Python has become a popular programming language in the data science community due to its simplicity, flexibility, and wide range of libraries and tools. With its powerful data manipulation and analysis capabilities, Python has emerged as the language of choice for data scientists, machine learning engineers, and analysts.
By learning Python, you can effectively clean and manipulate data, create visualizations, and build machine-learning models. It also has a strong community with a wealth of online resources and support, making it easier for beginners to learn and get started.
This blog will navigate your path via a detailed roadmap along with a few useful resources that can help you get started with it.
Step 1. Learn the basics of Python programming
Before you start with data science, it’s essential to have a solid understanding of its programming concepts. Learn about basic syntax, data types, control structures, functions, and modules.
Step 2. Familiarize yourself with essential data science libraries
Once you have a good grasp of Python programming, start with essential data science libraries like NumPy, Pandas, and Matplotlib. These libraries will help you with data manipulation, data analysis, and visualization.
Apply your knowledge by working on real-world data science projects. This will help you gain practical experience and also build your portfolio. Here are some Python project ideas you must try out!
Step 6. Keep up with the latest trends and developments
Data science is a rapidly evolving field, and it’s essential to stay up to date with the latest developments. Join data science communities, read blogs, attend conferences and workshops, and continue learning.
Our weekly and monthly data science newsletters can help you stay updated with the top trends in the industry and useful data science & AI resources, you can subscribe here.
Learn how to read and index time series data using Pandas package and how to build, predict or forecast an ARIMA time series model using Python’s statsmodels package with this free course.
Explore this list of top packages and learn how to use them with this short blog.
By following these steps, you’ll have a solid foundation in Python programming and data science concepts, making it easier for you to pursue a career in data science or related fields.
For an in-depth introduction do check out our Python for Data Science training, it can help you learn the programming language for data analysis, analytics, machine learning, and data engineering.
In conclusion, Python has become the go-to programming language in the data science community due to its simplicity, flexibility, and extensive range of libraries and tools.
To become a proficient data scientist, one must start by learning the basics of Python programming, familiarizing themselves with essential data science libraries, understanding statistics and mathematics, diving into machine learning, working on projects, and keeping up with the latest trends and developments.
With the numerous online resources and support available, learning Python and data science concepts has become easier for beginners. By following these steps and utilizing the additional resources, one can have a solid foundation in Python programming and data science concepts, making it easier to pursue a career in data science or related fields.
In this blog post, the author introduces the new blog series about the titular three main disciplines or knowledge domains of software development, project management, and data science. Amidst the mercurial evolving global digital economy, how can job-seekers harness the lucrative value of those fields–esp. data science, vis-a-vis improving their employability?
To help us launch this blog series, I will gladly divulge two embarrassing truths. These are:
Despite my marked love of LinkedIn, and despite my decent / above-average levels of general knowledge, I cannot keep up with the ever-changing statistics or news reports vis-a-vis whether–at any given time, the global economy is favorable to job-seekers, or to employers, or is at equilibrium for all parties–i.e., governments, employers, and workers.
Despite having rightfully earned those fancy three letters after my name, as well as a post-graduate certificate from the U. New Mexico & DS-Dojo, I (used to think I) hate math, or I (used to think I) cannot learn math; not even if my life depended on it!
Following my undergraduate years of college algebra and basic discrete math–and despite my hatred of mathematics since 2nd grade (chief culprit: multiplication tables!), I had fallen in love (head-over-heels indeed!) with the interdisciplinary field of research methods. And sure, I had lucked out in my Masters (of Arts in Communication Studies) program, as I only had to take the qualitative methods course.
But our instructor couldn’t really teach us about interpretive methods, ethnography, and qualitative interviewing etc., without at least “touching” on quantitative interviewing/surveys, quantitative data-analysis–e.g. via word counts, content-analysis, etc.
Fast-forward; year: 2012. Place: Drexel University–in Philadelphia, for my Ph.D. program (in Communication, Culture, and Media). This time, I had to face the dreaded mathematics/statistics monster. And I did, but grudgingly.
Let’s just get this over with, I naively thought; after all, besides passing this pesky required pre-qualifying exam course, who needs stats?!
About software development:
Fast-forward again; year: 2020. Place(s): Union, NJ and Wenzhou, Zhejiang Province; Hays, KS; and Philadelphia all over again. Five years after earning the Ph.D., I had to reckon with an unfair job loss, and chaotic seesaw-moves between China and the USA, and Philadelphia and Kansas, etc.
But like many other folks who try this route, I soon came face-to-face with that oh-so-debilitative monster: self-doubt! No way, I thought. I’m NOT cut out to be a software-engineer! I thus dropped out of the bootcamp I had enrolled in and continued my search for a suitable “plan-B” career.
About project management:
Eventually (around mid/late-2021), I discovered the interdisciplinary field of project management. Simply defined (e.g. by Te Wu, 2020; link), project management is
“A time-limited, purpose-driven, and often unique endeavor to create an outcome, service, product, or deliverable.”
One can also break down the constituent conceptual parts of the field (e.g. as defined by Belinda Goodrich, 2021; link) as:
Project life cycle,
Professional responsibility / ethics.
Ah…yes! I had found my sweet spot, indeed. or, so I thought.
Eventually, I experienced a series of events that can be termed “slow-motion epiphanies” and hard truths. Among many, below are three prime examples.
Hard Truth 1: The quantifiability of life:
For instance, among other “random” models: one can generally presume–with about 95% certainty (ahem!)–that most of the phenomena we experience in life can be categorized under three broad classes:
Phenomena we can easily describe and order, using names (nominal variables);
Phenomena we can easily group or measure in discrete and evenly-spaced amounts (ordinal variables);
And phenomena that we can measure more accurately, and which: i)–is characterized by trait number two above, and ii)–has a true 0 (e.g., Wrench et Al; link).
Hard Truth 2: The probabilistic essence of life:
Regardless of our spiritual beliefs, or whether or not we hate math/science, etc., we can safely presume that the universe we live in is more or less a result of probabilistic processes (e.g., Feynman, 2013).
Hard truth 3: What was that? “Show you the money (!),” you demanded? Sure! But first, show me your quantitative literacy, and critical-thinking skills!
And finally, related to both the above realizations: while it is true indeed that there are no guarantees in life, we can nonetheless safely presume that professionals can improve their marketability by demonstrating their critical-thinking-, as well as quantitative literacy skills.
Bottomline; The value of data science:
Overall, the above three hard truths are prototypical examples of the underlying rationale(s) for this blog series. Each week, DS-Dojo will present our readers with some “food for thought” vis-a-vis how to harness the priceless value of data science and various other software-development and project-management skills / (sub-)topics.
No, dear reader; please do not be fooled by that “OmG, AI is replacing us (!)” fallacy. Regardless of how “awesome” all these new fancy AI tools are, the human touch is indispensable!
In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. We will also be sharing code snippets so you can try out different analysis techniques yourself. So, without any further ado let’s dive right in.
What is Exploratory Data Analysis (EDA)?
“The greatest value of a picture is when it forces us to notice what we never expected to see.”John Tukey, American Mathematician
A core skill to possess for someone who aims to pursue data science, data analysis or affiliated fields as a career is exploratory data analysis (EDA). To put it simply, the goal of EDA is to discover underlying patterns, structures, and trends in the datasets and drive meaningful insights from them that would help in driving important business decisions.
The data analysis process enables analysts to gain insights into the data that can inform further analysis, modeling, and hypothesis testing.
EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization. These activities together help in generating hypotheses, identifying potential data cleaning issues, and informing the choice of models or modeling techniques for further analysis. The results of EDA can be used to improve the quality of the data, to gain a deeper understanding of the data, and to make informed decisions about which techniques or models to use for the next steps in the data analysis process.
