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Top 7 Online Data Science Courses — 2024 Guide & Reviews

Learn data science online this year by taking one of these top-ranked courses

Over several years and 100+ hours watching course videos, engaging with quizzes and assignments, and reading reviews on various aggregators and forums, I’ve narrowed down the best data science courses available to the list below.

This is a fairly long article with reviews of each course, so here’s the TL;DR:

7 Best Data Science Courses & Certifications for 2024:

Data science specialization — jhu @ coursera, applied data science with python specialization — umich @ coursera, data science micromasters — uc san diego @ edx, statistics and data science micromasters — mit @ edx, cs109 data science — harvard, python for data science and machine learning bootcamp — udemy.

The selections here are geared more toward individuals getting started in data science, so I’ve filtered courses based on the following criteria:

• The course goes over the entire data science process
• The course uses popular open-source programming tools and libraries
• The instructors cover the basic, most popular machine learning algorithms
• The course has a good combination of theory and application
• The course needs to either be on-demand or available every month or so
• There are hands-on assignments and projects
• The instructors are engaging and personable
• The course has excellent ratings – generally greater than or equal to 4.5/5

There are many more data science courses than when I started this page four years ago, so there needs to be a substantial filter to determine which courses are the best. I hope you feel confident that the courses below are worth your time and effort because it will take several months to learn and practice to be a data science practitioner.

Complementary courses

Some of the courses listed below teach introductory Python, but if you'd like to learn programming before joining a data science course, check out my picks for the best Python courses .

In addition to the top general data science course picks, I have included a separate section for more specific data science interests, like Deep Learning, SQL, and other relevant topics. These courses have a more specialized approach and don’t cover the whole data science process, but they are still the top choices for that topic. These extra picks are good for supplementing before, after, and during the main courses.

If you're more interested in just learning machine learning, then check out my complementary article on the best machine learning courses for this year.

Book companions

When learning data science online, it’s vital to get an intuitive understanding of what you’re actually doing and sufficient practice using data science on unique problems.

In addition to the courses listed below, I would suggest reading two books:

• Introduction to Statistical Learning — available for Free — is one of the most widely recommended books for beginners in data science. Explains the fundamentals of machine learning and how everything works behind the scenes
• Applied Predictive Modeling — a breakdown of the entire modeling process on real-world datasets with incredibly useful tips each step of the way

These two textbooks are incredibly valuable and provide a much better foundation than just taking courses alone. The first book is incredibly effective at teaching the intuition behind much of the data science process, and if you can understand almost everything in there, then you’re more well off than most entry-level data scientists.

Furthermore, since both of these books utilize R in their exercises and examples, a great learning experience would be to work through them in R and then convert them to Python.

Use Video Speed Controller for Chrome to speed up any video. I usually choose between 1.5x - 2.5x speed depending on the content, and use the “s” (slow down) and “d” (speed up) key shortcuts that come with the extension.

This course series is one of the most enrolled and highly rated course collections on this list. JHU did an incredible job with the curriculum's balance of breadth and depth. One thing included in this series that's usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.

Overall, the Data Science specialization is an ideal mix of theory and application using the R programming language. As far as prerequisites go, you should have some programming experience (it doesn't have to be R) and a good understanding of Algebra. While not necessary, previous knowledge of Linear Algebra and Calculus is helpful.

Price – Free or $49/month for certificate and graded materials Provider – Johns Hopkins University

Curriculum :

• The Data Scientist's Toolbox
• R Programming
• Getting and Cleaning Data
• Exploratory Data Analysis
• Reproducible Research
• Statistical Inference
• Regression Models
• Practical Machine Learning
• Developing Data Products
• Data Science Capstone

If you're rusty with statistics and want to learn more about R first, check out the Statistics with R Specialization .

The University of Michigan, which also launched an online data science Master’s degree , produces this fantastic specialization focused on the applied side of data science. This means you’ll get a solid introduction to commonly used data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to use them on real data.

This series doesn’t include the statistics needed for data science or the derivations of various machine learning algorithms but does provide a comprehensive breakdown of how to use and evaluate those algorithms in Python. Because of this, I think this would be more appropriate for someone that already knows R and/or is learning the statistical concepts elsewhere.

If you’re rusty with statistics, consider the Statistics with Python Specialization first. You’ll learn many of the most important statistical skills needed for data science.

Price – Free or $49/month for certificate and graded materials Provider – University of Michigan

• Introduction to Data Science in Python
• Applied Plotting, Charting & Data Representation in Python
• Applied Machine Learning in Python
• Applied Text Mining in Python
• Applied Social Network Analysis in Python

To take these courses, you’ll need to know some Python or programming in general, and there are actually a couple of great lectures in the first course dealing with some of the more advanced Python features you’ll need to process data effectively.

MicroMasters from edX are advanced, graduate-level courses that count towards a real Master’s at select institutions. In the case of this MicroMaster’s, completing the courses and receiving a certificate will count as 30% of the full Master of Science in Data Science degree from Rochester Institute of Technology (RIT).

Since these courses are geared toward prospective Master’s students, the prerequisites are higher than many of the other courses on this list. Since the first course in this series doesn’t spend any time teaching basic Python concepts, you should already be comfortable with programming. Spending some time going through a platform like Codecademy would probably get you up to speed for the first course.

Overall, I found this MicroMaster’s to be a perfect mix of theory and application. The lectures are comprehensive in scope and balanced superbly with real-world applications.

Price – Free or $1,260 for certificate and graded materials Provider – UC San Diego

• Python for Data Science
• Probability and Statistics in Data Science using Python
• Machine Learning Fundamentals
• Big Data Analytics using Spark

The one downside of this MicroMaster’s, and many courses on edX, is that they aren’t offered as frequently as other platforms. If your schedule aligns with the first course's start date, consider jumping in.

Dataquest is a fantastic resource on its own, but even if you take other courses on this list, Dataquest serves as a superb complement to your online learning.

Dataquest foregoes video lessons and instead teaches through an interactive textbook of sorts. Every topic in the data science track is accompanied by several in-browser, interactive coding steps that guide you through applying the exact topic you’re learning.

Video-based learning is more “passive” — it's very easy to think you understand a concept after watching a 2-hour long video, only to freeze up when you actually have to put what you've learned in action. — Dataquest FAQ

Dataquest stands out from the other interactive platforms because the curriculum is very well organized, you get to learn by working on full-fledged data science projects, and there's a super active and helpful Slack community where you can ask questions.

The platform has one primary data science learning curriculum for Python:

Data Scientist In Python Path This track currently contains 31 courses, covering everything from Python's basics to Statistics to math for Machine Learning, Deep Learning, and more. The curriculum is constantly being improved and updated for a better learning experience.

Price – 1/3 of content is Free, \$29/month for Basic, \$49/month for Premium

Here's a condensed version of the curriculum:

• Python - Basic to Advanced
• Python data science libraries - Pandas, NumPy, Matplotlib, and more
• Visualization and Storytelling
• Effective data cleaning and exploratory data analysis
• Command-line and Git for data science
• SQL - Basic to Advanced
• APIs and Web Scraping
• Probability and Statistics - Basic to Intermediate
• Math for Machine Learning - Linear Algebra and Calculus
• Machine Learning with Python - Regression, K-Means, Decision Trees, Deep Learning, and more
• Natural Language Processing
• Spark and Map-Reduce

Additionally, there are also entire data science projects scattered throughout the curriculum. Each project's goal is to apply everything you've learned up to that point and familiarize you with what it's like to work on an end-to-end data science strategy.

Lastly, if you're more interested in learning data science with R, check out Dataquest's new Data Analyst in R path. The Dataquest subscription gives you access to all paths on their platform so you can learn R or Python (or both!).

The inclusion of probability and statistics courses makes this series from MIT a well-rounded curriculum for understanding data intuitively. This MicroMaster's from MIT dedicates more time towards statistical content than the UC San Diego MicroMaster's mentioned earlier in the list.

Due to its advanced nature, you should have experience with single and multivariate calculus and Python programming. There isn't any introduction to Python or R like in some of the other courses in this list, so before starting the ML portion, they recommend taking Introduction to Computer Science and Programming Using Python to get familiar with Python. If you'd rather utilize an on-demand interactive platform to learn Python, check out Codecademy's Python track .

