Beginners Guide for Data Science

Beginner Guide to Data Science

Curated list of Resources for Getting Started in Data Science and Deep Learning in 2019Photo by Henri L. on Unsplash

Data Science is being adopted by almost all the companies right now whether it is a machinery business or automobiles.

According to Glassdoor, Data Scientist is best job in America in 2018 with a medium base salary of $110,000. However, there is also a huge skill gap for Data Science.

Becoming a Data Scientist is not that hard, if given right amount time and efforts while learning. However, I often find people trying out different courses, resources but still aren’t able to learn

Photo by Tim Gouw on Unsplash

I have been doing Data Science from 2 years, and I tried several courses/resources, so here’s a list of recommendation for Getting Started in:

Data Science

There are so many paid as well as free courses/MOOCs for getting started with Data Science, due to which choosing one becomes difficult.

The best resource for getting started in Data Science is “Applied Data Science with Python” by Coursera.

Applied Data Science with Python | Coursera

This a specialization which contains 4 courses which starts with Python Basics, then learning Statistics required for Data Science.

Then it covers various Visualization techniques using libraries like matplotlib etc in Python, Fundamentals of Machine Learning, and in Final course it cover basics of Natural Language Processing.

This specialization is free to access, if you choose the ‘Audit this course’. However, for getting certificate you would have to apply for Financial Aid or pay $50 subscription fee to coursera.

Machine Learning

The ‘Applied Data Science with Python’ from coursera covers Machine Learning in the specialization, however if you want to deep dive into Machine Learning algorithms and the mathematics behind it, there’s another great free resource called as fast.ai

Deep Learning For Coders-36 hours of lessons for free

This course is taught by a AI Researcher (Also, Ex-President Kaggle) Jeremy Howard and is a part of a Master of Science in Data Science Program from University of San Francisco.

Deep Learning

Also, with Deep Learning there are so many courses available which teach you to apply deep learning algorithms and get State of the Art results within few line of codes.

Applying these algorithms and getting results feels great, but one must know how they are working, instead of thinking of these algorithms as a black box.

1. Deep Learning Specialization

The specialization is taught by the great Andrew Ng.

Deep Learning | Coursera

This course is targeted towards beginner and just requires knowledge of Basic Python, Linear Algebra and Calculus.

The algorithms are taught from scratch and it’s a resource for getting started with Deep Learning.

There’s a great review of this course by Daniel Bourke on his YouTube channel, here’s the video if you’d like to see:

2. fast.ai

Deep Learning For Coders-36 hours of lessons for free

This is taught by Jeremy Howard.

This is the most rich and comprehensive course for Deep Learning. It covers all aspects of Algorithms.

This courses is taught with the help of fastai library which is a PyTorch wrapper, and it has a great community which will help you at every roadblock you face!

3. Intro to Deep Learning with PyTorch

Intro to Deep Learning with PyTorch | Udacity

Recently, PyTorch 1.0 stable was released by Facebook, and it has ability to fully utilize your gpu power for training models since it works on a basic data structure Tensors.

Udacity partnered with Factbook launched the free Deep Learning course. They start from basics of Neural Network and then goes to implementing various deep learning algorithms using PyTorch.

Miscelleneous Data Science Resources:

Apart doing MOOCs you can always stay updated with latest trends using following links:

Data Science in Tech - Hacker Noon

Youtube:

• Siraj Raval
• Lex Fridman
• 3Blue1Brown
• Daniel Bourke

Publications:

• Towards Data Science
• Becoming Human: Artificial Intelligence Magazine
• Machine Learnings

Medium Publications

• ML Review
• Analytics Vidhya
• Applied Data Science

Notes:

Chris Albon

Cheatsheets by Favio Vázquez:

FavioVazquez/ds-cheatsheets

Again, these are the recommendations. You can use any resources to get started in this field. And don’t complete these MOOCs, develop some real world project to test your skills by using the datasets available on Kaggle or by creating your own , because implementing them will give you an idea of the tools and libraries.

Happy Learning!

Data Science in Tech - Hacker Noon

Beginners Guide for Data Science was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.