Often it is assumed that EDA is to be performed only at the start of the data analysis process, however the reality is in contrast to this popular misconception, as stated EDA is an iterative process and can be revisited numerous times throughout the analysis life cycle if need may arise.
In this blog while highlighting the importance and different renowned techniques of EDA we will also show you examples with code so you can try them out yourselves and better comprehend what this interesting skill is all about.
Want to see some exciting visuals that we can create from this dataset? DSD got you covered! Visit the link
Importance of EDA:
One of the key advantages of EDA is that it allows you to develop a deeper understanding of your data before you begin modelling or building more formal, inferential models. This can help you identify
Understand the relationships between variables, and
Identify potential issues with the data, such as missing values, outliers, or other problems that might affect the accuracy of your models.
Another advantage of EDA is that it helps in generating new insights which may incur associated hypotheses, those hypotheses then can be tested and explored to gain a better understanding of the dataset.
Finally, EDA helps you uncover hidden patterns in a dataset that were not comprehensible to the naked eye, these patterns often lead to interesting factors that one couldn’t even think would affect the target variable.
The technique you employ for EDA is intertwined with the task at hand, many times you would not require implementing all the techniques, on the other hand there would be times that you’ll need accumulation of the techniques to gain valuable insights. To familiarize you with a few we have listed some of the popular techniques that would help you in EDA.
One of the most popular and effective ways to explore data is through visualization. Some popular types of visualizations include histograms, pie charts, scatter plots, box plots and much more. These can help you understand the distribution of your data, identify patterns, and detect outliers.
Below are a few examples on how you can use visualization aspect of EDA to your advantage:
The histogram is a kind of visualization that shows the frequencies of each category in a dataset.
The above graph shows us the number of responses belonging to different age groups and they have been partitioned based on how many came to the appointment and how many did not show up.
A pie chart is a circular image, it is usually used for a single feature to indicate how the data of that feature are distributed, commonly represented in percentages.
The pie chart shows the distribution that 20.2% of the total data comprises of individuals who did not show up for the appointment while 79.8% of individuals did show up.
Box plot is also an important kind of visualization that is used to check how the data is distributed, it shows the five number summary of the dataset, which is quite useful in many aspects such as checking if the data is skewed, or detecting the outliers etc.
The box plot shows the distribution of the Age column, segregated on the basis of individuals who showed and did not show up for the appointments.
Descriptive statistics are a set of tools for summarizing data in a way that is easy to understand. Some common descriptive statistics include mean, median, mode, standard deviation, and quartiles. These can provide a quick overview of the data and can help identify the central tendency and spread of the data.
Grouping and aggregating:
One way to explore a dataset is by grouping the data by one or more variables, and then aggregating the data by calculating summary statistics. This can be useful for identifying patterns and trends in the data.
Exploratory data analysis also includes cleaning data, it may be necessary to handle missing values, outliers, or other data issues before proceeding with further analysis.
As you can see, fortunately this dataset did not have any missing value.
Correlation analysis is a technique for understanding the relationship between two or more variables. You can use correlation analysis to determine the degree of association between variables, and whether the relationship is positive or negative.
The heatmap indicates to what extent different features are correlated to each other, with 1 being highly correlated and 0 being no correlation at all.
Types of EDA:
There are a few different types of exploratory data analysis (EDA) that are commonly used, depending on the nature of the data and the goals of the analysis. Here are a few examples:
Univariate EDA, short for univariate exploratory data analysis, examines the properties of a single variable by techniques such as histograms, statistics of central tendency and dispersion, and outliers detection. This approach helps understand the basic features of the variable and uncover patterns or trends in the data.
The pie chart indicates what percentage of individuals from the total data are identified as alcoholic.
This type of EDA is used to analyse the relationship between two variables. It includes techniques such as creating scatter plots and calculating correlation coefficients and can help you understand how two variables are related to each other.
The bar chart shows what percentage of individuals are alcoholic or not and whether they showed up for the appointment or not.
This type of EDA is used to analyze the relationships between three or more variables. It can include techniques such as creating multivariate plots, running factor analysis, or using dimensionality reduction techniques such as PCA to identify patterns and structure in the data.
The above visualization is distplot of kind, bar, it shows what percentage of individuals belong to one of the possible four combinations diabetes and hypertension, moreover they are segregated on the basis of gender and whether they showed up for appointment or not.
This type of EDA is used to understand patterns and trends in data that are collected over time, such as stock prices or weather patterns. It may include techniques such as line plots, decomposition, and forecasting.
This kind of chart helps us gain insight of the time when most appointments were scheduled to happen, as you can see around 80k appointments were made for the month of May.
This type of EDA deals with data that have a geographic component, such as data from GPS or satellite imagery. It can include techniques such as creating choropleth maps, density maps, and heat maps to visualize patterns and relationships in the data.
In the above map, the size of the bubble indicates the number of appointments booked in a particular neighborhood while the hue indicates the percentage of individuals who did not show up for the appointment.
Popular libraries for EDA:
Following is a list of popular libraries that python has to offer which you can use for Exploratory Data Analysis.
Pandas: This library offers efficient, adaptable, and clear data structures meant to simplify handling “relational” or “labelled” data. It is a useful tool for manipulating and organizing data.
NumPy: This library provides functionality for handling large, multi-dimensional arrays and matrices of numerical data. It also offers a comprehensive set of high-level mathematical operations that can be applied to these arrays. It is a dependency for various other libraries, including Pandas, and is considered a foundational package for scientific computing using Python.
Matplotlib: Matplotlib is a Python library used for creating plots and visualizations, utilizing NumPy. It offers an object-oriented interface for integrating plots into applications using various GUI toolkits such as Tkinter, wxPython, Qt, and GTK. It has a diverse range of options for creating static, animated, and interactive plots.
Seaborn: This library is built on top of Matplotlib and provides a high-level interface for drawing statistical graphics. It’s designed to make it easy to create beautiful and informative visualizations, with a focus on making it easy to understand complex datasets.
Plotly: This library is a data visualization tool that creates interactive, web-based plots. It works well with the pandas library and it’s easy to create interactive plots with zoom, hover, and other features.
Altair: is a declarative statistical visualization library for Python. It allows you to quickly and easily create statistical graphics in a simple, human-readable format.
In conclusion, Exploratory Data Analysis (EDA) is a crucial skill for data scientists and analysts, which includes data cleaning, manipulation, and visualization to discover underlying patterns and trends in the data. It helps in generating new insights, identifying potential issues and informing the choice of models or techniques for further analysis.
It is an iterative process that can be revisited throughout the data analysis life cycle. Overall, EDA is an important skill that can inform important business decisions and generate valuable insights from data.
Bellevue, Washington (January 11, 2023) – The following statement was released today by Data Science Dojo, through its Marketing Manager Nathan Piccini, in response to questions about future in-person bootcamps:
Data science myths are one of the main obstacles preventing newcomers from joining the field. In this blog, we bust some of the biggest myths shrouding the field.
The US Bureau of Labor Statistics predicts that data science jobs will grow up to 36% by 2031. There’s a clear market need for the field and its popularity only increases by the day. Despite the overwhelming interest data science has generated, there are many myths preventing new entry into the field.
Data science myths, at their heart, follow misconceptions about the field at large. So, let’s dive into unveiling these myths.
1. All data roles are identical
It’s a common data science myth that all data roles are the same. So, let’s distinguish between some common data roles – data engineer, data scientist, and data analyst. A data engineer focuses on implementing infrastructure for data acquisition and data transformation to ensure data availability to other roles.
