1. Mathematics
1.1. Books & Courses
1.1.1. Kindle
1.1.1.1. Graph Theory
1.1.2. Coursera
1.1.2.1. Data Science Math
1.2. Guides
1.2.1. https://elitedatascience.com/learn-math-for-data-science
1.3. Topics
1.3.1. Graph Theory
1.3.2. Linear Algebra
2. Statistics
2.1. Books
2.1.1. LeanPub
2.1.1.1. Statistical Inference for data science
2.1.2. PDF
2.1.2.1. The Elements of Statistical Learning
2.1.2.2. Building Probabilistic Graphical Models
2.1.2.3. Think Bayes
2.1.2.4. Think Stats
2.1.2.5. Introduction to statistical learning
2.1.2.6. A probabilistic Theory of Pattern Recognition
2.1.3. Packt Ebooks
2.1.3.1. Building Probabilistic Graphical Models with Python
2.1.4. Probability Theory
2.2. Guides
2.2.1. How to Learn Statistics for Data Science, The Self-Starter Way
2.3. Topics
2.3.1. Statistical Inference
2.3.2. Probabilistic Modeling
2.3.3. Anomaly Detection
2.3.4. Bayesian
2.3.5. Exploratory Analysis
3. Computer Science
3.1. Algorithms and Data Structures
3.1.1. KD-Trees
3.1.2. Recursion
3.1.3. Guides
3.1.3.1. Problem Solving with Algorithms and Data Structures using Python — Problem Solving with Algorithms and Data Structures
3.2. Software Development
3.2.1. Languages
3.2.1.1. python
3.2.1.1.1. Threads
3.2.1.1.2. Packages
3.2.1.1.3. Decorators
3.2.1.1.4. Books & Courses
3.2.1.1.5. Web FrameWorks
3.2.1.2. R
3.2.1.2.1. Packages
3.2.1.2.2. Books
3.2.2. Coding Standards
3.2.2.1. Git
3.2.2.2. Continuous Integration
3.2.2.3. Books
3.2.2.3.1. LeanPub
3.2.3. Databases
3.2.3.1. MongoDb
3.2.3.2. Postgresql
3.2.3.3. SQL
3.2.3.4. Graph
3.2.3.5. TimeSeries Databases
3.2.3.6. Books
3.2.3.6.1. PDF
3.3. Parallel and Scalable computing
3.3.1. Spark
3.4. System Architecture and Design
3.4.1. Books
3.4.1.1. LeanPub
3.4.1.1.1. RabbitMQ Patterns
4. Blogs
4.1. DeepMind
4.2. Karpathy
4.3. blog.dataquest.io
5. Data Vizualization
5.1. Books
5.1.1. Kindle
5.1.1.1. Storytelling with Data
5.1.2. LeanPub
5.1.2.1. The Art of Data Science
5.2. Topics
5.2.1. Plot.ly
5.2.2. ggplot2
5.2.3. seaborn
5.2.4. Matplotlib
6. Machine Learning
6.1. Books
6.1.1. Kindle
6.1.1.1. Python 3 Text Processing
6.1.1.2. Natural Language Processing with Python
6.1.1.3. Predictive Analytics
6.1.2. PDF
6.1.2.1. Hidden Technical Debt in ML
6.1.2.2. Python Machine Learning
6.1.2.3. Cassandra - The definitive Guide
6.1.2.4. Hadoop the Definitive Guide
6.1.2.5. High Performance Spark
6.1.2.6. Practical Machine Learning - Recommendation Systems
6.1.2.7. Practical Machine Learning - Anomaly detection
6.1.2.8. Practical Machine Learning with H2O
6.1.2.9. Thoughtful ML with Python
6.1.2.10. Time Series Databases
6.1.3. Packt Ebooks
6.1.3.1. Mastering Machine Learning with Scikit-learn
6.1.3.2. Python Machine Learning
6.1.3.3. Clojure for Machine Learning
6.1.3.4. Building Machine Learning Systems with Python
6.1.3.4.1. Building Machine Learning Systems with Python - Second Edition
6.1.3.5. Python Text Processing with NLTK 2.0 Cookbook: Lite
6.2. Topics
6.2.1. Supervised
6.2.1.1. Neural Networks
6.2.2. Big Data Tools
6.2.2.1. Databases
6.2.2.1.1. Hadoop
6.2.2.1.2. MangoDb
6.2.2.1.3. Cassandra
6.2.2.2. Systems
6.2.2.2.1. TensorFlow
6.2.2.2.2. Spark
6.2.3. Unsupervised
6.2.3.1. K Nearest Neighbors
6.2.3.2. Natural Language Processing
6.2.3.2.1. LDA
7. Communication
7.1. Blog
7.2. Portfolio
7.3. Scientist Teaching Science
7.4. Weekly Lab Meetings
8. Domain Expertise
8.1. Biology
8.1.1. NGS
8.2. Ecommerce
8.3. Machine Learning
8.4. Data Science
8.4.1. District Data Labs
8.4.2. Data Science Meetups
8.4.3. read whitepapers
8.5. Programming
8.5.1. Reading Open Source Code
8.5.2. Python Meetups
8.5.3. Hackathon
8.6. Finance
9. Books & Courses
9.1. Kindle
9.1.1. Data Science from Scratch
9.2. PDF
9.2.1. Data Science at the Commandline
9.2.2. Doing Data Science
9.2.3. Head First Data Analysis
9.2.4. Python Data Science Handbook Python Data Science Handbook | Python Data Science Handbook
9.3. Udemy
9.3.1. Data Science and Machine Learning Boot Camp
9.4. Coursera
9.4.1. Data Science Specialization
9.4.2. Stanford - Andrew Ng Machine Learning | Coursera
9.5. Udacity
9.5.1. Google Tensorflow Deep Learning | Udacity