I want to learn Data Science to Earn Name & Fame in Data Scientist Field. Where to start? What if I get stuck?

Data Science has been hailed as the transformative trend that is set to re-wire the industries and re-invent the ways people do things. Products and applications are being developed in agriculture, healthcare, urban planning, trade, commerce, finance, and the possibilities are growing.

I have been working on different data science and machine learning projects for quite some time now. A lot of friends have asked for basic introductory material to start learning Data Science. The internet has a plethora of resources and books available for the learner at every level. The main bottleneck is, "Where do I start?" and "What if I get stuck?".

Data Science is an umbrella term which is used to describe pretty much everything, from data engineering to data processing to data analysis to machine learning, pattern recognition, and deep learning. Don't worry, start with these resources and over time, you'll make your own map and find your territory in the land of Data Science.

I enjoy learning and then sharing what I learned about data science. When you teach a concept, that's when you learn it for real. Teaching is like learning 2.0. A couple of friends chipped in (Thanks Konik, Chirag, Dinesh, Ankur, Mohammad) and we plan to make data science education accessible to everyone who is interested in learning.

So, we prepared a list of resources. The pre-requisite knowledge is high-school mathematics. Go ahead, and start learning, and if you feel stuck, have questions, just send a short email to (datascience.evolve@gmail.com) and say Hi. One of us would be happy to get back to you, introduce ourselves, and explain stuff to you. This way, you don't get stuck and are well on your learning curve. Let's get the learning started.

Whitepapers

NITI AYOG National AI strategy discussion paper: This is a good place to start if you want to understand AI landscape, use-cases and aspirations of India.

Skills

SQL, Codecademy: SQL lets you interact with databases. Need to learn because you need to first extract data to do data science.
Python, Codecademy: 11 hours course to basic python literacy. Python is one of the most famous languages used to analyze data and train models.
Bash/ Unix: Tons of good resources online. Learn as you go.
Github: https://www.codecademy.com/learn/learn-git
Data Analysis with Python: Take your python skills on a test-drive and learn to manipulate data in python.
Web Development by Steve Huffman, co-founder of Reddit: Learning about web-app development helps you understand, what goes into making your product live

Courses

Machine Learning, Andrew Ng, Coursera: This has come to be "Hello World" of Machine Learning education. Go through this course first.
Neural Network: Youtube 3 Blue 1 Brown: Introductory video to understand neural networks.
Coursera Deep learning specialization Andrew Ng: Deep learning, in the pedagogy of Andrew Ng.

Books

Introduction to Statistical Learning: This book is a beauty. Some friends have suggested that this book clarified regression/classification algorithms like no other resource.
Linear Algebra and its Applications By Gilbert Strang: This is where it all starts.
Time series forecasting by Rob Hyndman
Elements of Statistical Learning: Sequel to Introduction to Statistical Learning, might be a little heavy, but worth the fun.
Deep Learning by Goodfellow et al

Interview guides

Acing AI interview guides: https://medium.com/acing-ai

Online tutorials/assignments/exercises

Data Science Masterclass: https://github.com/sourabhrohilla/ds-masterclass-hands-on/
Run python3 Jupiter notebooks on AWS

Other lists of resources (Just if you feel like being overwhelmed) :

Data Science masters: http://datasciencemasters.org/
Awesome Data Science: https://github.com/bulutyazilim/awesome-datascience
Awesome Machine Learning: https://github.com/josephmisiti/awesome-machine-learning
LeadingIndia.ai list of resources: https://leadingindia.ai/resources/

Source: HOB

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