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Awesome-Data-Science-Materials

This repository contains a summary of some data science materials: papers, useful packages, MOOC, career development info, etc.

Data Science Education & Career

  • Machine Learning Mastery: Less math and more tutorials with working code.
  • Deep Learning: Deep Learning Specialization. It teaches the most important and foundational principles of Deep Learning
  • Introduction to Deep Learning (MIT): It is a high-level introduction course. If you want to learn more details about the building blocks of deep learning, refer to the previous course from deeplearning.ai
  • TensorFlow in Practice: This Specialization will teach you best practices for using TensorFlow. It is code heavy but doesn't introduce the theoretical background of the models. It is a great companion to Deep Learning Specialization.
  • A Crash Course in Causality: Great introduction to causal inference.It provides R code on some example data.

Data Science Paper

SQL

  1. SQL for Data Scientist Learning Notes
  2. Select * SQL
  3. Leetcode
  4. LinkedIn Learning
  5. Window Functions
  6. HackerRank
  7. W3 Schools
  8. CodeAcademy
  9. SQLZoo
  10. SQL Bolt:
  11. Zachary Thomas' SQL Questions
  12. Effective SQL for Data Science

Deep Learning

Effective CNNs

Different Architecture Search Algorithms:

  • NASnet: 1800 GPU days (5 yrs on 1 GPU)

  • AmoebaNet: 3150 GPU days

  • DARTS: 4 GPU days

  • ENAS: 1000 x cheaper than standard NAS

Understanding Neural Networks

RNN

Others

Natural Language Processing

Operations research

operations research package in r

operations research package in python

  • Gurobi: proprietary and free for academic use

Experimentation

Tidy Data

  • broom package: takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames

Causal Inference

  • CausalTree

Others

  • Unix Learning Notes: http://scientistcafe.com/notes/Unix/
  • scijava-jupyter-kernel aims to be a polyglot Jupyter kernel. It uses the Scijava scripting languages to execute the code in Jupyter client and it's possible to use different languages in the same notebook.

Some of the supported languages are Groovy (default), Python, Beanshell, Clojure, Java, Javascript, Ruby and Scala.

https://github.com/scijava/scijava-jupyter-kernel/tree/afd8c1c7be5b92a734e0fac9d78bcc0216162340

Reproducible Report

R Markdown Theme Gallery: http://www.datadreaming.org/post/r-markdown-theme-gallery/

Web Development

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Summary of some data science materials

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