Price – Free or $1,350 for certificate and graded materials Provider – University of Michigan

• Probability - The Science of Uncertainty and Data
• Data Analysis in Social Science—Assessing Your Knowledge
• Fundamentals of Statistics
• Machine Learning with Python: From Linear Models to Deep Learning
• Capstone Exam in Statistics and Data Science

The ML course has several interesting projects you'll work on, and at the end of the whole series, you'll focus on one exam to wrap everything up.

With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. It’s not on an interactive platform, like Coursera or edX, and doesn’t offer any certification, but it’s free and definitely worth your time.

Curriculum:

• Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas
• Pandas, SQL, and the Grammar of Data
• Statistical Models
• Storytelling and Effective Communication
• Bias and Regression
• Classification, kNN, Cross-Validation, Dimensionality Reduction, PCA, MDS
• SVM, Evaluation, Decision Trees and Random Forests, Ensemble Methods, Best Practices
• Recommendations, MapReduce, Spark
• Bayes Theorem, Bayesian Methods, Text Data
• Effective Presentations
• Experimental Design
• Deep Networks
• Building Data Science

Python is used in this course, and many lectures go through the intricacies of the various data science libraries to work through real-world, exciting problems. This is one of the only data science courses that actually touches on every part of the data science process.

Also available using R .

A very reasonably priced course for the value. The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects. A huge benefit to this course over other Udemy courses is the assignments. Throughout the course, you’ll break away and work on Jupyter Notebook workbooks to solidify your understanding; then, the instructor follows up with a solutions video to thoroughly explain each part.

• Python Crash Course
• Python for Data Analysis - Numpy, Pandas
• Python for Data Visualization - Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
• Data Capstone Project
• Machine learning - Regression, kNN, Trees and Forests, SVM, K-Means, PCA
• Recommender Systems
• Big Data and Spark
• Neural Nets and Deep Learning

This course focuses more on the applied side, and one thing missing is a section on statistics. If you plan on taking this course, it would be a good idea to pair it with a separate statistics and probability course as well.

An honorary mention goes out to another Udemy course: Data Science A-Z . I do like Data Science A-Z quite a bit due to its complete coverage. Still, since it uses other tools outside of the Python/R ecosystem, I don’t think it fits the criteria as well as Python for Data Science and Machine Learning Bootcamp .

Other top data science courses for specific skills

Deep Learning Specialization — Coursera Created by Andrew Ng, maker of the famous Stanford Machine Learning course , this is one of the highest-rated data science courses on the internet. This course series is for those interested in understanding and working with neural networks in Python.

Complete SQL Mastery — CodeWithMosh A fantastic beginner to advanced SQL course. Check out my picks for the best SQL courses for more options and reviews.

Computational Thinking using Python XSeries — edX Although this series only runs once every several months, if you’re new to Computer Science and Python, this is a great series to jump into if you get the chance. I found the lecturers passionate about what they teach, making it a pleasant experience to take the courses.

Mathematics for Machine Learning — Coursera This is one of the most highly rated courses dedicated to the specific mathematics used in ML. Take this course if you’re uncomfortable with the linear algebra and calculus required for machine learning, and you’ll save some time over other, more generic math courses.

Bayesian Statistics: From Concept to Data Analysis — Coursera Bayesian, instead of Frequentist, statistics is an important subject to learn for data science. Many of us learned Frequentist statistics in college without even knowing it, and this course does a great job comparing and contrasting the two to make it easier to understand the Bayesian approach to data analysis.

Spark and Python for Big Data with PySpark — Udemy From the same instructor as the Python for Data Science and Machine Learning Bootcamp in the list above, this course teaches you how to leverage Spark and Python to perform data analysis and machine learning on an AWS cluster. The instructor makes this course fun and engaging by giving you mock consulting projects to work on, then going through a complete walkthrough of the solution.

Learning Guide

How to actually learn data science.

When joining any of these courses, you should make the same commitment to learning as you would to a college course. One goal for learning data science online is to maximize mental discomfort. It’s easy to get caught in the habit of signing in to watch a few videos and feel like you’re learning, but you’re not really learning much unless it hurts your brain.

Vik Paruchuri (from Dataquest ) produced this helpful video on how to approach learning data science effectively:

Essentially, it comes down to doing what you’re learning , i.e., when you take a course and learn a skill, apply it to a real project immediately . Working through real-world projects, you are genuinely interested in helps solidify your understanding and proves you know what you’re doing.

One of the most uncomfortable things about learning data science online is that you never really know when you’ve learned enough. Unlike in a formal school environment, when learning online, you don’t have many good barometers for success, like passing or failing tests or entire courses. Projects help remediate this by first showing you what you don’t know and then serving as a record of knowledge when it’s done.

Overall, the project should be the main focus and courses and books should supplement that.

When I first started learning data science and machine learning, I began (as a lot do) by trying to predict stocks. I found courses, books, and papers that taught the things I wanted to know, and then I applied them to my project as I was learning. I learned so much in such a short period of time that it seems like an improbable feat if laid out as a curriculum.

It turned out to be extremely powerful working on something I was passionate about. It was easy to work hard and learn nonstop because predicting the market was something I really wanted to accomplish.

Essential knowledge and skills

All data scientists must possess a base skill set and level of knowledge, regardless of what industry they’re in. For hard skills, you need not only to be proficient with the mathematics of data science but also the skills and intuition to understand data.

The Mathematics you should be comfortable with:

• Statistics (Frequentist and Bayesian)
• Probability
• Linear Algebra
• Basic calculus
• Optimization

Furthermore, these are the basic programming skills you should be comfortable with:

• Python or R,
• Extracting data from various sources, like SQL databases, JSON, CSV, XML, and text files
• Cleaning and transforming unstructured, messy data
• Effective Data visualization
• Machine learning – Regression, Clustering, kNN, SVM, Trees and Forests, Ensembles, Naive Bayes

Lastly, it’s not all about the hard skills; many critical soft skills aren’t taught in courses. These are:

• Curiosity and creativity
• Communication skills – speaking and presenting in front of groups and explaining complex topics to non-technical team members.
• Problem-solving – coming up with analytical solutions for business problems.

Python vs. R

After going through the list, you might have noticed that each course is dedicated to one language: Python or R. So which one should you learn?

Short answer: just learn Python , or learn both .

Python is an incredibly versatile language with huge support in data science, machine learning, and statistics. You can also do things like build web apps, automate tasks, scrape the web , create GUIs, build a blockchain, and create games.

Because Python can do so many things, I think it should be your chosen language. Ultimately, it doesn’t matter that much which language you choose for data science since you’ll find many jobs looking for either. So why not pick the language that can do almost anything?

However, learning R is also very useful in the long run since many statistics/ML textbooks use R for examples and exercises. In fact, both books I mentioned at the beginning use R, and unless someone translates everything to Python and posts it to Github, you won’t get the full benefit of the book. Once you learn Python, you’ll be able to learn R pretty easily.

Check out this StackExchange answer for a great breakdown of how the two languages differ in machine learning.

Are certificates worth it?

One big difference between Udemy and other platforms—like edX, Coursera, and Metis—is that the latter platforms offer certificates upon completion and are usually taught by university instructors.

Some certificates, like those from edX and Metis, even carry continuing education credits. Other than that, many real benefits, like accessing graded homework and tests, are only accessible if you upgrade. If you need to stay motivated to complete the entire course, committing to a certificate also puts money on the line, making you less likely to quit. There’s definitely personal value in certificates, but unfortunately, not many employers value them that much.

Coursera and edX vs. Udemy

Udemy does not currently have a way to offer certificates, so I generally find Udemy courses to be good for more applied learning material. In contrast, Coursera and edX are usually better for theory and foundational material.

Whenever I’m looking for a course about a specific tool, whether Spark, Hadoop, Postgres, or Flask web apps, I search Udemy first since the courses favor an actionable, applied approach. Conversely, when I need an intuitive understanding of a subject, like NLP, Deep Learning, or Bayesian Statistics, I’ll search edX and Coursera first.

Wrapping Up

Data science is a vast, interesting, and rewarding field to study and be a part of. You’ll need many skills, a wide range of knowledge, and a passion for data to become an effective data scientist that companies want to hire, and it’ll take longer than the hyped-up YouTube videos claim.

