Any developer event I attend or speak at nowadays, I saw a good number of hands raised in favor of learning Data Science, Machine Learning and related disciplines. I have seen a wide range of people wants to get started in the data science field but they often keep it just there because of not having a proper reference to resources for getting started. That is why I thought it would be beneficial for the community if I could present it with a list of resources which can help the developers get started quickly. Because making sense of data is just something a data scientist only ambition, it is something we all try to be good at. Isn't it?

Data science is not just about applying machine learning to your data and getting predictive statistics out of it. It involves a series of other things in the pipeline and together the pipeline is often referred to as CRISP-DM (cross-industry process for data mining). Before you feed the data to your favorite machine learning algorithm, you will have to give your data a proper shape for being able to apply machine learning. This requires wrangling the data which further includes cleaning it, preprocessing it and so on. But what if you do not have the data to proceed? In that case, you will have to collect the data from whatever resources available to you.

So you see, data science is more of a journey than just discovering patterns from the data. This article attempts to present you some resources (free and sometimes a little bit paid) which can help you get started with this journey in no time. The resources presented here are going to be of three types - Courses, Blogs, YouTube videos/channels. So, without further ado, let’s begin.

Note: I have tried to not make the list programming language specific and we assume you already have the knowledge of basic Python.

Courses:

As mentioned earlier, data science comprises many things and there are a lot of good resources on the internet covering each and one of them. But the problem here is the resources are scattered and for a beginner, it can be quite challenging to get hold of something that is suitable for him(s). So, it will be a good idea for a beginner to begin with something that is well-accepted by the community and something which has most of the things in place in a friendly way.

Data Science (including Statistics)

• Intro to Inferential Statistics by Udacity (free course)
• Intro to Descriptive Statistics by Udacity (free course)
• Statistics and Probability by Khan Academy (free course)
• Data Analyst with Python by DataCamp (free if offered via DataCamp Online Classroom)
• Data Scientist with Python by DataCamp (free if offered via DataCamp Online Classroom)
• Applied Data Science with Python Specialization by Coursera (free via Financial Aid)
• Practical Data Science by CMU (free course)
• Intro to Data Science by Udacity (free course)
• Intro to Data Analysis by Udacity (free course)
• Data Visualization by Kaggle Learn (free course)
• SQL by Kaggle Learn (free course)

Machine Learning and Deep Learning

• Machine Learning by Coursera (free via Financial Aid)
• Machine Learning Crash Course by Google (free course)
• Introduction to Machine Learning for Coders by Fast.ai (free course)
• Machine Learning by Kaggle Learn (free course)
• Intro to Machine Learning by Udacity (free course)
• Deep Learning Specialization by Coursera (free via Financial Aid)
• Practical Deep Learning for Coders by Fast.ai (free course)
• Intro to Deep Learning with PyTorch by Udacity (free course)
• Deep Learning by Kaggle Learn (free course)

Blogs:

Machine Learning Mastery - https://machinelearningmastery.com
Analytics Vidhya - https://analyticsvidhya.com
KDNuggets - https://www.kdnuggets.com
Towards Data Science - https://towardsdatascience.com
DataCamp Community - https://www.datacamp.com/community/tutorials
Data Science Central - https://www.datasciencecentral.com/
Christopher Olah’s blog - http://colah.github.io
Sebastian Ruder’s blog - http://ruder.io/
Sebastian Raschka’s blog - https://sebastianraschka.com/
Andrew Trask’s blog - https://iamtrask.github.io/

YouTube Channels:

Siraj Raval - https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
Luis Serrano - https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ
Brandon Rohrer - https://www.youtube.com/user/BrandonRohrer
Data School - https://www.youtube.com/user/dataschool
Khan Academy - https://www.youtube.com/user/khanacademy
Josh Starmer - https://www.youtube.com/user/joshstarmer

Bonus!

If you are someone who is more comfortable with books then you can refer to this compilation by FloydHub which does a pretty great job of enlisting all the books that are relevant for Data Science and Machine Learning. Some of them are freely available and some of them come with a little price.

Note that I did not include any course that covers how to incorporate software engineering practices or how to take machine learning models into production. It is something I will love to see you figuring out because once you have got started with the following courses, you will have a brief overview of where to find what.

I hope the resources mentioned above serve their purpose to the fullest and I sincerely hope they serve their purpose well.