customer experience
Decrease in bounce rate with improved customer experience

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.

Organization's customer service department
Customer service representatives / Customer service team in an organization – Data Science Dojo

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.

Upskill with data science - CTA

 

6 key steps of the data science life cycle explained  
Ayesha Saleem
| October 1, 2022

To perform a systematic study of data, we use data science life cycle to perform testable methods to make predictions.  

Before you apply science to data, you must be aware of the important steps. A data science life cycle will help you get a clear understanding of the end-to-end actions of a data scientist. It provides us with a framework to fulfill business requirements using data science tools and technologies. 

Follow these steps to accomplish your data science life cycle

In this blog, we will study the iterative steps used to develop, deliver, and maintain any data science product.  

data science life cycle
6 steps of data science life cycle – Data Science Dojo

1. Problem identification 

Let us say you are going to work on a project in the healthcare industry. Your team has identified that there is a problem of patient data management in this industry, and this is affecting the quality of healthcare services provided to patients. 

Before you start your data science project, you need to identify the problem and its effects on patients. You can do this by conducting research on various sources, including: 

  • Online forums 
  • Social media (Twitter and Facebook) 
  • Company websites 

 

Understanding the aim of analysis to extract data is mandatory. It sets the direction to use data science for the specific task. For instance, you need to know if the customer is willing to minimize savings loss or prefers to predict the rate of a commodity. 

To be precise, in this step we answer the following questions: 

  • Clearly state the problem to be solved 
  • Reason to solve the problem 
  • State the potential value of the project to motivate everyone 
  • Identify the stakeholders and risks associated with the project 
  • Perform high-level research with your data science team 
  • Determine and communicate the project plan 

Pro-tip: Enroll yourself in Data Science boot camp and become a Data Scientist today

2. Data investigation 

To complete this step, you need to dive into the enterprise’s data collection methods and data repositories. It Is important to gather all the relevant and required data to maintain the quality of research. Data scientists contact the enterprise group to apprehend the available data.  

In this step, we: 

  • Describe the data 
  • Define its structure 
  • Figure out relevance of data and 
  • Assess the type of data record 

 

Here you need to intently explore the data to find any available information related to the problem. Because the historical data present in the archive contributes to better understanding of business.  

In any business, data collection is a continual process. At various steps, information on key stakeholders is recorded in various software systems. To study that data to successfully conduct a data science project it is important to understand the process followed from product development to deployment and delivery. 

Also, data scientists also use many statistical methods to extract critical data and derive meaningful insights from it.  

3. Pre-processing of data 

Organizing the scattered data of any business is a pre-requisite to data exploration. First, we gather data from multiple sources in various formats, then convert the data into a unified format for smooth data processing.  

All the data processing happens in a data warehouse, in which data scientists together extract, transform and load (ETL) the data. Once the data is collected, and the ETL process is completed, data science operations are carried out.  

It is important to realize the role of the ETL process in every data science project. Also, a data architect contributed widely at the stage of pre-processing data as they decide the structure of the data warehouse and perform the steps of ETL operations.  

The actions to be performed at this stage of a data science project are: 

  • Selection of the applicable data 
  • Data integration by means of merging the data sets  
  • Data cleaning and filtration of relevant information  
  • Treating the lacking values through either eliminating them or imputing them 
  • Treating inaccurate data through eliminating them 
  • Additionally, test for outliers the use of box plots and cope with them 

 

This step also emphasizes the importance of elements essential to constructing new data. Often, we are mistaken to start data research for a project from scratch. However, data pre-processing suggests us to construct new data by refining the existing information and eliminating undesirable columns and features.

Data preparation is the most time-consuming but the most essential step in the complete existence cycle. Your model will be as accurate as your data. 

4. Exploratory data analysis  

Applause to us! We now have the data ready to work on. At this stage make sure that you have the data in your hands in the required format. Data analysis is carried out by using various statistical tools. Support of data engineer is crucial in data analysis. They perform the following steps to conduct the Exploratory Data Analysis: 

  • Examine the data by formulating the various statistical functions  
  • Identify dependent and independent variables or features 
  • Analyze key features of data to work on 
  • Define the spread of data 

 

Moreover, for thorough data analysis, various plots are utilized to visualize the data for better understanding for everyone. Data scientists explore the distribution of data inside distinctive variables of a character graphically by the usage of bar graphs. Not only this but relations between distinct aspects are captured via graphical representations like scatter plots and warmth maps. 