If you’re more interested in the machine learning and AI side of data science, check out my articles on machine learning courses and AI courses as supplements to this article.

If you have any questions or suggestions, feel free to leave them in the comments below.

Thanks for reading, and have fun learning!

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Free Data Science Resources for Beginners

In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.

To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to business analytics.

However, the rewards are worth it. Organizations will prize alchemists who can turn raw data into smarter decisions , better products , happier customers , and ultimately more profit . Plus, you’ll get to solve interesting problems and master new, impactful technologies.

If that sounds like a career you’d enjoy, then bookmark this page and read on because we compiled this list just for you.

Data Science Resources

• Programming and Data Wrangling
• Statistics and Probability
• Data Collection
• Data Visualization
• Applied Machine Learning
• Communication
• Creativity and Innovation
• Operations and Strategy
• Business Analytics
• Natural Language Processing
• Recommendation Systems
• Time Series Analysis
• Competitions
• Problem Solving Challenges

[images style=”0″ image=”https%3A%2F%2Felitedatascience.com%2Fwp-content%2Fuploads%2F2017%2F05%2Fdata-science-diamond.png” width=”960″ align=”center” top_margin=”0″ alt_text=”Data%20Science%20Diamond” full_width=”Y”]

* Note: Advanced, Niche, or Industry-Specific Skills

Certain roles might require other skills, such as:

Deep Learning, Big Data, Optimization, Anomaly Detection, Graph and Network Models, Quantitative Finance, Research Leadership, Project Management, Product Design, Software Engineering, Spacial Data Analysis, etc…

In this guide, we’ll only be covering the skills that are most frequently demanded across the industry.

[images style=”0″ image=”https%3A%2F%2Felitedatascience.com%2Fwp-content%2Fuploads%2F2017%2F05%2Fdata-science-diamond-foundation.png” width=”960″ align=”center” top_margin=”0″ alt_text=”Data%20Science%20Foundation” full_width=”Y”]

1. Foundational Skills

Foundational skills form the basis of true understanding, which will in turn allow you to discover novel solutions, build more accurate models, and make better decisions. Before getting to other topic-specific data science resources, it’s important to lay the groundwork first.

1.1. Programming and Data Wrangling

First, you’ll need to know at least one scripting language well enough to wrangle datasets, prototype models, and perform analyses.

We strongly recommend choosing between Python or R, as they are both open-source (free), widely adopted, and supported by active communities. They each have their own strengths, but we recommend picking just one at the start.

• Python is more common in software startups, large tech firms, and adTech. Python tends to be more flexible because it’s a general purpose programming language. It’s also better for deep learning and processing data.
• R / RStudio is popular in research, finance, and analytics. R is a statistical programming language that has mature libraries for econometrics, statistics, and machine learning.
• We’ve also written a more detailed comparison of Python vs. R for data science .

If you’re still on the fence, we’d recommend starting with Python due to its breadth and flexibility (and it’s a bit more beginner-friendly).

Tip: Each resource link below opens in a new tab, so you won’t lose your place.

Python Resources:

• Learn Python the Hard Way (Online Book) –  Recommended for beginners who want a complete course in programming with Python.
• LearnPython.org (Interactive Tutorial)  –  Short, interactive tutorial for those who just need a quick way to pick up Python syntax.
• How to Think Like a Computer Scientist (Interactive Book) – Interactive “CS 101” course taught in Python that really focuses on the art of problem solving. This goes beyond the bare minimum needed to get started, but it’s such a wonderful gem that we had to include it here.
• PythonChallenge.com (Online Puzzle) – Fun puzzle with 33 levels that you can solve with Python programming.
• How to Learn Python for Data Science, The Self-Starter Way  – Our guide that covers these resources in more detail.

R / RStudio Resources:

• R for Data Science (Online Book) – Recommended for beginners who want a complete course in data science with R.
• Swirl (Interactive R Package) – Very cool R package that you can install and learn the language directly from inside RStudio (the most common interface used to run R).
• Introduction to Data Science with R (Video Series) – For those who learn better by watching someone else walk through the steps.

1.2. Statistics and Probability

A strong statistics foundation helps you fully understand machine learning, conditional probability, A/B testing, and many other core skills. It also helps you “think like a data scientist,” which include spotting biases, efficiently iterating on predictive models, and knowing how to extract insights from data.

Plus, learning the common probability distributions (especially Gaussian, Binomial, Uniform, Exponential, Poisson) is critical for implementing many real-world applications, such as multi-armed bandits, market-basket analyses, and anomaly detection programs.

• Statistics and Probability (Khan Academy) – Practical introduction to statistics and probability from Khan Academy. Recommended for getting up to speed quickly.
• Harvard Stats 110: Probability (Video Series) – Rigorous treatment of probability theory from Harvard. Recommended for building deeper mastery.
• Think Stats: Probability and Statistics for Programmers – Free PDF available. Excellent resource for those with programming backgrounds. Quote: “If you have basic skills in Python, you can use them to learn concepts in probability and statistics and practical skills for working with data.”
• Crash Course on Basic Statistics (PDF)  – Short PDF that covers a whirlwind review of key topics. We like this review sheet because it has simple intuitive explanations for each concept.
• How to Learn Statistics for Data Science, The Self-Starter Way – Our guide that covers these resources in more detail.

[images style=”0″ image=”https%3A%2F%2Felitedatascience.com%2Fwp-content%2Fuploads%2F2017%2F05%2Fdata-science-diamond-technical.png” width=”960″ align=”center” top_margin=”0″ alt_text=”Data%20Science%20Technical%20Skills” full_width=”Y”]

2. Technical Skills

Data science is all about converting raw data into insights, predictions, software, and so on. Therefore, you’ll need to be comfortable working with data.

Core technical skills include collecting, cleaning, managing, and visualizing data, plus the big umbrella of applied machine learning.

2.1. Data Collection

Everything hinges on the quality and quantity of your data. Just as a chemist needs the right chemicals, you’ll need relevant data.

There are 4 common ways to collect data:

• Internal Data.  This is proprietary data that your company collects through its operations or through partnerships with other providers. This is usually the most relevant data.
• Searching Online. Need a labeled set of 8 million videos? There’s a webpage for that…  Seriously, you’d be surprised at what you can find out there. Online datasets allow you to prototype before investing in proprietary data.
• API’s.  API’s allow you to programmatically (and legally) access datasets that other companies collect. You can find anything from Twitter feeds to weather data to financial data.
• Web Scraping . Web crawling and scraping is a powerful tool that you must use responsibly. It opens a whole new world, but make sure to respect terms of services.

API Resources:

• Python: requests Quickstart Guide (Tutorial) – How to use the  requests library to request data from API’s.
• R: httr Quickstart Guide (Tutorial) – How to use the  httr library to request data from API’s.

Web Scraping Resources:

• R: rvest (Tutorial) – Basic web scraping with the  rvest library.
• Python Web Scraping Libraries – Our overview of the Python web scraping landscape.

SQL is the lingua franca for database management and querying, and you should be able to write complex queries.

Learning SQL also gives a better understanding of relational data in general (i.e. data in “table” format), which will improve your data analysis skills in any language.

• Intro to SQL by Khan Academy (Course) – Comprehensive video series that covers every important SQL topic.
• sqlcourse.com (Interactive Tutorial) – Great to use review or a quick crash course.
• SQL Fundamentals (Course) – Course that covers the basics of SQL. Includes quizzes along the way to test your understanding.

2.3. Data Visualization

Data visualization is important for exploratory analysis and for communicating your insights, and no list of data science resources would be complete without this topic.

Raw data can be difficult to interpret, so you’ll need to investigate trends and distributions with plots and charts.

• Data Visualization in Python (Video Series) – Tutorial on using the  matplotlib library in Python.
• Data Visualization in R (Video Series) – Tutorial on using the  ggplot library in R.
• Python Seaborn Tutorial – Our tutorial for the  seaborn  library in Python, which we strongly recommend for beginners.

2.4. Applied Machine Learning

Machine learning is a broad umbrella term that contains many sub-tasks. In a nutshell, it’s about teaching computers how to learn patterns and models from data.