A data analyst, however, uses data to report any observed trends and patterns to report. Using both the data and the analysis provided by a data engineer and a data analyst, a data scientist works on predictive modeling, distinguishing signals from noise, and deciphering causation from correlation.
Finally, these are not the only data roles. Other specialized roles such as data architects and business analysts also exist in the field. Hence, a variety of roles exist under the umbrella of data science, catering to a variety of individual skill sets and market needs.
2. Graduate studies are essential
Another myth preventing entry into the data science field is that you need a master’s or Ph.D. degree. This is also completely untrue.
In busting the last myth, we saw how data science is a diverse field welcoming various backgrounds and skill sets. As such, a Ph.D. or master’s degree is only valuable for specific data science roles. For instance, higher education is useful in pursuing research in data science.
However, if you’re interested in working on real-life complex data problems using data analytics methods such as deep learning, only knowledge of those methods is necessary. And so, rather than a master’s or Ph.D. degree, acquiring specific valuable skills can come in handier in kickstarting your data science career.
3. Data scientists will be replaced by artificial intelligence
As artificial intelligence advances, a common misconception arises that AI will replace all human intelligent labor. This misconception has also found its way into data science forming one of the most popular myths that AI will replace data scientists.
This is far from the truth because. Today’s AI systems, even the most advanced ones, require human guidance to work. Moreover, the results produced by them are only useful when analyzed and interpreted in the context of real-world phenomena, which requires human input.
So, even as data science methods head towards automation, it’s data scientists who shape the research questions, devise the analytic procedures to be followed, and lastly, interpret the results.
Being a data scientist does not translate into being an expert programmer! Programming tasks are only one component of the data science field, and these too, vary from one data science subfield to another.
For example, a business analyst would require a strong understanding of business, and familiarity with visualization tools, while minimal coding knowledge would suffice. At the same time, a machine learning engineer would require extensive knowledge of Python.
In conclusion, the extent of programming knowledge depends on where you want to work across the broad spectrum of the data science field.
5. Learning a tool is enough to become a data scientist
Knowing a particular programming language, or a data visualization tool is not all you need to become a data scientist. While familiarity with tools and programming languages certainly helps, this is not the foundation of what makes a data scientist.
So, what makes a good data science profile? That, really, is a combination of various skills, both technical and non-technical. On the technical end, there are mathematical concepts, algorithms, data structures, etc. While on the non-technical end there are business skills and understanding of various stakeholders in a particular situation.
To conclude, a tool can be an excellent way to implement data science skills. However, it isn’t what will teach you the foundations or the problem-solving aspect of data science.
6. Data scientists only work on predictive modeling
Another myth! Very few people would know that data scientists spend nearly 80% of their time on data cleaning and transforming before working on data modeling. In fact, bad data is the major cause of productivity levels not being up to par in data science companies. This requires significant focus on producing good quality data in the first place.
This is especially true when data scientists work on problems involving big data. These problems involve multiple steps of which data cleaning and transformations are key. Similarly, data from multiple sources and raw data can contain junk that needs to be carefully removed so that the model runs smoothly.
So, unless we find a quick-fix solution to data cleaning and transformation, it’s a total myth that data scientists only work on predictive modeling.
7. Transitioning to data science is impossible
Data science is a diverse and versatile field welcoming a multitude of background skill sets. While technical knowledge of algorithms, probability, calculus, and machine learning can be great, non-technical knowledge such as business skills or social sciences can also be useful for a data science career.
At its heart, data science involves complex problem solving involving multiple stakeholders. For a data-driven company, a data scientist from a purely technical background could be valuable but so could one from a business background who can better interpret results or shape research questions.
And so, it’s a total myth that transitioning to data science from another field is impossible.
In our current era, the terms “AI”, “ML”, “analytics”–etc., are indeed THE “buzzwords” du jour. And yes, these interdisciplinary subjects/topics are **very** important, given our ever-increasing computing capabilities, big-data systems, etc.
The problem, however, is that **very few** folks know how to teach these concepts! But to be fair, teaching in general–even for the easiest subjects–is hard. In any case, **this**–the ability to effectively teach the concepts of data-science–is the genius of DS-Dojo. Raja and his team make these concepts considerably easy to grasp and practice, giving students both a “big picture-,” as well as a minutiae-level understanding of many of the necessary details.
Still, a leery prospective student might wonder if the program is worth their time, effort, and financial resources. In the sections below, I attempt to address this concern, elaborating on some of the unique value propositions of DS-Dojo’s pedagogical methods.
The More Things Change…
Data Science enthusiasts today might not realize it, but many of the techniques–in their basic or other forms–have been around for decades. Thus, before diving into the details of data-science processes, students are reminded that long before the terms “big data,” AI/ML and others became popularized, various industries had all utilized techniques similar to many of today’s data-science models. These include (among others): insurance, search-engines, online shopping portals, and social networks.
This exposure helps Data-Science Dojo students consider the numerous creative ways of gathering and using big-data from various sources–i.e. directly from human activities or information, or from digital footprints or byproducts of our use of online technologies.
The big picture of the Data Science Bootcamp
As for the main curriculum contents, first, DS-Dojo students learn the basics of data exploration, processing/cleaning, and engineering. Students are also taught how to tell stories with data. After all, without predictive or prescriptive–and other–insights, big data is useless.
The bootcamp also stresses the importance of domain knowledge, and relatedly, an awareness of what precise data-points should be sought and analyzed. DS-Dojo also trains students to critically assess: why, and how should we classify data? Students also learn the typical data-collection, processing, and analysis pipeline, i.e.:
And finally, interpretation and evaluation.
However, any aspiring (good) data scientist should disabuse themselves of the notion that the process doesn’t present challenges. Au contraire, there are numerous challenges; e.g. (among others):
Complex and heterogeneous data
Data ownership and distribution,
Following the above coverage of the craft’s introductory processes and challenges, DS-Dojo students are then led earnestly into the deeper ends of data-science characteristics and features. For instance, vis-a-vis predictive analytics, how should a data-scientist decide when to use unsupervised learning, versus supervised learning? Among other considerations, practitioners can decide using the criteria listed below.
Unsupervised Learning…Vs. … >>
<< …Vs. …Supervised Learning
>> Target values unknown
>> Targets known
>> Training data unlabeled
>> Data labeled
>> Goal: discover information hidden in the data
>> Goal: Find a way to map attributes to target value(s)
Overall, the main domains covered by DS-Dojo’s data-science bootcamp curriculum are:
An introduction/overview of the field, including the above-described “big picture,” as well as visualization, and an emphasis on story-telling–or, stated differently, the retrieval of actual/real insights from data;
Overview of classification processes and tools
Applications of classification
Special topics–e.g., text-analysis
And “last but [certainly] not least,” big-data engineering and distribution systems.
In addition to the above-described advantageous traits, data-science enthusiasts, aspirants, and practitioners who join this program will be pleasantly surprised with the bootcamp’s de-emphasis on specific tools/approaches. In other words, instead of using doctrinaire approaches that favor only Python, or R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at our disposal.
“Whoo-Hoo! Yes, I’m a Data Scientist!”
By the end of the bootcamp, students might be tempted to adopt the above stance–i.e., as stated above (as this section’s title/subheading). But as a proud alumnus of the program, I would cautiously respond: “Maybe!” And if you have indeed mastered the concepts and tools, congratulations!
But strive to remember that the most passionate data-science practitioners possess a rather paradoxical trait: humility, and an openness to lifelong learning. As Raja Iqbal, CEO of DS-Dojo pointed out in one of the earlier lectures: The more I learn, the more I realize what I don’t know. Happy data-crunching!
