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Online resources for data science education
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README.md

Online Eductional Resources for eScience

This structures eScience educational materials by topic. If you know of additional materials or topics that should be included, please submit an issue to this repo.

General Resources

Python for Beginners

Prerequisites: None

Learning objectives

  • Create and manipulate elementary types and lists
  • Read and write CSV and Excel files
  • Create and manipulate DataFrames
  • Basics of flow control (if, for)
  • Create functions
  • Organize code into files

Online Resources

There are two versions of Python in common use: Python 2 and Python 3. There are some small differences between them. You likely will use Python 3 more, but don’t hesitate to take advantage of a Python 2 tutorial.

R for Beginners

Prerequisites: None

Learning objectives

  • Create and manipulate elementary types and lists
  • Read and write CSV and Excel files
  • Create and manipulate DataFrames
  • Basics of flow control (if, for)
  • Create functions
  • Organize code into files

Online resources

git for Beginners

Prerequisites: None

Learning objectives

  • Git initialization
  • Creating a local repository
  • Creating a remote repository
  • Cloning a local repository
  • Add, remove files
  • Committing and pushing a change
  • Obtaining a previous version of a file
  • Viewing the changes made since the last commit
  • Git workflow

Online resources

git for Intermediate Users

Prerequisites

Learning Objectives

  • Resolving conflicts
  • Managing branches
  • Using issues

Online Resources

Team Processes

Introduction to Cloud Computing

SQL Basics

SQL Servers

Introduction to JavaScript

Programming Style and Documentation

High Performance Computing (HPC)

  • HPC for python with Dask

Geographical Information Systems

Learning Objectives

  • Resolving conflicts
  • Managing branches
  • Using issues

Online Resources

Machine Learning

UI Design

Data Structures

Basics of Building Empirical Models

Prerequisites: None

Learning Objectives

  • Statistical basics: probability distributions, sampling, variability, estimation vs. prediction vs. hypothesis testing
  • Residual analysis
  • Model evaluation: metrics, cross validation
  • Parameter estimation

Neuro Data Science

Online Resources

Image Processing

Online Resources

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