To some people, machine learning is synonymous with data science, but we consider it a separate field that heavily overlaps with data science. There’s no doubt that machine learning is a powerful toolset, and it’s the meatiest skill on this list.

• Machine Learning by Andrew Ng (Video Series)  – This is the gold standard when it comes to learning the theory behind machine learning courses.
• Elements of Statistical Learning (PDF) – Reference text.  This is one of the classic textbooks of the industry, but it requires a solid math background.
• An Introduction to Statistical Learning in R (PDF) –  Reference text . Another classic textbook that has gentler math requirements.
• How to Learn Machine Learning, the Self-Starter Way – Our beginner-friendly overview of the machine learning landscape.
• Modern Machine Learning Algorithms: Strengths and Weaknesses – Our concise tour of machine learning algorithms.
• Python Machine Learning Tutorial – Our end-to-end tutorial for training your first model using Python’s  Scikit-Learn library.

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3. Business Skills

Business skills and soft skills are sometimes overlooked in data science curricula, but they are supremely important, and employers will look out for them. Data science is never performed in a vacuum. You’ll need to anticipate business needs, think creatively about solutions, and communicate your insights clearly.

As machine learning libraries mature and algorithms become easier to use “out-of-the-box,” businesses will value people who can work with data  and work with people. This section of our list of data science resources will help you stand out.

3.1. Communication

If a tree falls in a forest but no one is around to hear it, does it make a sound? If data is analyzed but no one can explain the results, does it really matter?

Effective communication skills are universal, but data scientists have the added challenge of discussing highly technical or mathematical topics.

During data scientist interviews, you’ll often be asked to “explain a technical concept to a layperson” or “describe a previous project you’ve worked on.” Employers will specifically look for clarity, conciseness, and organization.

• The best stats you’ve ever seen (TED Talk)  – This is an iconic TED talk and a fun display of storytelling with data.
• Think Fast, Talk Smart (Video)  – This is a workshop at the Stanford Graduate School of Business on how to overcome anxiety and speak spontaneously. Not only will this help you for the rest of your career, but it will also allow you to stand out during your interview.
• 7 Tips for Improving Communication (Video)  – Simple, practical tips on how to communicate effectively on a daily basis.
• How to Win Friends and Influence People (PDF) , (Free Audiobook Version)  – This is a book we’d recommend for anyone, data scientist or not. While some of the verbiage is a bit dated, the teachings about interpersonal relationships are timeless.
• Practice teaching a technical concept to a friend – This will help you solidify your understanding of the concept while getting valuable communication practice. Try explaining an interesting machine learning algorithm, including its strengths, weaknesses, and proper use cases.
• Practice describing projects that you’ve completed – This will help you practice organizing the many moving parts of data science into coherent narratives.

3.2. Creativity & Innovation

Data scientists are hired to build new products, perform complex analyses, and invent valuable ways to use data.

In fact, they rarely solve the same problem twice. Even if you can apply the same methods to an adjacent dataset, you’ll need to be creative about feature engineering, supplemental data, and business implications.

You’ll naturally become a better creative thinker as you gain more experience, but the following resources can help jumpstart your problem-solving and innovation skills.

• Machine Intelligence and Data Products (Video) – Future-looking discussion of data products and data science.
• Machine Intelligence Landscape (Chart) – Venture capitalist’s perspective on the landscape of machine intelligence applications.
• The art of innovation (TED Talk)  – Great TED talk on innovation by Guy Kawasaki.
• 7 steps of creative thinking (TED Talk)  – Creative thinking tips from the perspective of a serial artist and entrepreneur.
• Working backwards to solve a problem (TED Talk)  – Chess grand-master Maurice Ashley on how to see the endgame and work backwards.

3.3. Business Operations and Strategy

Here’s a question you should ask yourself every day: “What are some ways I can improve this business?”

At the end of the day, companies don’t hire you to analyze data… they hire you to help them grow or become more profitable. This means that you should have an understand how data can help make better decisions and build better products.

• Data Driven Decisions (Video)  – How to take business objectives, extract testable hypotheses from them, and then design experiments to evaluate.
• Making Data-driven Decisions in Business – Training series from Google about using analytics to make better decisions.
• Big Data: New Tricks for Econometrics by Hal Varian (PDF)  – Hal Varian, Chief Economist at Google, gives an excellent overview of the technology and methodology landscape for data analysis.
• How data will transform business (TED Talk)  – Thought-provoking discussion of the relationship between business strategy and technology. Explains why the two long-standing theories of business strategy have become invalidated by the rise of big data.
• Victor Cheng’s Case Interview Workshop (Video Series)  – Some employers like to ask consulting-style “case” questions during the interview. This is more common for Data Scientists in business operations, strategy, or analytics roles. This is an excellent crash course on tackling case interviews.

3.4. Business Analytics

Business analytic skills are critical for data scientists in operational roles. Python and R will allow you to perform more complex analyses than Excel can, thanks to the flexibility of programming languages.

After you master the technical tools, building strong domain knowledge will lead to greater business impact.

• Introduction to Business Analytics (Video) – Short and sweet intro to how businesses use analytics, including case studies.
• Digital Marketing Metrics & KPI’s Explained (Video) – Introduction to common metrics and analytics methods using in digital marketing.
• Effective Cross-Selling using Market Basket Analysis (Tutorial) – How to do smarter cross-selling.
• An Intuitive Guide to A/B Testing (Video) – Overview of A/B testing and interpretation.
• 25 Examples of Business KPIs (Examples) – “What gets measured gets managed.” Here are 25 examples of business Key Performance Indicators (KPIs).
• Google Analytics Academy (Courses) – Practical courses on digital analytics, e-commerce analytics, and other topics.

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4. Supplementary Skills

Supplementary Skills are more situational depending on the role, but they help you become a well-rounded data scientist. Here are data science resources for NLP, recommender systems, and time series analysis.

4.2. Natural Language Processing (NLP)

Natural Language Processing (NLP), or Text Mining, is an exciting sub-field of machine learning for extracting structure, grammar, and insights from text.

Famous applications include Sentiment Analysis, Article Classification, and even teaching a Neural Network to write Shakespeare .

• Stanford NLP (Video Series) – Full course on “traditional” Natural Language Processing, including sentiment analysis, Naive Bayes models, n-grams, etc.
• CS224N: NLP with Deep Learning (Course) , (Course materials here) – Introduction to the theory behind deep learning for NLP.
• Python NLP Libraries – Our overview of Python libraries for NLP. Once you have basic programming skills and a solid understanding of applied machine learning, you can actually jump straight here.

4.3. Recommendation Systems

Recommendation Systems, or Collaborative Filters, are one of the great success stories of data science, especially in e-Commerce. No list of data science resources would be complete without touching on them.

They power many amazing websites and apps, including Amazon, Yelp, Netflix, and Spotify. In a nutshell, recommendation systems find other users who have similar tastes to you to make better recommendations for you. This produces a huge win-win by improving user experience while driving up revenue.

• Recommendation engine tutorial (Video)  – Introduction to collaborative filters using Python. Does a very nice job of explaining the intuition behind the algorithm.
• Recommender Systems (Video Series) – Discussion of the theory and math behind collaborative filters by Andrew Ng. More math-heavy, and it’ll be easier to follow if you have some background with Linear Algebra.
• Recommender Systems in Python 101 – Reference tutorial that implements an article recommender system in Python.
• Collaborative Filtering with R (Tutorial) – Reference tutorial that implements a book recommender system in R.

4.3. Time Series Analysis

Time Series Analysis deals with data series that are indexed by time. For example, stock prices, precipitation amounts, and Twitter hashtags by hour would all be considered time series. Time series analysis is commonly used in Finance, Forecasting, and Econometrics.

While much of machine learning deals with “cross-sectional data” (data without regard to differences in time), there are also models specifically designed to handle time series.

• Time Series (Course Material) – Lecture slides, homework, and R Code for the Time Series course at Oregon State University.
• The Little Book of R for Time Series (Online Book) – Very practical step-by-step introduction to using R for time series analysis. Includes code and outputs for each step.
• Time Series Forecasting with Python (Tutorial) – Tutorial on performing time series visualization, analysis, and forecasting with Python.
• Seasonal ARIMA with Python (Tutorial) – Introduction to ARIMA models in Python. Includes all code.
• Statistical forecasting, Fuqua School of Business (Online Book) – Course notes from the statistical forecasting course taught at the Fuqua School of Business at Duke University.