Writing an SEO optimized blog is important because it can help increase the visibility of your blog on search engines, such as Google. When you use relevant keywords in your blog, it makes it easier for search engines to understand the content of your blog and to determine its relevance to specific search queries.
Consequently, your blog is more likely to rank higher on search engine results pages (SERPs), which can lead to more traffic and potential readers for your blog.
In addition to increasing the visibility of your blog, SEO optimization can also help to establish your blog as a credible and trustworthy source of information. By using relevant keywords and including external links to reputable sources, you can signal to search engines that your content is high-quality and valuable to readers.
5 things to consider for writing a top-performing blog
A successful blog reflects top-quality content and valuable information put together in coherent and comprehensible language to hook the readers.
The following key points can assist to strengthen your blog’s reputation and authority, resulting in more traffic and readers in the long haul.
1. Handpick topics from industry news and trends: One way to identify popular topics is to stay up to date on the latest developments in the data science and analytics industry. You can do this by reading industry news sources and following influencers on social media.
2. Use free – keyword research tools: Do not panic! You are not required to purchase any keyword tool to accomplish this step. Simply enter your potential blog topic on search engine such as Google and check out the top trending write-ups available online.
This helps you identify popular keywords related to data science and analytics. By analyzing search volume and competition for different keywords, you can get a sense of what topics are most in demand.
3. Look for the untapped information in the market: Another way to identify high-ranking blog topics is to look for areas where there is a lack of information or coverage. By filling these gaps, you can create content that is highly valuable and unique to your audience.
4. Understand the target audience: When selecting a topic, it’s also important to consider the interests and needs of your target audience. Check out the leading tech discussion forums and groups on Quora, LinkedIn, and Reddit to get familiar with the upcoming discussion ideas. What are they most interested in learning about? What questions do they have? By addressing these issues, you can create content that resonates with your readers.
5. Look into the leading industry websites: Finally, take a look at what other data science and analytics bloggers are writing about. From these acknowledged websites of the industry, you can get ideas for topics that help you identify areas where you can differentiate yourself from the competition
Recommended blog structure for SEO:
Overall, SEO optimization is a crucial aspect of blog writing that can help to increase the reach and impact of your content. The correct flow of your blog can increase your chances of gaining visibility and reaching a wider audience. Following are the step-by-step guidelines to write an SEO optimized blog on data science and analytics:
1. Choose relevant and targeted keywords:
Identify the keywords that are most relevant to your blog topic. Some of the popular keywords related to data science topics can be:
Business Intelligence (BI)
These are some of the keywords that are commonly searched by your target audience. Incorporate these keywords into your blog title, headings, and throughout the body of your post. Read the beginner’s guide to keyword research by Moz.
2. Use internal and external links:
Include internal links to other pages or blog posts on the website you are publishing your blog, and external links to reputable sources to support your content and improve its credibility.
3. Use header tags:
Use header tags (H1, H2, H3, etc.) to structure your blog post and signal to search engines the hierarchy of your content. Here is an example of a blog with the recommended header tags and blog structure:
H2: Linear Algebra and Optimization for Machine Learning
H2: The Hundred-Page Machine Learning Book
H2: R for everyone
H2: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
4. Use alt text for images:
Add alt text to your images to describe their content and improve the accessibility of your blog. Alt text is used to describe the content of an image on a web page. It is especially important for people who are using screen readers to access your website, as it provides a text-based description of the image for them.
Alt text is also used by search engines to understand the content of images and to determine the relevance of a web page to a specific search query.
5. Use a descriptive and keyword-rich URL:
Make sure your blog post URL accurately reflects the content of your post and includes your targeted keywords. For example, if the target keyword for your blog is data science books, then the URL must include the keyword in it such as “top-data-science-books“.
6. Write a compelling meta description:
The meta description is the brief summary that appears in the search results below your blog title. Use it to summarize the main points of your blog post and include your targeted keywords. For the blog topic: Top 6 data science books to learn in 2023, the meta description can be:
“Looking to up your data science game in 2023? Check out our list of the top 6 data science books to read this year. From foundational concepts to advanced techniques, these books cover a wide range of topics and will help you become a well-rounded data scientist.”
Share your data science insights with the world
If this blog helped you learn writing a search engine friendly blog, then without waiting a further, choose the topic of your choice and start writing. We offer a platform to industry experts and knowledge geeks to evoke their ideas and share them with a million plus community of data science enthusiasts across the globe.
Every eCommerce business depends on information to improve its sales. Data science can source, organize and visualize information. It also helps draw insights about customers, marketing channels, and competitors.
Every piece of information can serve different purposes. You can use data science to improve sales, customer service, user experience, marketing campaigns, purchase journeys, and more.
How to use Data Science to boost eCommerce sales
Sales in eCommerce depend on a variety of factors. You can use data to optimize each step in a customer’s journey to gain conversions and enhance revenue from each conversion.
Analyze Consumer Behavior
Data science can help you learn a lot about the consumer. Understanding consumer behavior is crucial for eCommerce businesses as it dictates the majority of their decisions.
Consumer behavior analysis is all about understanding the relationship between things you can do and customers’ reactions to them. This analysis requires data science as well as psychology. The end goal is not just understanding consumer behavior, but predicting it.
For example, if you have an eCommerce store for antique jewelry, you will want to understand what type of people buy antique jewelry, where they search for it, how they buy it, what information they seek before purchasing, what occasions they buy it for, and so on.
You can extract data on consumer behavior on your website, social media, search engines, and even other eCommerce websites. This data will help you understand customers and predict their behavior. This is crucial for audience segmentation.
Data science can help segment audiences based on demographics, characteristics, preferences, shopping patterns, spending habits, and more. You create different strategies to convert audiences of different segments.
Audience segments play a crucial role in designing purchase journeys, starting from awareness campaigns all the way to purchase and beyond.
Optimize digital marketing for better conversion
You need insights from data analytics to make important marketing decisions. Customer acquisition information can tell you where the majority of your audience comes from. You can also identify which sources give you maximum conversions.
You can then use data to improve the performance of your weak sources and reinforce the marketing efforts of high-performing sources. Either way, you can ensure that your marketing efforts are helping your bottom line.
Once you have locked down your channels of marketing, data science can help you improve results from marketing campaigns. You can learn what type of content or ads perform the best for your eCommerce website.
Data science will also tell you when the majority of your audience is online on the channel and how they interact with your content. Most marketers try to fight the algorithms to win. But with data science, you can uncover the secrets of social media algorithms to maximize your conversions.
Suggest products for upselling & cross-selling
Upselling & Cross-selling are some of the most common sales techniques employed by ecommerce platforms. Data science can help make them more effective. With Market Basket or Affinity Analysis, data scientists can identify relationships between different products.
By analyzing such information of past purchases and shopping patterns you can derive criteria for upselling and cross-selling. The average amount they spend on a particular type of product tells you how high you can upsell. If the data says that customers are more likely to purchase a particular brand, design, or color; you can upsell accordingly.
Similarly, you can offer relevant cross-selling suggestions based on customers’ data. Each product opens numerous cross-selling options.
Instead of offering general options, you can use data from various sources to offer targeted suggestions. You can give suggestions based on individual customers’ preferences. For instance, A customer is more likely to click on a suggestion saying “A Red Sweater to go with your Blue Jeans’ ‘ if their previous purchase shows an inclination for the color red.
This way data science can help increase probability of upsold & cross-sold purchases so that eCommerce businesses get more revenue from their customers.
Analyze consumer feedback
Consumers provide feedback in a variety of ways, some of which can only be understood by learning data science. It is not just about reviews and ratings. Customers speak about their experience through social media posts, social shares, and comments as well.
Feedback data can be extracted from several places and usually comes in large volumes. Data scientists use techniques like text analytics, computational linguistics, and natural language processing to analyze this data.
