Curated list of Resources for Getting Started in Data Science and Deep Learning in 2019
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 median 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
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:
There are so many paid as well as free courses/MOOCs for getting started with Data Science, due to which choosing one becomes difficult.
- Data Lit
March 2019 Update: The AI Wizard Siraj Raval just launched his new course called ‘Data Lit’. It contains all the things that you need to be a Data Scientist such as SQL, Statistics, Getting started with Kaggle etc.
Teacher firstname.lastname@example.org Categories Data Lit Review (6 reviews) Overview Overview Course Objective Data Lit is is a…www.theschool.ai
Try the Data Lit course, it’s free and it has its own Slack community, which will help you if you get stuck at any phase of the Data Lit course.
2. Applied Data Science with Python
The Second best resource for getting started in Data Science is “Applied Data Science with Python” by Coursera.
The 5 courses in this University of Michigan specialization introduce learners to data science through the python…www.coursera.org
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.
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
fast.ai's practical machine learning MOOC for coders. Learn Random Forests, Logistic Regression, Gradient Descent, NLP, recommendation systems…course.fast.ai/
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.
- Deep Learning Specialization
The specialization is taught by the great Andrew Ng.
Deep Learning from deeplearning.ai. If you want to break into AI, this Specialization will help you do so. Deep…www.coursera.org
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:
fast.ai's practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems…course.fast.ai
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
Learn the basics of deep learning and implement your own deep neural networks with PyTorchwww.udacity.com
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 is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract…hackernoon.com
I'm Siraj. I'm on a warpath to inspire and educate developers to build Artificial Intelligence. Games, music, chatbots…www.youtube.com
Videos exploring research topics in artificial intelligence, deep learning, autonomous vehicles, and beyond.www.youtube.com
3blue1brown, by Grant Sanderson, is some combination of math and entertainment, depending on your disposition. The goal…www.youtube.com
Hello, world! Nice to meet you all. I post videos on fitness, nutrition, technology and whatever other experiments I'm…www.youtube.com
Latest News, Info and Tutorials on Artificial Intelligence, Machine Learning, Deep Learning, Big Data and what it means…becominghuman.ai
Understand how machine learning and artificial intelligence will change your work & life.machinelearnings.co
Highlights from Machine Learning Research, Projects and Learning Materials. From and For ML Scientists, Engineers an…medium.com
Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science…medium.com
I am a data scientist and machine learning engineer with a decade of experience applying statistical learning…chrisalbon.com
Cheatsheets by Favio Vázquez:
Again, these are the recommendations. You can use any resources to get started in this field. And don’t just 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 how the tools and libraries works.