[images style=”0″ image=”https%3A%2F%2Felitedatascience.com%2Fwp-content%2Fuploads%2F2017%2F05%2Fdata-science-diamond-practice.png” width=”960″ align=”center” top_margin=”0″ alt_text=”Data%20Science%20Projects” full_width=”Y”]

5. Practice

Practice projects have two main purposes:

• They help you solidify concepts and practice pulling together all the moving pieces of data science.
• They arm you with something tangible to show employers.  If a picture is worth 1000 words, a project is worth a million…

By nature, projects are personal undertakings, and you should pick topics you’re interested in. Here are a few places to find project ideas:

• 8 Fun Machine Learning Projects for Beginners – Our list of 8 fun machine learning project ideas for beginners.
• Predict Titanic Survival (Kaggle Competition)  – Kaggle is a site that hosts data science competitions, many of which are beginner-friendly. The Titanic Survival Prediction challenge is a classic, with detailed tutorials for both Python and R.
• Hacker Rank (Programming Challenges) – Short programming challenges that are good for sharpening your skills without committing to a longer project.

And that’s a wrap!

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This module is optimized to help you develop a strong foundation for a data science career. It dives deep into the core principles of probability, statistics, and mathematics necessary for building machine and deep learning models further in the program. You will start working with data and learn how to visually present the results of your analyses.

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Here you will focus on developing a versatile programming skillset. You will acquire a thorough functional understanding of relational databases, using essential SQL queries to preprocess data, as well as coding and leveraging popular libraries in Python, like NumPy and matplotlib. You will also learn the best ways to manipulate and visualize data in R.

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Advanced Specialization

By now, you will have developed a solid understanding of Python programming and statistical modeling. The concluding module gives you the opportunity to mold your data science expertise according to a field of your choosing. From developing next-gen fintech products to helping retail giants boost profitability through customer analytics, you’ll be able to make a valuable contribution to a diverse industry spectrum.

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Table of Contents

Best Websites to Learn Data Science

Curious about how to dive into the world of data science without the hefty price tag?

Explore the best websites to learn Data Science for free, where you can unlock the secrets of big data, machine learning, and analytics without spending anything.

10 Best Websites for Data Science – Overview

Here’s an overview of the top 10 websites to learn Data Science:

Best Websites to Learn Data Science for Beginners

Below is the list of best websites to learn Data Science for beginners:

GUVI’s Data Science course, offered in collaboration with IIT-Madras, is a comprehensive program designed to equip learners with advanced programming skills and data science mastery.

The course covers a wide range of topics, including computational thinking, Python programming, machine learning, data visualization with tools like Tableau and Power BI, and natural language processing (NLP).

The course format includes live online classes, lifetime access to recorded videos, and hands-on experience with real-time projects. It also provides one-on-one mentorship sessions and “Ask-me-Anything” sessions with industry experts. The program is structured to be completed in 3 to 5 months, depending on the chosen schedule, and offers 100% job placement support with guaranteed interviews.

Course Diversity: Covers advanced programming, machine learning, data visualization, and NLP.

Learning Style: Live online classes with real-time projects and mentorship.

Pricing Structure: Course fee with EMI options available.

Platform Usability: User-friendly online platform with live and recorded sessions.

Certifications Offered: IIT-M Certification for Advanced Programming Professional.

Language Options: Available in English, Hindi, and Tamil.

Instructor Expertise: Taught by industry experts and IIT faculty.

Duration of Courses: 3 to 5 months, depending on the chosen schedule.

Community and Support: Dedicated support and community interaction.

2. Coursera

The “Introduction to Data Science” specialization on Coursera, offered by IBM, is a foundational program designed to equip learners with essential data science skills.

It includes four courses that cover a broad range of topics, including data science methodology, tools for data science, databases, and SQL for data science with Python. The specialization provides a hands-on approach to learning, with common data science tools like JupyterLab, R Studio, GitHub, and Watson Studio. It also teaches the mindset and methodology to tackle different types of data science problems.

By the end of the specialization, learners will have a clear understanding of what data science and machine learning are, their applications, and the types of tasks performed by data scientists.

Course Diversity: Covers data science methodology, tools, databases, and SQL with Python.

Learning Style: Hands-on learning with common data science tools.

Pricing Structure: Accessible through Coursera subscription, financial aid available.

Platform Usability: User-friendly platform with flexible learning schedule.

Certifications Offered: Shareable certificate upon completion of the specialization.

Language Options: Content available in English with subtitles in 22 languages.

Instructor Expertise: Courses taught by experienced instructors from IBM.

Duration of Courses: Approximately 1 month at 10 hours a week.

Community and Support: Access to Coursera’s community of learners and developers.

The “Databases and SQL for Data Science with Python” course on edX, offered by IBM, is designed to equip data professionals like Data Scientists, Data Analysts, and Data Engineers with a working knowledge of SQL.

The course covers foundational SQL statements like SELECT, INSERT, UPDATE, and DELETE, and teaches how to filter result sets, use various clauses, and differentiate between Data Manipulation Language (DML) and Data Definition Language (DDL). It also includes advanced SQL techniques like views, transactions, stored procedures, and joins.

The course is structured to provide hands-on experience with real databases on the Cloud and uses data science tools like Jupyter notebooks with SQL and Python.

Course Diversity: Covers foundational to advanced SQL techniques and applications in data science.

Learning Style: Hands-on learning with real databases and data science tools.

Pricing Structure: Accessible through edX subscription, financial aid available.

Platform Usability: User-friendly platform with courses structured for easy navigation and progress tracking.

Certifications Offered: Shareable certificate available upon completion.

Language Options: Content available in English with subtitles in 20 languages.

Duration of Courses: Approximately 20 hours, self-paced learning.

Community and Support: Access to edX’s community of learners and developers.

The “Data Science Tutorial for Beginners” on Kaggle, created by DATAI, is an insightful and comprehensive notebook designed for individuals new to data science. This tutorial uses a dataset from “Pokemon- Weedle’s Cave” and focuses on beginner-level data visualization and exploratory data analysis using Python.

The notebook guides learners through various steps of data analysis, including data cleaning, manipulation, and visualization. It employs popular Python libraries such as Pandas, Matplotlib, and Seaborn to demonstrate practical data science techniques.

The tutorial is interactive, allowing users to run code and see results in real-time, making it an excellent resource for hands-on learning.

Course Diversity: Focuses on data visualization and exploratory data analysis for beginners.

Learning Style: Interactive notebook with hands-on Python coding.

Pricing Structure: Free access to the tutorial on Kaggle.

Platform Usability: User-friendly and interactive, ideal for learning by doing.

Certifications Offered: Does not offer certifications.

Language Options: Content primarily in English.

Instructor Expertise: Created by DATAI, known for quality data science tutorials.

Duration of Courses: Self-paced, interactive learning experience.

Community and Support: Active community with over 1200 comments for discussion and support.

5. DataCamp

DataCamp’s “Data Scientist with Python” track is a comprehensive learning path designed for individuals aspiring to become data scientists.

It includes 23 courses and 11 projects, totaling approximately 90 hours of content. The curriculum covers a wide range of topics, from Python essentials for data science to advanced machine learning techniques.

Learners will gain hands-on experience with popular Python libraries for data science, including pandas, Seaborn, Matplotlib, and scikit-learn. As they progress, they will work with real-world datasets to learn statistical and machine learning techniques, perform hypothesis testing, and build predictive models.

The track also provides an introduction to supervised learning with scikit-learn and allows learners to apply their skills to various projects.

Course Diversity: Covers Python essentials, machine learning, and data analysis.

Learning Style: Hands-on learning with real-world coding exercises and projects.

Pricing Structure: Accessible through DataCamp subscription.

Platform Usability: Interactive and user-friendly interface.

Certifications Offered: Statement of Accomplishment upon completion.

Language Options: Primarily in English.

Instructor Expertise: Courses taught by industry professionals and experienced educators.