For instance, you can compare the percentage of positive words and negative words used in reviews to get a general idea about customer satisfaction.
But feedback analysis does not stop with language. Consumer feedback is also hidden in metrics like time spent on page, CTR, cart abandonment, clicks on page, heat maps and so on. Data on such sublime behaviors can tell you more about the customer’s experience with your eCommerce website than reviews, ratings and feedback forms.
This information helps you identify problem areas that cause your customers to turn away from a purchase.
Personalize customer experience
To create a personalized experience, you need information about the customer’s behavior, previous purchases, and social activity. This information is scattered across the web, and you need lessons in data science to bring it to one place. But, more importantly, data science helps you draw insights from information.
With this insight you can create different journeys for different customer segments. You utilize data points to map a sequence of options that would lead a customer to conversion. 80% customers are more likely to purchase if the eCommerce website offers a personalized experience.
For example: Your data analytics say that a particular customer has checked out hiking boots but has abandoned most purchases at the cart. Now you can focus on personalizing this customer’s experience by focusing on cart abandonment issues such as additional charges, postage shipping cost, payment options etc.
Several eCommerce websites use data to train their chatbots to serve as personal shopping assistants for their customers. These bots use different data points to give relevant shopping ideas.
You can also draw insights from data science to personalize offers, discounts, landing pages, product gallery, upselling suggestions, cross-selling ideas and more.
Use data science for decision making & automation
The information provided by data science serves as the foundation for decision-making for eCommerce businesses. In a competitive market, a key piece of information can help you outshine your competitors, gain more customers and provide a better customer experience.
In this blog, we asked ChatGPT to come up with some interesting and fun facts of the core areas related to data science, Artificial Intelligence, machine learning, and Statistics. The new AI tool surely knows the struggles of professionals and how to put it in a fun way.
Some fun facts for Mathematicians by ChatGPT:
Here are some fun facts for all statisticians to make you laugh by ChatGPT:
Statisticians have a sense of humor too! The probability of someone laughing at a joke you tell is directly proportional to the number of statistical jokes you know.
A statistician’s favorite thing to do is to take a sample.
A statistician’s favorite drink is a “mean” martini.
A statistician’s favorite time of day is “mean time.”
Statisticians have a tendency to “mean” well.
Statisticians are always looking for a “standard deviation” from the norm.
Statisticians are always looking for a “mean” to get ahead.
Statisticians are never “mean” to each other – they are always “normally distributed.”
Mathematicians are always on the lookout for new “equations” to solve.
A mathematician’s favorite thing is to “prove” a theorem.
Mathematicians are known to have a “matrix” of connections in the industry.
Mathematicians have been known to “calculate” the most unlikely scenarios.
A mathematician’s favorite type of math is “abstract” math – it keeps things interesting!
Mathematicians are always looking for ways to “optimize” their equations.
Mathematicians have a saying: “The more complex the problem, the better!”
Mathematicians are known to be “precise” – they never leave room for error in their calculations.
Some fun facts for Data Scientists by ChatGPT
Here are a few funny facts about data scientists:
Data scientists have been known to “mine” for data in the most unlikely places.
A data scientist’s favorite type of data is “big data” – the bigger, the better!
A data scientist’s favorite tool is the “data hammer” – they can pound any data into submission.
Data scientists have a saying: “The data never lies, but it can be misleading.”
Data scientists have been known to “data dunk” their colleagues – throwing them into a pool of data and seeing if they can swim.
Data scientists are always “data mining” for new insights and discovering “data gold.”
Data scientists are known to have “data-phoria” – a state of excitement or euphoria when they uncover a particularly interesting or valuable piece of data.
Data scientists have been known to “data mash” – combining different datasets to create something new and interesting.
Some fun facts for Machine Learning professionals by ChatGPT
Here are some fun facts about machine learning professionals
Machine learning professionals are always on the lookout for new “learning opportunities.”
A machine learning professional’s favorite thing is to “train” their algorithms.
Machine learning professionals are known to have a “neural network” of friends in the industry.
Machine learning professionals have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way.
A machine learning professional’s favorite type of data is “clean” data – it makes their job much easier!
Machine learning professionals are always looking for ways to “optimize” their algorithms.
Machine learning professionals have a saying: “The more data, the merrier!”
Machine learning professionals are known to be “adaptive” – they can quickly adjust to new technologies and techniques.
Some fun facts for AI experts by ChatGPT
Here are a few funny facts about artificial intelligence experts:
AI experts are always on the lookout for new “intelligent” ideas.
AI experts have been known to “teach” their algorithms to do new tasks.
AI experts are known to have a “neural network” of connections in the industry.
AI experts have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way.
AI experts are always looking for ways to “optimize” their algorithms.
AI experts have a saying: “The more data, the smarter the AI!”
AI experts are known to be “adaptive” – they can quickly adjust to new technologies and techniques.
AI experts are always looking for ways to make their algorithms more “human-like.”
The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy.
The first recorded instance of artificial intelligence was in the early 1800s when mathematician Charles Babbage designed a machine that could perform basic mathematical calculations.
One of the earliest demonstrations of artificial intelligence was the “Turing Test,” developed by Alan Turing in 1950. The test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
The first self-driving car was developed in the 1980s by researchers at Carnegie Mellon University.
In 1997, a computer program called Deep Blue defeated world chess champion Garry Kasparov, marking the first time a computer had beaten a human at chess.
In 2011, a machine translation system developed by Google called Google Translate was able to translate entire documents from Chinese to English with near-human accuracy.
In 2016, a machine learning algorithm developed by Google DeepMind called AlphaGo defeated the world champion at the ancient Chinese board game Go, which is considered to be much more complex than chess.
Artificial intelligence has the potential to revolutionize a wide range of industries, including healthcare, finance, and transportation.
Some fun facts for Data Engineers by ChatGPT
Here are a few funny facts about data engineers by ChatGPT:
Data engineers are always on the lookout for new “pipelines” to build.
A data engineer’s favorite thing is to “ingest” large amounts of data.
Data engineers are known to have a “data infrastructure” of connections in the industry.
Data engineers have been known to “scrape” the internet for new data sources.
A data engineer’s favorite type of data is “structured” data – it makes their job much easier!
Data engineers are always looking for ways to “optimize” their data pipelines.
Data engineers have a saying: “The more data, the merrier!”
Data engineers are known to be “adaptive” – they can quickly adjust to new technologies and techniques.
Do you have a more interesting answer by ChatGPT?
People across the world are generating interesting responses using ChatGPT. The new AI tool has an immense contribution to the knowledge of professionals associated with different industries. Not only does it produce witty responses but also share information that is not known before by many. Share with us your use of this amazing AI tool as a Data Scientist.
In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand.
Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career.
To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.
This talk will introduce you to what a typical data scientist’s job looks like. It will familiarize you with the day-to-day work that a data scientist does and differentiate between the different roles and responsibilities that data scientists have across companies.
This talk will help you understand what a typical day in the data scientist’s life looks like and assist you to decide if data science is the perfect choice for your career.
2. Data mining crash course
Data mining has become a vital part of data science and analytics in today’s world. And, if you planning to jumpstart your career in the field of data science, it is important for you to understand data mining. Data mining is a process of digging into different types of data and data sets to discover hidden connections between them.
The concept of data mining includes several steps that we are going to cover in this course. In this talk, we will cover how data mining is used in feature selection, connecting different data attributes, data aggregation, data exploration, and data transformation.
Additionally, we will cover the importance of checking data quality, reducing data noise, and visualizing the data to demonstrate the importance of good data.
3. Intro to data visualization with R & ggplot2
While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. Thereby, it is important for one to learn about data visualization with R & ggplot2.