The instruments like Tableau, PowerBI and so on are well known for performing Exploratory Data Analysis and Visualization. Information on Data Science with Python and R is significant for performing EDA on an information. 

5. Data modeling 

Data modeling refers to the process of converting raw data into a form that can be transverse into other applications as well. Mostly, this step is performed in spreadsheets, but data scientists also prefer to use statistical tools and databases for data modeling.  

The following elements are required for data modeling: 

 

Data dictionary: A list of all the properties describing your data that you want to maintain in your system, for example, spreadsheet, database, or statistical software. 

 

Entity relationship diagram: This diagram shows the relationship between entities in your data model. It shows how each element is related to the other, as well as any constraints to that relationship  

 

Data model: A set of classes representing each piece of information in your system, along with its attributes and relationships with other objects in the system.  

 

The Machine Learning engineer applies different algorithms to the information and delivers the result. While demonstrating the information numerous multiple times, the models are first tried on fake information like genuine information. 

6. Model evaluation/ Monitoring 

Before we learn what, model evaluation is all about, we need to know that model evaluation can be done parallel to the other stages of the data science life cycle. It helps you to know at every step if your model is working as intended or if you need to make any changes. Alongside, eradicate any error at an early stage to avoid getting false predictions at the end of the project. 

In case you fail to acquire a quality result in the evaluation, we must reiterate the complete modeling procedure until the preferred stage of metrics is achieved.  

As we assess the model towards the end of project, there might be changes in the information, however, the result will change contingent upon changes in information. Thus, while assessing the model the following two stages are significant 

 

  • Data drift analysis: 

Data drift refers to the changes in the input information. Data drift analysis is a feature in data science that highlights the changes in the information along with the circumstance. Examination of this change is called Data Drift Analysis. The accuracy of the model relies heavily on how well it handles this information float. The progressions in information are significantly a direct result of progress in factual properties of information. 

 

  •  Model drift analysis 

We use drift machine learning techniques to find the information. Additionally, more complex techniques like Adaptive Windowing, Page Hinkley, and so on are accessible for use. Demonstrating Drift Analysis is significant as we realize change is quick. Steady advancement likewise can be utilized where the model is presented to added information gradually. 

Start your data science project today

Data science life cycle is a collection of individual steps that need to be taken to prepare for and execute a data science project. The steps include identifying the project goals, gathering relevant data, analyzing it using appropriate tools and techniques, and presenting results in a meaningful way. It is not an effortless process, but with some planning and preparation you can make it much easier on yourself. 

5 data science competitions to uplift your analytical skills
Arham Noman
| September 28, 2022

For a 21st-century professional, having proven analytical skills is increasingly important. Companies all over the world have started to push data scientists to participate in leading data science competitions. Businesses now emphasize all their employees gain analytical skillsets, regardless of their department.

One of the best ways to prove that you have a strong grip on analytics/ data science skills is to take part in reputable competitions that test these to show your employer that you have the required skill set.  

There are many events these days for data science professionals, so it can get overwhelming trying to figure out which ones are worth your time. If you are not sure where to begin, or which ones to take part in, here are a few notable ones to help you get started. 

 

Data Science Competitions
Participating in data science competitions – Data Science Dojo

 

1. Kaggle 

Kaggle is the most popular platform for practicing data science skills. It hosts multiple popular datasets, and regularly has competitions where anyone can participate to build the best machine learning models with data set and compete against others working on the same dataset.

You can learn more about Kaggle competitions on our blog here: Insightful Kaggle competitions and data science portfolios | Data Science Dojo 

 

 Read more about Kaggle Competitions in this blog by

 

2. IBM Call for Code 

The IBM call for code competition asks for contributions across several different areas in order to solve real world challenges. There are currently 4 areas in 2022 where you can get involved and build solutions:

The Global Challenge, open source projects, racial justice, and deployments. You can find out more on the call for code page here: Call for Code | Tech for Good | IBM Developer  

 

3. Machine Hack: 

Machine hack is a community that hosts competitions or hackathons for data science and AI enthusiasts. There are a wide variety of challenges available from the data science pipeline, from machine learning to data visualization. You can also win cash prizes for some of the challenges. 