Duration of Courses: 90 hours across 23 courses and 11 projects.

Community and Support: Access to a large community of learners and professionals.

Udacity’s “Data Scientist Nanodegree” program is a comprehensive and advanced course designed to equip learners with the skills needed to become proficient data scientists.

It covers a wide range of topics, including data manipulation, data analysis with Python, machine learning, and software engineering practices for data science. The course is structured into several modules, each focusing on different aspects of data science, and includes real-world projects to provide hands-on experience.

The program is suitable for individuals with a background in Python data analysis libraries and basic statistical modeling.

Over four months, learners will gain expertise in scikit-learn, interpreting test results, designing smart experiments, and more. The program also offers personalized project reviews, mentorship, and a completion certificate.

Course Diversity: Covers data analysis, machine learning, and software engineering in data science.

Learning Style: Hands-on learning with real-world projects and mentorship.

Pricing Structure: Paid program with a month-to-month subscription model.

Platform Usability: User-friendly platform with structured learning paths.

Certifications Offered: Completion certificate provided.

Language Options: Content available in English.

Instructor Expertise: Courses taught by experienced data scientists and industry experts.

Duration of Courses: Approximately 4 months to complete.

Community and Support: Access to Udacity’s community and mentorship for learner success.

7. MIT OpenCourseWare

MIT’s “Introduction to Computational Thinking and Data Science” course, available on MIT OpenCourseWare, is a continuation of “6.0001 Introduction to Computer Science and Programming in Python.”

This course is designed for students with little or no programming experience and aims to provide a comprehensive understanding of the role computation plays in solving problems. Taught by Prof. Eric Grimson, Prof. John Guttag, and Dr. Ana Bell, the course uses Python 3.5 as the programming language.

It covers a range of topics in engineering, computer science, and mathematics, focusing on computational methods and data science principles. The course materials include lecture videos, slides, problem sets, and programming assignments, making it a valuable resource for undergraduate students.

Course Diversity: Covers computational thinking, data science, and programming in Python.

Learning Style: Includes lecture videos, problem sets, and programming assignments.

Pricing Structure: Free access to course materials.

Platform Usability: Well-organized and accessible for self-paced learning.

Instructor Expertise: Taught by experienced MIT faculty.

Duration of Courses: Varies, self-paced learning based on course materials.

Community and Support: Does not specify community or support options.

8. Codecademy

Codecademy’s “Data Science Foundations” is a comprehensive skill path designed for beginners to learn the basics of cleaning, analyzing, and visualizing data using Python and SQL.

This path includes Python 3, SQL, Pandas, Matplotlib, data visualization, and data cleaning. It’s structured to provide a foundational understanding of data science, teaching industry-standard languages and libraries.

The path comprises 15 units, 49 lessons, 34 projects, and 35 quizzes, totaling approximately 56 hours of content. Learners will gain skills in analyzing data with Python and statistics, reading and writing databases with SQL, creating meaningful data visualizations, and building a data science portfolio.

Course Diversity: Covers Python, SQL, data analysis, visualization, and cleaning.

Learning Style: Interactive, hands-on learning with projects and quizzes.

Pricing Structure: Included with Codecademy’s paid plans.

Platform Usability: User-friendly, with AI-assisted learning and a mobile IDE.

Certifications Offered: Certificate of completion available.

Instructor Expertise: Developed by experienced data professionals.

Duration of Courses: Approximately 56 hours to complete.

Community and Support: Access to Codecademy’s community and learning resources.

9. Topcoder

Topcoder Academy’s “Data Science Fundamentals” certification is a comprehensive program designed to provide learners with foundational knowledge and skills in data science.

This certification is suitable for beginners and covers a broad range of topics, including data visualization, Python programming, JavaScript, JSON, APIs, and relational database management systems.

The curriculum is divided into four courses, encompassing skills like D3.js for data visualization, SQL for database management, and NumPy for scientific computing. The program also delves into machine learning with Python, offering insights into TensorFlow and neural networks.

Course Diversity: Covers data visualization, Python programming, machine learning, and more.

Learning Style: Comprehensive online courses with a focus on practical skills.

Pricing Structure: Free enrollment for a limited time.

Platform Usability: User-friendly online platform for easy learning.

Certifications Offered: Data Science Fundamentals certification upon completion.

Language Options: Courses available in English.

Instructor Expertise: Courses developed by industry experts.

Duration of Courses: 340 to 670 hours of study.

10. LinkedIn Learning

“Data Science Foundations: Fundamentals” on LinkedIn Learning is an accessible, nontechnical course that provides an overview of data science. It’s designed to define the relationships to other data-saturated fields such as machine learning and artificial intelligence.

The course reviews primary practices in data science, including gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. It also discusses ethics and accountability in data science. This course is suitable for anyone looking to understand the basics of data science and its applications in various industries.

Course Diversity: Covers the basics of data science, including vocabulary, skills, and techniques.

Learning Style: Video lectures, readings, and quizzes.

Pricing Structure: Available through LinkedIn Learning subscription or individual course purchase.

Platform Usability: User-friendly interface with easy access to course materials.

Instructor Expertise: Taught by Barton Poulson, a data science expert and educator.

Duration of Courses: 5 hours and 17 minutes.

Community and Support: Access to LinkedIn Learning’s resources and community.

Frequently Asked Questions

1. what are the best websites for learning data science.

The best websites for learning Data science are GUVI, Coursera, edX, Kaggle, DataCamp, and MIT OpenCourseWare.

2. What are some free data science learning websites along with certifications?

Kaggle, MIT OpenCourseWare and Topcoder are some free data science learning websites along with certifications.

3. Why should I choose a website for learning Data Science?

You should choose a website for learning Data Science because they offer flexibility and have a variety of learning resources. They cater to different learning styles with interactive tutorials, video lectures, and hands-on exercises.

4. How do I choose the right website for learning Data Science?

You can choose the right website for learning Data Science by considering factors like course content quality, learning style compatibility (videos, interactive exercises), instructor expertise, community support, and pricing.

5. Can a beginner learn Data Science effectively through websites?

Yes, beginners can effectively learn Data Science through websites. Many platforms offer beginner-friendly courses that start with basics and gradually progress to more complex topics.

6. Are there websites that offer content in multiple languages for learning Data Science?

Yes, some websites like GUVI, Coursera and edX provide Data Science learning content in multiple languages and subtitles.

Final Words

These websites are perfect for anyone looking to break into the field of data science, offering a wealth of knowledge from basic concepts to advanced techniques.

Keep checking this article as we will keep updating this space as more websites make space in the heart and study schedule of students preparing for placements and competitive exams.

Explore More Data Science Resources

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Thirumoorthy

Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education

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Top 10 Data Science Websites to learn More

Home Blog Data Science Top 10 Data Science Websites to learn More

Being a data scientist means constantly growing, enabling businesses to become more data-propelled, and learning newer trends and tools. There are various excellent resources in data science that can help you to develop your skillset. According to International Data Corporation (IDC), organizations are turning towards digitalization completely. This will help to create more investments, technology development and open various new jobs.

Currently, numerous resources are being created on the internet consisting of data science websites, data analytics websites, data science portfolio websites, data scientist portfolio websites and so on. So, having the right knowledge of tools and technology is important for handling such data. The easiest way to get started is by taking an online data science bootcamp program . Most aspirants either spend too much time in search of the right course or the right technology.

Steps to Learn and Master Data Science

Learning a language – python.

Choosing and learning a new programming language is not an easy thing, in terms of learning data science, Python comes out first. Python is a high-level, interpreted, general-purpose, object-oriented programming language. Python provides great functionality to deal with mathematics, statistics and scientific function. The best Website to learn Python: w3schools.com. 

Get to know more about data science for business . 

Learning Data Analysis in Excel

Data analysis is a process of inspecting, cleaning, transforming and modelling data with an objective of uncover the useful knowledge, results and supporting decision. Best Website for excel: excelexposure.com. 

Learning Data  Visualization

To discover hidden truth or information on business problem, data needs to be viewed properly. Appropriate data visualization tool selection is important, to know what to expect from data is also important. Various data visualizations are used in Exploratory data analysis (EDA). EDA is used by data scientists to analyze and explore data sets and summarize their properties, often using data visualization techniques . 