In this tutorial, you will get a brief introduction to data visualization with the ggplot2 package. The focus of the tutorial will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.
4. Crash course in data visualization: Tell a story with your data
Telling a story with your data is more important than ever. The best insights and machine learning models will not create an impact unless you are able to effectively communicate with your stakeholders. Hence, it is very important for a data scientist to have an in-depth understanding of data visualization.
In this course, we will cover chart theory and pair programs that will help us create a chart using Python, Pandas, and Plotly.
5. Feature engineering
To become a proficient data scientist, it is significant for one to learn about feature engineering. In this talk, we will cover ways to do feature engineering both with dplyr (“mutate” and “transmute”) and base R (“ifelse”). Additionally, we’ll go over four different ways to combine datasets.
With this talk, you will learn how to impute missing values as well as create new values based on existing columns.
6. Intro to machine learning with R & caret
The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open-source machine-learning algorithms. If you are a data scientist working with R, the caret package (short for Classification and Regression Training) is a must-have tool in your toolbelt.
In this talk, we will provide an introduction to the caret package. The focus of the talk will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work.
7. Building robust machine learning models
Modern machine learning libraries make the model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent.
Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Thereby, our aim here is to take you through the fundamentals of building robust machine-learning models.
8. Text analytics crash course with R
Industries across the globe deal with structured and unstructured data. To generate insights companies, work towards analyzing their text data. The data pipeline for transforming unstructured text into valuable insights consists of several steps that each data scientist must learn about.
This course will take you through the fundamentals of text analytics and teach you how to transform text data using different machine-learning models.
9. Translating data into effective decisions
As data scientists, we are constantly focused on learning new ML techniques and algorithms. However, in any company, value is created primarily by making decisions. Therefore, it is important for a data scientist to embrace uncertainty in a data-driven way.
In this talk, we present a systematic process where ML is an input to improve our ability to make better decisions, thereby taking us closer to the prescriptive ideal.
10. Data science job interviews
Once you are through your data science learning path, it is important to work on your data science interviews in order to uplift your career. In this talk, you will learn how to solve SQL, probability, ML, coding, and case interview questions that are asked by FAANG + Wall Street.
We will also share the contrarian job-hunting tips that can help you to find a job at Facebook, Google, or an ML startup.
Step up to the data science pathway today!
We hope that the aforementioned 12 talks assist you to get started with your data science learning path. If you are looking for a more detailed guide, then do check out our Data Science Roadmap.
This blog covers the top 8 data science use cases in the finance industry that can help them when dealing with large volumes of data.
The finance industry deals with large volumes of data. With the increase in data and accessibility of AI, financial institutions can’t ignore the benefits of data science. They have to use data science to improve their services and products. It helps them make better decisions about customer behavior, product development, marketing strategies, etc.
From using machine learning algorithms to Python for Data Science, there are several key methods of applications of data science in finance. Listed below are the top eight examples of data science being used in the finance industry.
1. Trend forecasting
Data science plays a significant role in helping financial analysts forecast trends. For instance, data science uses quantitative methods such as regression analysis and linear programming to analyze data. These methods can help extract hidden patterns or features from large amounts of data, making trend forecasting easier and more accurate for financial institutions
2. Fraud detection
Financial institutions can be vulnerable to fraud because of their high volume of transactions. In order to prevent losses caused by fraud, organizations must use different tools to track suspicious activities. These include statistical analysis, pattern recognition, and anomaly detection via machine/deep learning. By using these methods, organizations can identify patterns and anomalies in the data and determine whether or not there is fraudulent activity taking place.
For example, financial institutions often use historical transaction data to detect fraudulent behavior. So when banks detect inconsistencies in your transactions, they can take action to prevent further fraudulent activities from happening.
3. Market research
Tools such as CRM and social media dashboards use data science to help financial institutions connect with their customers. They provide information about their customers’ behavior so that they can make informed decisions when it comes to product development and pricing.
Remember that the finance industry is highly competitive and requires continuous innovation to stay ahead of the game. Data science initiatives, such as a Data Science Bootcamp or training program, can be highly effective in helping companies develop new products and services that meet market demands.
4. Investment management
Investment management is another area where data science plays an important role. Companies use data-driven approaches to optimize investment portfolios. They also use predictive models, such as financial forecasting, to estimate future returns based on past performance. Such predictions allow investors to maximize profits and minimize risks when it comes to investing. In addition to providing valuable insight into the future, data science also provides guidance on how to best allocate capital and reduce risk exposure.
5. Risk analysis
Risks are unavoidable in any organization. However, managing those risks requires understanding their nature and causes. In the finance industry, companies use data science methods such as risk assessment and analysis to protect themselves against potential losses.
For example, they can tell you which products are likely to fail, and which assets are most susceptible to theft and other types of loss. And when applied properly, these tools can help an organization improve security, efficiency, and profitability.
6. Task automation
One of the greatest challenges faced by many firms today is the need to scale up operations while maintaining efficiency. To do so, they must automate certain processes. One way to achieve this goal is through the use of data science. Data scientists can develop tools that improve existing workflows within the finance industry.
Examples of these tools include speech-to-text, image recognition, and natural language processing. The finance industry uses insights from data science to automate systems that eliminate human error and accelerate operational efficiency.
7. Customer service
It’s no surprise that customer satisfaction affects revenue growth. As a result, companies spend large amounts of money to ensure that their customers receive top-notch service. Data science initiatives can help financial services providers deliver a superior experience to their customers. Whether it’s improving customer support apps or streamlining internal communications, financial companies can leverage this technology to transform their operations.
For instance, financial institutions can track consumer behavior to provide better customer service. A company may use data analytics to identify the best time to contact consumers by analyzing their online behavior. Companies can also monitor social media conversations and other sources for signs of dissatisfaction regarding their services to improve customer satisfaction.
For certain financial institutions, the ability to scale up could mean the difference between success and failure. The good news is that data science offers solutions and insight that help companies identify what areas need to be scaled. These insights help them decide whether they should hire additional staff or invest in new equipment, among other things.
A good example of using data analytics for scalability is IBM’s HR Attrition Case Study. IBM, one of the world’s leading technology firms, has been able to use data science to solve its own scaling challenges by using it to analyze trends and predict future outcomes. This study shows how data scientists used predictive analytics to understand why employees quit their jobs at IBM.
Data science revolutionizing finance industry
There’s no doubt that data science will revolutionize almost all aspects of the financial industry. By using different data science tools and methods, financial companies can gain competitive advantages. The great thing about data science is that it can be learned through various methods.
Data science bootcamps, online courses, and books offer all the tools necessary to get started. As a result, anyone who works in finance—whether they are junior analysts or senior executives—can learn how to incorporate data science techniques in their industry.
There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out.
1. Analytics Power Hour
Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show.
Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen.
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.
Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before.
Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts.
7. In Machines We Trust
This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in.
“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 mins long each, so it won’t take long to listen and get the latest info that you’re looking for.
8. More or Less
This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.
For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy.
9. Data Engineering Podcast
This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face.
Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips.
10. Data viz Today
This show doesn’t need a lot of commitment from you, as they release 30 min episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in.
Share with us exciting Data Science podcasts
These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!
These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.
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.
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:
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 makethe 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.
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.
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:
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:
Once you identify the entries to be fixed, simply replace the values with the correct version.
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:
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.
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 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.
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.
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:
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.
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:
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.
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.
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:
Mean: The mean is the average of a data set. It is “sensitive” to outliers.
Median: The median is the middle of the set of numbers. It is resistant to outliers
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
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.
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.
Notice how 209 propagated forward.
Let’s observe backward fill too
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.
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.
