The first half is more or less my learning path in the past two years while the second half is my plan for this year. I tried to make a balance between comprehension and doability. For more extensive lists, you can check Github search or CS video lectures

Hope the list is helpful, especially to whom are not in CS major but interested in data science!

• One Month Plan
• Practical Data Science
• Machine Learning
• Natural Language Processing
• Deep Learning
• Systems
• Analytics
• Reinforcement Learning
• Other Courses
• Interviews
• Bayesian
• Time series
• Quant
• More Lists

You may find the list overwhelming. Here is my suggestion if you want to have some basic understanding in one month:

• Learn Python the hard way: Free book
• Stanford Statistical Learning (Course page) or Coursera Stanford by Andrew Ng (Coursera, Youtube)
• Ng’s deep learning courses: Coursera
• Keras in 30 sec: Link

• Feature Engineering and Selection: A Practical Approach for Predictive Models: Link

• Stanford Statistical Learning: Course page
• Coursera Stanford by Andrew Ng: Coursera, Youtube
• Stanford 229: Youtube, Course page
• Machine Learning Foundations (機器學習基石): Coursera , Youtube
• Machine Learning Techniques (機器學習技法): Youtube
• CMU 701 by Tom Mitchell: Course page

• Introduction to Statistical Learning: pdf
• Computer Age Statistical Inference: Algorithms, Evidence, and Data Science: pdf
• The Elements of Statistical Learning: pdf

Statistical Learning is the introduction course. It is free to earn a certificate. It follows Introduction to Statistical Learning book closely. Coursera Stanford by Andrew Ng is another introduction course course and quite popular. Taking either of them is enough for most of data science positions. People want to go deeper can take 229 or 701 and read ESL book.

• Stanford - Basic NLP course on Coursera: Videos, Slides
• Stanford - CS224n Natural Language Processing with Deep Learning: Course web, Videos
• CMU - Neural Nets for NLP 2017: Course web, Videos
• University of Oxford and DeepMind - Deep Learning for Natural Language Processing: 2016-2017: Course web, Videos and slides
• Sequence Models by Andrew Ng on Coursera: Coursera

• Speech and Language Processing (3rd ed. draft): Book
• An Introduction to Information Retrieval: pdf
• Deep Learning (Some chapters or sections): Book
• A Primer on Neural Network Models for Natural Language Processing: Paper. Goldberg also published a new book this year
• NLP by Jacob Eisenstein. Free book draft

• NLTK: https://www.nltk.org/
• Standord packages: https://nlp.stanford.edu/software/

The basic NLP course by Stanford is the fundamental one. SLP 3ed follows this course. After this, feel free to take one of the three NLP+DL courses. They basically cover same topics. The Stanford one have HWs available online. CMU one follows Goldberg's book. Deepmind one is much shorter.

Some other people's collections: NLP, DL-NLP, Speech and NLP, Speech, RNN

• Ng’s deep learning courses: Coursera. This specialization is so popular. Prof. Ng covers all a lot of details and he is really a good teacher.
• Tensorflow. Stanford CS20SI: Youtube
• Stanford 231n: Convolutional Neural Networks for Visual Recognition (Spring 2017): Youtube, Couse page
• Stanford 224n: Natural Language Processing with Deep Learning (Winter 2017): Youtube, Course page
• The self-driving car is a really hot topic recently. Take a look at this short course to see how it works. MIT 6.S094: Deep Learning for Self-Driving Cars: Youtube, Couse page
• Neural Networks for Machine Learning by Hinton: Coursera. This course is so hard for me but it covers almost everything about neural networks. Prof. Hinton is the hero.
• FAST.ai: Course

• Deep learning book by Ian Goodfellow: https://www.deeplearningbook.org/. Very detailed reference book.
• ArXiv for research updates: https://arxiv.org/. I found it the mobile version of Feedly is useful to follow ArXiv. Also, try https://deeplearn.org/ or https://www.arxiv-sanity.com/top.

• LSTM: My collection

Ng's courses are already good enough. Reading Part 2 of Goodfellow's book can also be helpful. Learning one kind of DL packages is important, such as Keras, TF or Pytorch. People may choose a focus, either CV or NLP. People want to have deeper understanding of DL can take Hinton's course and read Part 3 of Goodfellow's book. Fast.ai has very practical courses.

• Docker Mastery: Udemy
• The Ultimate Hands-On Hadoop: Udemy
• Spark and Python for Big Data with PySpark: Udemy

• Lean Analytics: Amazon
• Data Science for Business: Amazon
• Data Smart: Amazon
• Storytelling with Data: Amazon

• Udacity: Course
• UCL Course on RL by David Silver: Course page
• CS 294: Deep Reinforcement Learning by UC Berkeley, Fall 2017: Course page

• Reinforcement Learning: An Introduction (2nd): pdf

• Recommender System by UMN: Coursera
• Mining Massive Datasets by Stanford: Free book, Course
• Introduction to Algorithms by MIT: Course page with videos
• Database by Stanford: Course
• How to Win a Data Science Competition: Coursera
• How to finish a Data Challenge: Kaggle EDA kernels

• 111 Data Science Interview Questions & Detailed Answers: Link
• 40 Interview Questions asked at Startups in Machine Learning / Data Science Link
• 100 Data Science Interview Questions and Answers (General) for 2017 Link
• 21 Must-Know Data Science Interview Questions and Answers Part I Part II
• 45 Questions to test a data scientist on basics of Deep Learning (along with solution) Link
• 30 Questions to test a data scientist on Natural Language Processing Link
• Questions on Stackoverflow: Link
• Compare two models: My collection

• Over 100 Data Science Interview Questions Link
• 20 questions to detect fake data scientists Link
• Question on Glassdoor: link

• Bayesian Statistics: From Concept to Data Analysis: Coursera
• Bayesian Methods for Machine Learning: Coursera
• Statistical Rethinking: Course Page (Recorded Lectures: Winter 2015, Fall 2017)

• Bayesian Data Analysis, Third Edition
• Applied Predictive Modeling

• Time Series Forecasting (Udacity): Udacity
• Topics in Mathematics with Applications in Finance (MIT): Course page, Youtube

• Time Series Analysis and Its Applications: Springer

• https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
• https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
• More: https://machinelearningmastery.com/?s=Time+Series&submit=Search

• Heard on the Street: Quantitative Questions from Wall Street Job Interviews by Timothy Falcon Crack: Amazon
• A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou: Amazon

• Financial Markets with Robert Shiller (Yale): Youtube, Coursera
• Topics in Mathematics with Applications in Finance (MIT): Youtube, Course page

• A Collection of Dice Problems: pdf

• Computer Science courses with video lectures: https://github.com/Developer-Y/cs-video-courses
• The Open Source Data Science Masters: https://datasciencemasters.org