 

4. DataCamp: 

DataCamp has weekly competitions on their website. Each event has a cash prize associated with it as well. You can submit your solutions, and vote on the best solutions from other participants as well 

 

5. DrivenData: 

DrivenData provides a platform for data scientists who want to make a social impact with their work. The challenges on the platform focus on solving social issues through data science.

These challenges include things like predicting public health risks at restaurants, identifying endangered species in images, and matching students to schools where they are likely to succeed. The winning code gets a prize, and gets published under an open-source license for others to benefit as well 

 

Are you excited to participate in data science competitions?

All of the above-mentioned data science events allow you to gain hands-on learning of data science skills. It offers a platform to the learner for improving problem-solving skills and proving their abilities in the competitive market.

Not only does participating in these competitions helps you stand out, but these also let you brainstorm innovative ideas for the future.

Top 7 data science tools to master before 2023
Austin Chia
| September 22, 2022

Data science tools are becoming increasingly popular as the demand for data scientists increases. However, with so many different tools, knowing which ones to learn can be challenging

In this blog post, we will discuss the top 7 data science tools that you must learn. These tools will help you analyze and understand data better, which is essential for any data scientist.

So, without further ado, let’s get started!

List of 7 data science tools 

There are many tools a data scientist must learn, but these are the top 7:

Top 7 data science tools - Data Science Dojo
Top 7 data science tools you must learn
  • Python
  • R Programming
  • SQL
  • Java
  • Apache Spark
  • Tensorflow
  • Git

And now, let me share about each of them in greater detail!

1. Python

Python is a popular programming language that is widely used in data science. It is easy to learn and has many libraries that can be used to analyze data, machine learning, and deep learning.

It has many features that make it attractive for data science: An intuitive syntax, rich libraries, and an active community.

Python is also one of the most popular languages on GitHub, a platform where developers share their code.

Therefore, if you want to learn data science, you must learn Python!

There are several ways you can learn Python:

  • Take an online course: There are many online courses that you can take to learn Python. I recommend taking several introductory courses to familiarize yourself with the basic concepts.

 

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

 

  • Read a book: You can also pick up a guidebook to learning data science. They’re usually highly condensed with all the information you need to get started with Python programming.
  • Join a Boot Camp: Boot camps are intense, immersive programs that will teach you Python in a short amount of time.

 

Whichever way you learn Python, make sure you make an effort to master the language. It will be one of the essential tools for your data science career.

2. R Programming

R is another popular programming language that is highly used among statisticians and data scientists. They typically use R for statistical analysis, data visualization, and machine learning.

R has many features that make it attractive for data science:

  • A wide range of packages
  • An active community
  • Great tools for data visualization (ggplot2)

These features make it perfect for scientific research!

In my experience with using R as a healthcare data analyst and data scientist, I enjoyed using packages like ggplot2 and tidyverse to work on healthcare and biological data too!

If you’re going to learn data science with a strong focus on statistics, then you need to learn R.

To learn R, consider working on a data mining project or taking a certificate in data analytics.

 

3. SQL

SQL (Structured Query Language) is a database query language used to store, manipulate, and retrieve data from data sources. It is an essential tool for data scientists because it allows them to work with databases.

SQL has many features that make it attractive for data science: it is easy to learn, can be used to query large databases, and is widely used in industry.

If you want to learn data science involving big data sets, then you need to learn SQL. SQL is also commonly used among data analysts if that’s a career you’re also considering exploring.

There are several ways you can learn SQL:

  • Take an online course: There are plenty of SQL courses online. I’d pick one or two of them to start with
  • Work on a simple SQL project
  • Watch YouTube tutorials
  • Do SQL coding questions

 

4. Java

Java is another programming language to learn as a data scientist. Java can be used for data processing, analysis, and NLP (Natural Language Processing).

Java has many features that make it attractive for data science: it is easy to learn, can be used to develop scalable applications, and has a wide range of frameworks commonly used in data science. Some popular frameworks include Hadoop and Kafka.

There are several ways you can learn Java:

 

5. Apache Spark

Apache Spark is a powerful big data processing tool that is used for data analysis, machine learning, and streaming. It is an open-source project that was originally developed at UC Berkeley’s AMPLab.

Apache Spark is known for its uses in large-scale data analytics, where data scientists can run machine learning on single-node clusters and machines.

Spark has many features made for data science:

  • It can process large datasets quickly
  • It supports multiple programming languages
  • It has high scalability
  • It has a wide range of libraries

If you want to learn big data science, then Apache Spark is a must-learn. Consider taking an online course or watching a webinar on big data to get started.