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:
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.
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.
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:
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.
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.
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.
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.
In this blog, we will discuss how companies apply data science in business and use combinations of multiple disciplines such as statistics, data analysis, and machine learning to analyze data and extract knowledge.
If you are a beginner or a professional seeking to learn more about concepts like Machine Learning, Deep Learning, and Neural Networks, the overview of these videos will help you develop your basic understanding of Data Science.
Overview of the data science course for beginners
If you are an aspiring data scientist, it is essential for you to understand the business problem first. It allows you to set the right direction for your data science project to achieve business goals.
As you are assigned a data science project, you must assure yourself to gather relevant information around the scope of the project. For that you must perform three steps:
Ask relevant questions from the client
Understand the objectives of the project
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:
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.
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.
In this intro to Azure Machine learning & Cloud Computing, we’ll cover some of the key benefits of using Azure and how it can help organizations get started with machine learning and cloud computing. We’ll also cover some of the key tools that are available in Azure to help you get started with your machine learning and cloud computing projects.
Start your Data Science journey today
If you are afraid of spending hundreds of dollars to enroll in a data science course, then direct yourself to the hundreds of free videos available online. Master your Data Science learning and step into the world of advanced technology.
Data science is used in different fields and industries. And believe it or not, it also plays a significant role in digital marketing. In this post, that is what we’re going to be discussing.
Data science is a big field, and it is employed extensively in different industries, from healthcare and transport to education and commerce. In fact, it is the cornerstone of groundbreaking technologies such as AI-based virtual assistants and self-driving cars.
“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 processing i.e., data mining, data modeling etc.
Finalization of results
If you want, you can learn more about data science by taking this course.
How Data Science is useful in digital marketing
Now that we’re done with this preamble, let’s move on to discuss how data science can be useful in digital marketing.
1. Keyword research
One of the main benefits of data science in digital marketing is providing help with keyword research. Actually, before moving on, let’s clear up how exactly keyword research is related to digital marketing.
Keyword research is a vital and necessary part of Search Engine Optimization (SEO). And SEO itself is a major branch of digital marketing. That’s basically how these two are connected.
Let’s get back to the point.
Whenever a digital marketing expert wants to work on the SEO of their website, they first have to create a keyword strategy for the content. The keyword strategy basically describes the short-tail and long-tail keywords that have to be featured in the website’s content and metadata. It also describes the number of times that the keywords have to be used and so on.
Now, there is no limit to the number of keywords that are (and can be) searched by online users. They literally run into trillions. When someone has to select a few from this vast and virtually endless trove of keywords, they have to employ data science.
Here is how data science can work in keyword research:
For the first phase, the digital marketer (or the SEO specialist) will narrow the keywords down to the ones related to their niche. This is, as we mentioned above, the “data collection” step.
Then, from this collection of keywords, the ones with high search volumes will be prioritized and short-listed. This is the “data organization” step.
After this, the specialist will have to find those long-tail and short-tail keywords that have a manageable ranking difficulty. In other words, this step will entail going through the shortlisted keywords and handpicking the most suitable ones.
Then, the selected keywords will be refined even more until the finalized list is prepared. This can be referred to as the “data analysis” step.
And once all the above is done, the list of keywords will be prepared in a document and given to the relevant personnel. This is the last step of the data science life cycle.
So, taking a look from the first step of the process to the last one, we can observe that from a list of infinite keywords, a selected number of them were handpicked and finalized. Again, this is basically what data science is. To find patterns and useful insights from unsorted or sorted data.
2. Analysis of website performance metrics
This is yet another instance of digital marketing where data science can be highly beneficial.
Basically, digital markers have to keep an eye on the performance of their website or online platform. They have to see how users are interacting with the various web pages and how much traffic the website(s) is/are generating.
To measure website performance, there are actually a lot of different stats and metrics. For example, some of them include:
Amount of traffic
Requests per second
By employing data science strategies to gather and analyze the various metrics, digital marketers can easily understand how well their website is working and how users are interacting with it.
Similarly, by analyzing these metrics, they can also easily find out if the website (or a particular webpage) has been hit by a search engine penalty. This is actually a very useful benefit of keeping on top of website performance metrics.
There are different types of violations that can bring about a penalty from the search engine, or that can just simply reduce the traffic/popularity of a certain webpage.
For one, if a page takes a lot of time to load, it can get abandoned by a lot of users. This can be detected if there is a rise in the bounce rate and a decrease in the dwell time. Incidentally, the loading time itself is a website performance metric on its own.
To improve the loading time, methods such as code beautification and minimization can be used. Similarly, the images and effects featured on the page can be toned down etc.
Plagiarism is also a harmful factor that can get websites penalized. These types of penalties can either reduce a website’s rank or get it completely de-listed.
To avoid this, webmasters always have to check plagiarism before finalizing any content for their websites.
This is usually done with the help of plagiarism-checking tools that can scan the given content against the internet in order to find any duplication that may exist in the former.
3. Monitoring website ranking statistics
Just as monitoring website performance by analyzing statistics like the bounce rate, dwell time etc., is important, staying on top of the ranking statistics is equally necessary.
By staying up-to-date with the website ranking in the SERPs, digital marketers are able to adjust and manage their SEO strategies. If upon taking a certain step, the rank of the site drops, then it means that it (the step) should not be taken in future. On the other hand, if the rank rises after making some changes to the website, then it is a signal indicating that the changes are beneficial rather than harmful.
Data science can be employed for keeping up with this information as well.
Grow digital marketing with Data Science
There are actually a lot of other ways in which data science can be useful in digital marketing. But, since we want to stick to brevity, we’ve listed some common and main ones above.
Get hired as a Data Analyst by confidently responding to the most asked interview questions. No matter how qualified or experienced you are, if you stumble over your thoughts while answering the interviewer, it might take away some of your chances of getting onboard.
1. Share about your most successful/most challenging data analysis project?
In this question, you can also share your strengths and weaknesses with the interviewer.
When answering questions like these, data analysts must attempt to share both their strengths and weaknesses. How do you deal with challenges and how do you measure the success of a data project? You can discuss how you succeeded with your project and what made it successful.
Take a look at the original job description to see if you can incorporate some of the requirements and skills listed. If you were asked the negative version of the question, be honest about what went wrong and what you would do differently in the future to fix the problem. Despite our human nature, mistakes are a part of life. What’s critical is your ability to learn from them.
Further talk about any SAAS platforms, programming languages, and libraries. Why did you use them and how did you use them to accomplish yours?
Discuss the entire pipeline of your projects from collecting data, to turning it into valuable insights. Describe the ETL pipeline including data cleaning, data preprocessing, and exploratory data analysis. What were your learnings and what issues did you encounter and how did you deal with them.
2. Tell us about the largest data set you’ve worked with? Or What type of data you have worked with in the past?
What they’re really asking: Can you handle large data sets?
Data sets of varying sizes and compositions are becoming increasingly common in many businesses. Answering questions about data size and variety requires a thorough understanding of the type of data and its nature. What data sets did you handle? What types of data were present?
It is not necessary that you should only mention a dataset you worked with at your job. But you can also share about varying sizes specifically large datasets you worked with as a part of a data analysis course, Bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.
The more versatile your experience with datasets will be, the greater the chances there are of getting hired.
The expected answer to this question will include details about: How you handle missing data, outliers, duplicate data, etc.?c.?
Data analysts are widely responsible for data preparation, data cleansing, or data cleaning. Organizations expect data analysts to spend a significant amount of time preparing data for an employer. As you answer this question, share in detail with the employer why data cleaning is so important.
In your answer, give a short description of what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set.
4. Name some data analytics software you are familiar with. OR What data software have you used in the past? OR What data analytics software are you trained in?
