Day by day, data science is becoming popular. That’s why everyone is trying to get into the world of data science. I will share the best online tools for learning data science in this post, including online courses, online tutorials, YouTube channels, blogs on data science, eBooks, etc. I would suggest you bookmark this article because you would certainly be supported in your learning process by this article.

Online Courses for understanding the science of data

1. Crash Course in Data Science, John Hopkins University (Coursera)

This course provides an introduction to the technical side of data science, but for those who need to handle data scientists or data science jobs, it is particularly aimed at understanding the “big picture”.

2. Essentials of Data Science and Machine Learning- Microsoft (EdX)

With a combination of practical and theoretical knowledge, this course, targeted at those looking to boost their career opportunities, guides you through key concepts and terminology, statistical techniques such as regression, clustering, and classification, and the practical steps required to construct and evaluate models.

As it is a Microsoft course, the cloud-based components concentrate on the Azure system of the business, but in organizations that are connected to competing cloud systems such as AWS, the principles that are taught are equally relevant. This requires a simple understanding of R or Python, the two most commonly used data science programming languages, so it might be beneficial to look at one of the courses covering data science.

Technology of Data – Harvard

All of Harvard’s data science course class materials and lectures are made freely accessible online, so they can be learned at your own pace. You may not end up graduating from one of the most prestigious universities in the world, but the course is rigorous and technical enough to make you an expert by the end of the day. The course is part of a degree in data science and is intended for students who have prior knowledge of key fields such as programming, maths, and statistics, or are also learning. However, if you are sufficiently committed, there are enough free tools out there on those topics to make this a viable choice for those outsides of academia.

Using Python – Rakesh Gopalakrishnanan Introduction to Data Science (Udemy)

This is one of the highest-rated introductory courses on data science and coding in Python by Udemy. As it starts right from the basics, it doesn’t need any prior knowledge or experience. Unlike some other very entry-level courses, however, it progresses to some actual practical instruction in Python and, especially usefully, its Sci-Kit Learn system, a very common tool for data exploration and mining at the academic and enterprise level.

Best Books for Data Science

Learning From Data (Introductory Machine Learning) (PRICE: FREE)

The introductory course for machine learning involves theories, algorithms, and applications. The emphasis is on actual comprehension, not just “knowing.” It is offered by the Technology Institute of California (CALTEC).

The Elements of Statistical Learning (PRICE: FREE)

This book covers everything from linear techniques to neural networks, random forests, and boosting.

My complete guide to Machine Learning

This is my book, which covers all about regression and basic ML techniques. Get this one as a paperback to support me as a creator, and for the website:

Deep Learning (An MIT Press book)

The Deep Learning textbook is a resource designed to help students and practitioners in general and deep learning, in particular, enter the field of machine learning.

Some other notable resources:

Data Science Tutorial– Great Learning

Data Science Full Course– Edureka

Data Science Full Course For Beginners– codebasics

Data Science Full Course– Simplilearn

Learn Data Science Tutorial– freeCodeCamp

R Programming Tutorial– freeCodeCamp

Statistics for Data Science– Great Learning

Statistics – A Full University Course on Data Science Basics–freeCodeCamp

Statistics Course for Data Science | Statistics Course– MarinStatsLectures-R Programming & Statistics

Mathematics for Machine Learning [Full Course]– Edureka

Machine Learning Data Pre-processing & Data Wrangling using Python– The AI University

Data Visualization Tutorial– by Krish Naik


To begin working with big data, analytics, and artificial intelligence, you don’t have to invest a lot and research for years. Demand for “armchair data scientists” is expected to outstrip demand in the coming years for typically trained data scientists without formal training in the subject but with the expertise and experience to analyze data in their daily work.

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