Best website for data visualization learning: geeksforgeeks.org 

Start learning Inferential Statistics and Hypothesis Testing

Exploratory data analysis helps you to know patterns and trends in the data using many methods and approaches. In data analysis, EDA performs an important role. Hence, data analyst utilizes most of their time doing EDA. Sometimes, due to time constraints and resource constraints you can handle large-size data. Time like this asks for sampling methods and approaches. Instead of using whole data in analysis, data analyst tends to find defect or abnormality in the sample. Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. This process of inferring the information from sample data is known as ‘inferential statistics.’ 

Hypothesis testing is a part of inferential statistics which uses data from a sample to analyze results about whole dataset or population. Hypothesis testing is done to know the null hypothesis can be rejected or fail to reject. If the null hypothesis is rejected, then the research hypothesis can be accepted. If the null hypothesis is accepted, then the research hypothesis is rejected. 

Learning inferential statistics website: wallstreetmojo.com, kdnuggets.com 

Learning Hypothesis testing website: stattrek.com 

Start learning database design and  SQL.

A database is a structured data collection that is stored and accessed electronically. File systems can store small datasets, while computer clusters or cloud storage keeps larger datasets. According to a database model, the organization of data is known as database design. The designer must decide and understand the data storage, and inter-relation of data elements. Considering this information database model is fitted with data. 

SQL stands for Structured Query Language. It is created for the recovery and control of data in a relational database. 

Database design basics with example: blog.devart.com 

SQL learning: w3schools.com 

Start Machine Learning

Machine learning is a part of artificial intelligence that concentrate on the utilization of data knowledge and algorithms to follow methods that human learns and moderately improves its accuracy. Machine learning has four key types as follows: 

• Supervised learning: In supervised learning, we introduce the algorithm’s product into the structure so that the system notices the patterns and trends before working on them. 
• Semi-Supervised learning: Semi-supervised learning utilizes an arrangement of a small quantity of labelled data and a large quantity of unlabelled data to develop models. 
• Unsupervised learning: The unsupervised learning technique is impressive for displaying correlation and understanding in unlabelled datasets. Models introduce input data with unspecified useful outcomes. 
• Reinforcement learning: The reinforcement learning technique in machine learning controls the best track or choice to select in condition to maximize the profit. Leading machine learning examples in daily life-like video games, utilize this approach. 

Machine learning website: machinelearningmastery.com 

You may also be interested in exploring  data science online training.  

Learn by Working on Projects

Working on hands-on projects gives you a real understanding and learning of the topic. Hence it is always good to work on the project. Implementation of various tools and methods to gain more knowledge on data, find insights and convert insights into useful decisions. Hands-on projects also teach collaboration, workflow of process and different experiences with the problem statement.

Data science project cycle is composed of six phases: 

• Business understanding 
• Data understanding
• Data preparation 
• Modelling 
• Evaluation 
• Deployment 

This is the greater abstraction level of the Crisp-DM methodology, meaning one that can apply, with no exception, to all data problems. Know more about Kaggle for data science . 

Website for Projects: Kaggle 

Working on live projects gives you understanding how things work in industry. Know more in KnowledgeHut data science bootcamp training . 

Top 10 Data Science Websites to Follow

Now, as we have little understanding of data science, we will have a look at following topics to know more about data science and newer developments in it. 

KDnuggets: It is one of the compelling and regularly updated sites for blogs on analytics, Data Science, Big Data and machine learning. It offers various blogs based on above mentioned technology in alphabetical order.

Datasets 

Amazon Datasets : All the dataset on Amazon is kept in AWS S3 which is an object storage service on the cloud platform. While using Amazon SageMaker datasets are quick to access and load.

Kaggle Datasets : It is an online community platform for data science enthusiasts. You can find the image dataset, time-series dataset, reviews, etc. All these datasets are totally free to download off Kaggle. 

Research Papers

Papers With Code: It is a great platform and free website for research papers on data science, machine learning, big data, etc. 

GitHub: It is a place to find detailed codes, architecture design. With GitHub, not only you can store your code but also use code from another user for your projects. Sharing your codes for everyone to access is also an integral part of GitHub. 

PyImageSearch: This is one of the best websites for computer vision projects. It also covers OpenCV and deep learning topics for computer vision projects. If you are a fan of computer vision projects and want to continue building more projects in this domain, it is highly recommended checking out the website for further study material, knowledge, and resources. 

YouTube : It is one of the best platforms to learn more about machine learning and Data science through videos. You can browse through various channels and binge watch some amazing videos that will inspire you and teach you practical knowledge and implementations in these fields. 

2 min papers: This YouTube channel explains newer technology research papers in easy context.

FreeCodeCamp: It is a YouTube channel for design, planning and implementation tutorials for projects in various languages.

Software 

TensorFlow : TensorFlow is go-o library for machine learning and artificial intelligence. It can be utilized across a range of problem but has a particular distinct on training and inference of deep neural networks. 

Keras : It is a library that helps with a python interface to learn, utilize artificial neural networks. Keres works as a configuration for the TensorFlow library.

Troubleshooting

Reddit , Quora: Resources mentioned in this point tend to serve a similar objective, i.e., interaction and involvement, and answers to several questions for clarifications. 

Publication medium

Towards Data Science: one of the biggest publications on Medium that is one of the best websites for the viewers to acknowledge thoughts, ideas, codes, and information related to Data Science, Machine Learning, Visualizations, computer vision, and so much more. It requires $5 a month, but still using various IDs you can access content for Free.

Code Snippets & explanations

Stack Overflow: Stack Overflow is a question-and-answer website for professional and enthusiast programmers. 

Stack Exchange : Stack Exchange is a network of question-and-answer websites on topics in diverse fields, each site covering a specific topic, where questions, answers, and users are subject to a reputation award process. 

This article covers various topics and their related websites for machine learning and data science. Every one of the resources mentioned here contains tons of content and valuable information that you can utilize for becoming better at the subject as well as benefitting from them by gaining further knowledge.

The resources for them are limitless, and in a future article, we will try to cover at least ten more such amazing and useful websites that can help you out on your Data Science journey! 

Frequently Asked Questions (FAQs)

Best platforms to learn data science for free are Kaggle and FreeCodeCamp. If you want assistance and instructor-led sessions follow  data science bootcamp program .

Yann LeCun is one of the top data scientists in the world currently working as Director of AI research in Meta. Also, there are many researchers who shown their brilliance in the field of data science like Dr DJ Patil, Chief data scientist at White House for Obama.

Yes, you can learn on you own or can join  data science online training .

Yes, you can, by learning to code smartly and applying learning to build many projects. 1 year should be sufficient to understand and learn the basics, provided you work in a regular fashion. After that you need to keep practicing. There is no end to learning, so you need to keep learning and improving.

There are many platforms who help you to practice coding for data science. First is Kaggle: It is an online community of data practitioners. You will get many notebooks, problems and one of the optimum solutions as well.

There are many platforms who offers data science learning. If you want industry level case study, instructor led sessions you can join  data science online training .

You can start by figuring out what you need to learn, then you can follow these steps. It is also important to understand machine learning in more depth with deep learning concepts to solve complex problems. Keep learning and Practicing.

Sushil Deore

A city dweller who loves to travel, interested in outdoor activity most on road trips also, A data science professional with a liking for deep learning.

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• Data Science

Complete Machine Learning & Data Science Program

Course description.

Unleash your inner data scientist with our Machine Learning & Data Science program. Explore a 360-degree learning experience designed for geeks who wish to get hands-on Data Science and ML. Mentored by industry experts; learn to apply DS methods and techniques, and acquire analytical skills. So Master the Art of Data Science Now!

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• 20+ Programming Tools & Libraries
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• Career Essential Soft Skills Program
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Hands-on, practical exercises designed to enhance your learning experience and reinforce the concepts covered in the course. These projects serve as crucial components in the learning journey, as they allow you to apply the knowledge and skills gained in real-world scenarios . Eg: Wikipedia Scraper, PubG Predictive Analysis, Spell Checker & many more.