 

6. Tensorflow

TensorFlow is a powerful toolkit for machine learning developed by Google. It allows you to build and train complex models quickly.

Some ways TensorFlow is useful for data science:

  • Provides a platform for data automation
  • Model monitoring
  • Model training

Many data scientists use TensorFlow with Python to develop machine learning models. TensorFlow helps them to build complex models quickly and easily.

If you’re interested to learn TensorFlow, do consider these ways:

  • Read the official documentation
  • Complete online courses
  • Attend a TensorFlow meetup

However, to learn and practice your Tensorflow skills, you’ll need to pick up decent deep learning hardware to support the running of your algorithms.

 

7. Git

Git is a version control system used to track code changes. It is an essential tool for data scientists because it allows them to work on projects collaboratively and keep track of their work.

Git is useful in data science for:

If you’re planning to enter data science, Git is a must-know tool! Since you’ll be coding a lot in Python/R/Java, you’ll want to master Git to work with your team well in a collaborative coding environment.

Git is also an essential part of using GitHub, a code repository platform used by many data scientists.

To learn Git, I’d recommend just watching simple tutorials on YouTube.

Final thoughts

And these are the top seven data science tools that you must learn!

The most important thing is to get started and keep upskilling yourself! There is no one-size-fits-all solution in data science, so find the tools that work best for you and your team and start learning.

I hope this blog post has been helpful in your journey to becoming a data scientist. Happy learning!

 

50 funniest Data Science jokes you should not miss
Ayesha Saleem
| September 21, 2022

Learning Data Science with fun is the missing ingredient for diligent data scientists. This blog post collected the best data science jokes including statistics, artificial intelligence, and machine learning.

 

Data Science jokes

For Data Scientists

1. There are two kinds of data scientists. 1.) Those who can extrapolate from incomplete data.

2. Data science is 80% preparing data, and 20% complaining about preparing data.

3. There are 10 kinds of people in this world. Those who understand binary and those who don’t.

4. What’s the difference between an introverted data analyst & an extroverted one? Answer: the extrovert stares at YOUR shoes.

5. Why did the chicken cross the road? The answer is trivial and is left as an exercise for the reader.

6. The data science motto: If at first, you don’t succeed; call it version 1.0

7. What do you get when you cross a pirate with a data scientist? Answer: Someone who specializes in Rrrr

8. A SQL query walks into a bar, walks up to two tables, and asks, “Can I join you?”

9. Why should you take a data scientist with you into the jungle? Answer: They can take care of Python problems

10. Old data analysts never die – they just get broken down by age

11. I don’t know any programming, but I still use Excel in my field!

12. Data is like people – interrogate it hard enough and it will tell you whatever you want to hear.

13. Don’t get it? We can help. Check out our in-person data science Bootcamp or online data science certificate program.

For Statisticians

14. Statistics may be dull, but it has its moments.

15. You are so mean that your standard deviation is zero.

16. How did the random variable get into the club? By showing a fake i.d.

17. Did you hear the one about the statistician? Probably….

18. Three statisticians went out hunting and came across a large deer. The first statistician fired, but missed, by a meter to the left. The second statistician fired, but also missed, by a meter to the right. The third statistician didn’t fire, but shouted in triumph, “On average we got it!”

19. Two random variables were talking in a bar. They thought they were being discreet, but I heard their chatter continuously.

20. Statisticians love whoever they spend the most time with; that’s their statistically significant other.

21. Old age is statistically good for you – very few people die past the age of 100.

22. Statistics prove offspring’s an inherited trait. If your parents didn’t have kids, odds are you won’t either.

For Artificial Intelligence experts

23. Artificial intelligence is no match for natural stupidity

24. Do neural networks dream of strictly convex sheep?

25. What did one support vector say to another support-vector? Answer: I feel so marginalized

26. AI blogs are like philosophy majors. They’re always trying to explain “deep learning.”

27. How many support vectors does it take to change a light bulb? Answer: Very few, but they must be careful not to shatter* it.

