Data-Science-Resources

Resources I used for the path of data science

Data-Science-Resources

Resources I used for the path of data science.

You are always welcome to drop me an email at: xiongxinland@gmail.com

Computer Vision

- paper：

 * Mark-RCNN - * code link 1

Machine Learning:

- Videos:

- Textbooks:

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

Natural Language Processing:

- Videos:

- Books:

  • 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

- Packages:

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

- More:

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

Deep Learning

- Videos:

  • 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

- Books:

  • Deep learning book by Ian Goodfellow: http://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 http://www.arxiv-sanity.com/top.

Analytics:

Systems:

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

Reinforcement Learning:

- Videos:

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

    - Books:

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

Others:

Interviews:

- Lists with Solutions:

  • 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 Link
  • 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

- Without Solutions:

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

Topics to Learn ->

Bayesian:

- Courses:

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

- Book:

  • Bayesian Data Analysis, Third Edition
  • Applied Predictive Modeling

Time series:

- Courses:

- Books:

  • Time Series Analysis and Its Applications: Springer

- With LSTM:

  • 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

Quant:

- Books:

  • 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

- Courses:

- Other:

  • A Collection of Dice Problems: pdf

More:

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

Course list:

  • Udacity software engineering: 1, 2, 3 -Ongoing-
  • Stanford 224n
  • Topics in Mathematics with Applications in Finance (MIT): Youtube, Course
  • FAST.ai part2: http://course.fast.ai/part2.html
  • CS 294: Deep Reinforcement Learning: http://rll.berkeley.edu/deeprlcourse/
  • CMU 701 by Tom Mitchell: http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
  • Cryptography: https://www.coursera.org/learn/crypto 
  • Statistical Rethinking: http://xcelab.net/rm/statistical-rethinking/
  • Probabilistic Graphical Models: https://www.coursera.org/specializations/probabilistic-graphical-models
  • Bitcoin and Cryptocurrency Technologies：https://www.coursera.org/learn/cryptocurrency
  • Compiler：https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about
  • NTU - Machine Learning (2017,Fall) http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html