Course Content

• Introducing Python - Python Basics, Operators, Loops, Functions, Strings, List, Tuples, Dictionary, Set, Object-oriented concepts(OOPs) and much more.  
• Data Toolkit -  Getting started with Files, Inventory Management System with Files, Inventory Management System with JSON, Mastering Numpy Arrays, Getting started with OS , Jupyter Notebook Setup, OS with Python, etc.
• Libraries -  Numpy, Pandas, Matplotlib, Streamlit, etc.
• Maths for Data Analysis:  Basic Probability for Data Science, Statistics, Probability Distribution, Inferential Statistics, and much more
• Maths for ML & AI: System of Linear Equation, Matrix, Vectors, and Calculus, etc
• Data Analysis with Python:  Getting started with Pandas, Data Preprocessing with Google Play store, Introduction to EDA, Data Cleaning, Data Visualization, Data Analysis Projects: Sugarcane Production, Black Friday Sales Data Analysis, Data Visualization on Heart Disease Dataset, GDP Analysis
• Excel: Exploring Data, Preparing Data, Analysing Data, Important Interview Questions Projects: Sales Data Analysis Using Covered Functions and Pivot Tables
• Tableau -  Introduction to Tableau, Understanding Parameters, Basic Plots, Fundamentals of Tableau, Designing the Plots, etc Projects : Superstore Sales Analysis Dashboard, Covid-19 World Dashboard  
• Web Scraping -  Learn how to Scrape, Selenium, Image Dataset Creation, and much more Projects:  Wikipedia Scraper, Youtube Scrapper, Stock Images Infinite Scroll
• SQL - Databases Fundamentals, SQL Fundamentals, Data Manipulation(DML), Querying, Intermediate SQL Queries, Joining and combining Data, Set Theory Clauses, Subqueries, Window Functions, Data Preprocessing and analysis Projects:  Sales and Revenue Analysis, Customer Segmentation and Profiling, Social Media Sentiment Analysis
• Streamlit - Getting started with Streamlit, Page Beautification, Working with Data, Introduction to Data Visualization, Deployment
• Introduction to AI, How Data Science Comes into Play, 
• Linear Regression, Multiple Linear Regression & Polynomial Linear Regression,
• Support Vector Machines, Decision Trees, Random Forests,
• Classification Algorithms, Clustering Algorithms, Feature Engineering, and much more
• MNIST Handwritten Digit Recogniser, Titanic Survival | EDA,
• PubG Predictive Analysis, Human Activity Recognition using Smartphone data,
• Predicting solar Irradiance

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CS50's Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

Associated Schools

Harvard School of Engineering and Applied Sciences

What you'll learn.

Graph search algorithms

Reinforcement learning

Machine learning

Artificial intelligence principles

How to design intelligent systems

How to use AI in Python programs

Course description

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

Instructors

David J. Malan

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Using Python for Research

Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.

CS50: Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

CS50's Understanding Technology

This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.

More From Forbes

3 high-income skills that pay $100,000+ in 2024.

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Some high-income skills are extremely specialized and require top 2% expertise, yielding a ... [+] six-figure income

High-income skills are the crème de la crème.

They lie on the upside of upskilling, and tend to be the skills that everyone has a crazy gold rush for, in part because much of the workforce is in dire need of fresh, up-to-date skills, and in part because they pay so well (obviously).

They are in popular demand, command the highest levels of respect and admiration in the world of white-collar jobs, and when wielded well, form a powerful toolkit in the arsenal of an ambitious career-minded professional.

But even in the world of high-income skills, there are...err let's say, exceptionally high-income skills.

These are the ones that enable professionals such as consultants and tech developers to make big money, going into six figures within a few years after graduating or starting their careers. These are also the skills that freelance websites and platforms such as Toptal and A-Team are known for, who attract large corporate clients for their skill sets.

Although you can essentially make significant money with any skill—if there is strong demand coupled with your high expertise—these three skills in particular stand out and have a bright and promising future for the next five or more years, according to research and statistics from sources such as the U.S. Bureau of Labor Statistics, and Salary.com.

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The high salary potential—or the potential to make money through them as a full-time freelancer or as one of your side hustles—combined with their boundless potential for career progression and the ease of accessibility that is available through online certifications and courses, makes these three skills in particular, worth the effort to obtain and develop:

1. Software Development

This is probably going to be an obvious one for most readers. Software development is a skill that perhaps, is needed now more than ever before, in the age of the AI revolution and the speed with which new start-ups are launching. Highly skilled software developers—or any highly skilled professional in the tech industry for that matter—is a rare find.

Therefore, it is essential to not remain satisfied with basic knowledge and skills. You should always be improving, attending training and online courses to sharpen your skill set and improve your specialized expertise, especially with programming languages and technologies such as Python, Java, JavaScript, and cloud computing platforms. You may observe that the industries that pay the highest (and have high demand) for software developers include technology, finance, healthcare, and e-commerce.

Potential Career Paths (Freelancing Included)

• Software engineer/developer
• Web developer
• Mobile app developer
• DevOps engineer
• Full-stack developer

Average salary range: Between $114,013 to $135,979

2. Data Science/Data Analytics

Thanks to AI, data science is another in-demand skill to consider developing if you have an interest in technology and data. Businesses are always looking to improve their process of making data-driven decisions, and they also need your help so they can integrate and implement artificial intelligence tools, which are hungry for large volumes of data. If you possess advanced skills in data mining, machine learning, statistical analysis, and data visualization, you have an increased chance of securing a lucrative salary, or commanding higher rates for your work as a freelancer.

• Data scientist
• Data analyst
• Business intelligence (BI) analyst
• Machine learning engineer
• Data engineer

Average salary range: Between $108665 to $133348

3. Cybersecurity

Of course, with handling large volumes of confidential data, comes the added weight of ensuring that none of that data leaks and is visible to unauthorized persons. Organizations carry the heavy obligation of ensuring compliance with GDPR (in Europe) and privacy regulations worldwide. Otherwise, it will seriously injure their reputation, trust with stakeholders and customers, and their finances (due to hefty fines). This makes cybersecurity a high-income, in-demand skill to learn in 2024.

• Cybersecurity analyst
• Ethical hacker/penetration tester
• Security architect
• Security consultant
• Chief Information Security Officer (CISO)

Average salary range: Between $119,537 to $148,298

High-income skills can equally be useful for a traditional job setting, or if you're working as a ... [+] freelancer

Don't you think developing these lucrative skills, or fine-tuning and updating your knowledge if you already possess them, is a good idea?

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[1] Microsoft Internal Data, October 2023. Results may vary for each advertiser based on campaign settings, targeted audiences, and other factors. Lift metrics represent an average of all advertisers with completed lift studies to date. These advertisers may be a non-representative sample of all advertisers on the marketplace. Lift measured between exposed users and a control group of eligible unexposed users, with lift represented on a per-user basis. Average user count per advertiser study = 1.6M. 1. Compared to users who were not exposed.

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• About Geography and Geospatial Science Working Group (GeoSWG)
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• GeoSWG membership is open to all CDC/ATSDR staff and interested persons working at CDC/ATSDR facilities, including contractors.
• The group supports those who work with spatially-referenced data or have an interest in learning more about the relationship between geography and health.

The Geography and Geospatial Science Working Group (GeoSWG) is a CDC/ATSDR science-based organization of geographers, geospatial scientists, epidemiologists, statisticians, social and behavioral scientists, and others. These professionals work with spatially-referenced data or have an interest in learning more about the relationship between geography and health.

GeoSWG promotes, supports, and ensures excellence in use of geography and geospatial science research within public health applications at CDC/ATSDR. GeoSWG also fosters communication, collaboration, and partnerships among geospatial scientists and organizations within and outside of CDC/ATSDR. The group also supports recruitment, retention, professional development, and advancement of geographers and geospatial scientists at CDC/ATSDR.

GeoSWG membership is open to all CDC/ATSDR staff and interested persons working at CDC/ATSDR facilities, including contractors. GeoSWG organizes forums and educational activities related to the application of geospatial science in public health.

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If you’re interested in discussing scientific and technical research, particularly for public health-related topics, using Geographic Information Systems (GIS) and Geospatial Science, consider joining the ATL-GIS listserv. This electronic mailing list is managed by GeoSWG. This listserv informs GIS users, both inside and outside of CDC, about pertinent data, training opportunities, scientific discussions, and more.

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