Data Science Learning Guide for Beginners- Best Websites to Learn Data Science

Here are the best websites to learn Data Science for beginners. You don’t even need to have a background in math to get started since we’ve got you covered!

“Get the fundamentals down, and the level of everything you do will rise.”

            -Michael Jordan

Note: Please keep in mind that these are our top recommended resources and although there are others, these are beginner-friendly and welcome transitory and intermediate-level learners as well.

Fundamentals – you love them or hate them. But we can all agree on one thing – they’re critical to learning and success.

If you’re a beginner data scientist with no prior experience, then we’ve got you a 2018 treat.

Websites for Learning Statistics and Math Foundations

From covering the basics of Math and Science to diving into college-level Calculus and Statistics, these websites will have covered the building blocks of Data Science education.

Khan Academy

Khan Academy is an invaluable resource that provides personalized education and resources for self-learning Math and Science essentials. If you’ve skipped out on your high school Maths and need a foundational course that walks you through every module, then Khan Academy is perfect. Tests and recap exercises reinforce concepts and help you improve retention rates for lessons learned. There’s also a community forum where you can ask questions and interact with teachers and peers online.

OpenIntro

OpenIntro includes lessons on introductory statistics with randomization and simulation and walks you through the basics to advanced statistics concepts. There are hands-on modules for learning statistics with R and SAS and they even provide video and PDF textbook resources for learning.

Exam Solutions

Exam Solutions is a website that’s designed to help students tackle the Cambridge International Exams (CIE) and Edexcel tests but don’t be deceived by its title. The Statistics section is carefully drafted, and perfect for beginners. Multiple ways of solving the same problem are demonstrated, and there are walkthrough videos for solving exam papers too. If you want a go-to site for a thorough revision of what you’ve picked up so far, give this one a try.

Websites for Learning Programming

Learning to code is fun and is filled with a world of interesting challenges. Try out these websites and share your code on forums for community feedback and critiques.

FreeCodeCamp

Learning the basics of Python and front-end web development is essential since you’ll be working on the web which makes FreeCodeCamp the perfect website for all your programming needs.

What makes FreeCodeCamp stand apart from the rest is its UI and open-source coding repos. Its community is active, and there are FCC groups in every country and city, so you’ll get a chance to meet up with your peers during Hackathons and regular coding meetups that are announced on their Facebook community groups.

Codecademy

Who hasn’t heard of Codecademy? Besides providing free resources and modules for self-learning, now they are even hosting mentorship programs which include live feedback and critiques on portfolios and projects by industry professionals.

CodeWars

If you’d love to learn how to code through play, then CodeWars is your game. You solve coding challenges known as ‘kata’ and go up the ‘kata’ ranks as you progress through their challenges. It’s basically learning gamified.

Udemy

If you’d love the option of paying for courses, then Udemy offers full-fledged beginner to advanced programming courses that take you from the very fundamentals to advanced concepts. The prices aren’t too high, and it’s like buying video courses online, except you get to message your mentor, ask questions, and participate in class project discussions.

Websites for Acquiring Data Sets and Structures for Analysis Projects

These websites aren’t exactly learning resources, but they are essential if you’re hunting for data sets for exploration and analysis. Every beginner data scientist starts off by making analysis projects like sentiment analysis, image analysis, and similar beginner-oriented projects. For that, you need a resource for acquiring data sets which is why these are where you go for your first time. Give them a try.

• Kaggle

  Participate in Data Science contests on Kaggle and showcase your projects online. It’s a great place to acquire sample data sets for analysis. Many companies even upload their own data sets here.

• FiveThirtyEight

  This is a fascinating website which focuses on opinion poll analysis projects, politics, economics, and sports blogs. You can grab their data sets on their GitHub repos.

• World

  As its name implies, it’s a website that offers free data sets and open-source resources for sharing data, findings, and visualizations. It even includes API documentation and integration toolkits for developing and deploying your own applications.

Websites for Data Analytics with R/Python

Let’s not forget the best data analytics sites which show you how to perform data modelling with R and Python. These even cover topics related to data collection, mining, and analytics tools like Tableau, Excel, SAS, and others.

R-Bloggers

R-Bloggers mostly covers lessons on how to use R with SQL servers.  Information related to industry events and latest software updates are regularly posted and updated on their website.

KDnuggets

KDnuggets includes a section on interview questions and has great introductory lessons to Data Visualizations and other informative blog posts. There are even Python stack tutorials and various learning resources related to R and Python.

Courses That Cover Data Science Essentials (Free/Certifications)

Most of these courses are free. However, some of them are certification courses and waive off their fee only when you opt out of their certification exams. On the bright side, these provide you with the comprehensive walkthrough you need to kickstart your Data Science career.

Andrew Ng’s Machine Learning Course on Coursera

If you’re an aspiring Data Scientist, then before you dive into Neural nets and deep learning, you must master the foundations of Machine Learning which is why Andrew Ng’s Machine Learning Coursera course is just what you need. The course emphasizes strongly on a clear demonstration of mathematical concepts which makes this a favorite amongst learners.

Coursera also offers excellent courses on Apache Hadoop, Spark and Natural Language Processing (NLP) and similar Data Analytics niches, which is why we highly recommend it.

EdX’s  Introduction To Probability – The Science of Uncertainty

Data science is about making predictions through the probabilistic modeling of unstructured data sets. This course is an excellent introductory premiere to concepts such as the laws of large numbers, exploring continuous random variables, and introduces random processes like the Poisson processes and Markov chains. You’ll learn inference methods and apply lessons learned to real-world projects. The only prerequisite for attendance is knowledge of college-level Calculus which you can pick up from Khan Academy.

Microsoft’s Data Science Course

Although it covers the foundations of Data Science, its projects revolve around Azure Machine Learning and Microsoft Product development. If you don’t mind that, go for it. It’s a three-month program, and it awards certification at the end of the course. It even covers SQL!

Udacity Courses

Udacity offers a plethora of Data Science and Machine Learning courses for beginners and intermediates. What makes their courses stand out is the fact that their explanations and demonstration of concepts related to decision trees, time series forecasting, and Random Forests are the best in the World Wide Web.  We recommend Udacity’s Intro to Inferential Statistics for learning Descriptive Statistics.

Tips to Make the Most of These Resources

Make sure you use tools like the Jupyter Notebook to post your analysis results and upload your code to GitHub repositories for community feedback and critique.

Try to replicate portfolio projects created by Data Science experts or key figures in the industry and look for interesting patterns and insights. Do not miss out on Hackathons or Data Science community events and stay in the loop regarding these matters since you’ll receive a lot of networking opportunities and get to meet prospective clients and employers.

The crucial thing is to constantly ask questions, figure out why certain things don’t work, and to make sure your portfolio adheres to what companies look for to land dream data science jobs you desire.

Conclusion

With the advent of online Data Science forums and communities, learning data science on your own has never been any easier.

But if you are serious about a career in Data Science and want to accelerate your learning, check out our Data Science Master Courses. By the way, we are offering some courses FREE of cost. 🙂

Liked our list of resources? Talk to us and feel free to comment your thoughts below!