28. Parent: If all your friends jumped off a bridge, would you follow them? Machine Learning Algorithm: yes.

29. They call me Dirichlet because all my potential is latent and awaiting allocation

30. Batch algorithms: YOLO (You Only Learn Once), Online algorithms: Keep Updates and Carry On

31. “This new display can recognize speech” “What?” “This nudist play can wreck a nice beach”

32. Why did the naive Bayesian suddenly feel patriotic when he heard fireworks? Answer: He assumed independence

33. Why did the programmer quit their job? Answer: Because they didn’t get arrays.

34. What do you call a program that identifies spa treatments? Facial recognition!

35. Human: What do we want!?

  • Computer: Natural language processing!
  • Human: When do we want it!?
  • Computer: When do we want what?

36. A statistician’s wife had twins. He was delighted. He rang the minister who was also delighted. “Bring them to church on Sunday and we’ll baptize them,” said the minister. “No,” replied the statistician. “Baptize one. We’ll keep the other as a control.”

For Machine Learning professionals

37. I have a joke about a data miner, but you probably won’t dig it. @KDnuggets:

38. I have a joke about deep learning, but I can’t explain it. Shamail Saeed, @hacklavya

39. I have a joke about deep learning, but it is shallow. Mehmet Suzen, @memosisland

40. I have a machine learning joke, but it is not performing as well on a new audience. @dbredesen

41. I have a new joke about Bayesian inference, but you’d probably like the prior more. @pauljmey

42. I have a joke about Markov models, but it’s hidden somewhere. @AmeyKUMAR1

43. I have a statistics joke, but it’s not significant. @micheleveldsman

44. I have a geography joke, but I don’t know where it is. @olimould

45. I have an object-oriented programming joke. But it has no class. Ayin Vala

46. I have a quantum mechanics joke. It’s both funny and not funny at the same time. Philip Welch

47. I have a good Bayesian laugh that came from a prior joke. Nikhil Kumar Mishra

48. I have a java joke, but it is too verbose! Avneesh Sharma

49. I have a regression joke, but it sounds quite mean. Gang Su

50. I have a machine learning joke, but I cannot explain it. Andriy Burkov

Did we miss your favorite Data Science joke?

Share your favorite data science jokes with us in the comments below. Let’s laugh together!

 

 

Business Analytics vs Data Science – Pick and choose your career path
Afsah Ur Rehman
| September 19, 2022

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!

Business analytics - Data science
                                                                                                      Team working on Business Analytics

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! 

 

Hands-on deep learning using Python in Cloud
Ali Mohsin
| August 3, 2022

Data Science Dojo has launched  Jupyter Hub for Deep Learning using Python offering to the Azure Marketplace with pre-installed Deep Learning libraries and pre-cloned GitHub repositories of famous Deep Learning books and collections which enables the learner to run the example codes provided.

What is Deep Learning?

Deep learning is a subfield of machine learning and artificial intelligence (AI) that mimics how people gain specific types of knowledge. Deep learning algorithms are incredibly complex and the structure of these algorithms, where each neuron is connected to the other and transmits information, is quite similar to that of the nervous system. Also, there are different types of neural networks to address specific problems or datasets, for example, Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs). While in the field of Data Science, which also encompasses statistics and predictive modeling, contains deep learning as a key component. This procedure is made quicker and easier by deep learning, which is highly helpful for data scientists who are tasked with gathering, processing, and interpreting vast amounts of data.

Deep Learning using Python

Python, a high-level programming language that was created in 1991 and has seen a rise in popularity, is compatible with deep learning, which has contributed to its development. While several languages, including C++, Java, and LISP, can be used with deep learning, Python continues to be the preferred option for millions of developers worldwide.

Additionally, data is the essential component in all deep learning algorithms and applications, both as training data and as input. Python is a great tool to employ for managing large volumes of data for training your deep learning system, inputting input, or even making sense of its output because it is primarily used for data management, processing, and forecasting.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

deep learning

Challenges for individuals

Individuals who want to upgrade their path from Machine Learning to Deep Learning and want to start with it usually lack the resources to gain hands-on experience with Deep Learning. A beginner in Deep Learning also faces compatibility issues while installing libraries.

What we provide

Jupyter Hub for Deep Learning using Python solves all the challenges by providing you an effortless coding environment in the cloud with pre-installed Deep Learning python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Moreover, this offer provides the user with repositories of famous authors and books on Deep Learning which contain chapter-wise notebooks with some exercises which serve as a learning resource for a user in gaining hands-on experience with Deep Learning.

The heavy computations required for Deep Learning applications are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

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!

Try Now!

Building a data science portfolio: 3 easy ways to stand out
Guest blog
| September 2, 2022

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!

Data science portfolio infographic

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.

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