What they need to know: Do you have basic competency with common tools? How much training will you need?
Before you appear for the interview, it’s a good time to look at the job listing to see what software was mentioned. As you answer this question, describe how you have used that software or something similar in the past. Show your knowledge of the tool by employing associated words.
Mention software solutions you have used for a variety of data analysis phases. You don’t need to provide a lengthy explanation. What data analytics tools you used and for which purpose will satisfy the interviewer.
5. What statistical methods have you used in data analysis? OR what is your knowledge of statistics? OR how have you used statistics in your work as a Data Analyst?
What they’re really asking: Do you have basic statistical knowledge?
Data analysts should have at least a rudimentary grasp of statistics and know-how that statistical analysis helps business goals. Organizations look for a sound knowledge of statistics in Data analysts to handle complex projects conveniently. If you used any statistical calculations in the past, be sure to mention it. If you haven’t yet, familiarize yourself with the following statistical concepts:
Descriptive and inferential statistics
While speaking of these, share information that you can derive from them. What knowledge can you gain about your dataset?
Read these amazing 12 Data Analytics books to strengthen your knowledge
In order to be a data analyst, you will almost certainly need both SQL and a statistical programming language like R or Python. If you are already proficient in the programming language of your choice at the job interview, that’s fine. If not, you can demonstrate your enthusiasm for learning it.
In addition to your current languages’ expertise, mention how you are developing your expertise in other languages. If there are any plans for completing a programming language course, highlight its details during the interview.
To gain some extra points, do not hesitate to mention why and in which situations SQL is used, and why R and python are used.
7. How can you handle missing values in a dataset?
This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset.
In the listwise deletion method, an entire record is excluded from analysis if any single value is missing.
Take the average value of the other participants’ responses and fill in the missing value.
You can use multiple-regression analyses to estimate a missing value.
It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions.
8. What is Time Series analysis?
Data analysts are responsible for analyzing data points collected at different intervals. While answering this question you also need to talk about the correlation between the data evident in time-series data.
Watch this short video to learn in detail:
9. What is the difference between data profiling and data mining?
Profiling data attributes such as data type, frequency, and length, as well as their discrete values and value ranges, can provide valuable information on data attributes. It also assesses source data to understand its structure and quality through data collection and quality checks.
On the other hand, data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data.
10. Explain the difference between R-Squared and Adjusted R-Squared.
The most vital difference between adjusted R-squared and R-squared is simply that adjusted R-squared considers and tests different independent variables against the model and R-squared does not.
An R-squared value is an important statistic for comparing two variables. However, when examining the relationship between a single stock and the rest of the S&P500, it is important to use adjusted R-squared to determine any discrepancies in correlation.
11. Explain univariate, bivariate, and multivariate analysis.
Bivariate analysis, which is simpler than univariate analysis, is used when the data set only has one variable and it does not involve causes or effects.
Univariate analysis, which is more complicated than bivariate analysis, is used when the data set has two variables and researchers are looking to compare them.
When the data set has two variables and researchers are investigating similarities between them, multivariate analysis is the right type of statistical approach.
12. How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?
Before appearing for an interview, make sure you study the company thoroughly and gain enough knowledge about it. It will leave an impression on the employer regarding your interest and enthusiasm to work with them. Also, in your answer you talk about the added value you will bring to the company by improving its business performance.
13. What do you think are the three best qualities that great data analysts share?
List down some of the most critical qualities of a Data Analyst. This may include problem-solving, research, and attention to detail. Apart from these qualities, do not forget to mention soft skills which are necessary to communicate with team members and across the department.
Did we miss any Data Analysts interview questions?
Share with us in the comments below and help each other to ace the next data analyst job.
Data Science Dojo is offering RStudio for FREE on Azure Marketplace packaged with a pre-installed running version of R alongside other language backends to simplify Data Science.
What is data science?
Data Science is one of the quickest-growing areas of work in the industry. According to Harvard Business Review, it’s regarded as the “sexiest job of the 21st century”.
Data science joins math and measurements, programming, refined analyses, machine learning and AI to reveal significant knowledge concealed in an association’s dataset. These understandings can be utilized to direct businesses in planning and decision making. The lifecycle of Data Science involves data collection (ingestion), data pre-processing and wrangling, predictive data analysis via machine learning and finally communication of outcomes for future strategies.
Individuals who were learning or pursuing Data Science and Machine Learning through R found it difficult to code and develop models using only a terminal or command line interface. Developers who wanted to perform extensive high powered ML operations but didn’t have enough computation power to do it locally was also another challenge.
In these circumstances an interactive environment configured with R can help the users in gaining hands-on experience with machine learning, data analysis and other statistical operations.
Working with RStudio
RStudio is an open-source tool that gives you an effortless coding IDE in the cloud with a pre-installed R programming language to start your data mining and analytics work. It is integrated with a set of modules that make code development, scientific computing, and graphical jobs to be more productive and easier. This tool allows developers to perform a variety of technical tasks such as predictive modeling, clustering, multivariate querying, stock market rate, spam filtering, recommendation systems, malware, and anomaly detection, image recognition, and medical diagnosis.
Provides an in-browser coding environment with syntax suggestions, autocomplete code feature and smart indentation
Provides the user with an easy-to-use free coding platform accessible at the local web server, powered by Azure machines
Apart from the primary built of R, RStudio has support for other famous interpreters as well such as Python, SQL, HTML, CSS, JS, C, Quarto and a few others
In-built debugging functionality by toggling breakpoints to detect and eradicate the issues or fix them quickly
As the computations are carried on Microsoft’s cloud servers, there is no memory or performance pressure on the company’s storage devices
In order to optimize the workload, the RAM and compute power can be scaled accordingly, thanks to Azure services
What Data Science Dojo has for you
The RStudio instance packaged by Data Science Dojo provides an in-browser coding environment with a running version of R pre-deployed in it, reducing the burden of installation. With an interactive user-friendly GUI-based application, developers can perform Machine Learning tasks with comfort and flexibility.
A browser based RStudio environment up and running with R pre-deployed
Convenient accessibility and navigation
Ability to work with different language scripts simultaneously
Rich graphics and interactive environment
Support for git and version control
Code consoles to run code interactively, with full support for rich output
Integrated R documentation and user help
Readily available cheat sheets to get started
Our instance supports the following backends:
Markdown and Header files
RStudio provides customers with an easy-to-use environment to gain hands-on experience with Machine Learning and Data Science. The responsiveness and processing speed are much better than the traditional desktop environment as it uses Microsoft cloud services. It comes with built-in support for git and version control.
Several variants of the R script can be executed in RStudio. It allows users to work on a variety of language backends at the same time with smart observability of variables and values side by side. The documentation and user support are incorporated into the tool to make it easy for developers to code.
At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free RStudio instance dedicated specifically to Machine Learning and Data Science on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science!
Click on the button below to head over to the Azure Marketplace and deploy Rstudio for FREE by clicking on “Get it now”.
Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.
In this blog, we will have a look at the list of free Data Science crash courses to help you succeed in Data Science
With more and more people entering the field, data science and data engineering are surely amongst the topmost emerging areas of work in the 21st century. Higher salaries, perks, benefits, and demand has made it a field of interest for 1000s of people.
While a good chunk of students is opting for data science in their undergraduate and graduate programs, there are people who are opting for different Data Science Bootcamps to get started with the field.
However, enrolling instantly in an expensive undergraduate, master’s, or data science Bootcamp might not be the correct choice for one to go with. An individual would want to explore more within the scope of data science before switching fields or making the final call. Hence, below we present a list of free data science crash courses that an individual can go through before choosing their career path.
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.
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.
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!