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GitHub Concepts

Learn How to Use GitHub and explore Key GitHub Concepts.

Building on the topics covered in Introduction to Version Control with Git, this conceptual course enables you to navigate the user interface of GitHub effectively. Create public and private repositories, create and modify files, branches, and issues, assign tasks, tag users, review pull requests, and merge branches. Clone and fork repositories and generate private access tokens (PAT).

By Jasmin Ludolf, Data Science Content Developer, DataCamp

1. Introduction to GitHub

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Introduction to version control with Git

Discover the importance of version control when working on data science projects and explore how to use Git to track files, compare differences, modify and save files, undo changes, and allow collaborative development through the use of branches. Introduction to the structure of a repository, create new repositories and clone existing ones, and show how Git stores data. Skills to handle conflicting files.

By George Boorman, Analytics and Data Science Curriculum Manager, DataCamp

Ressources: Git Cheatsheet

1. Introduction to Git

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Introduction to NumPy

NumPy is an essential Python library. TensorFlow and scikit-learn use NumPy arrays as inputs, and pandas and Matplotlib are built on top of NumPy. In this Introduction to NumPy course, you'll become a master wrangler of NumPy's core object: arrays! You'll discover why NumPy is so efficient and use broadcasting and vectorization to make your NumPy code even faster. By the end of the course, you'll be using 3D arrays to alter a Claude Monet painting.

By Izzy Weber, Curriculum Developer @ DataCamp

1. Understanding NumPy Arrays

Create and change array shapes to suit your needs. Discover NumPy's many data types and how they contribute to speedy array operations.

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Interactive Data Visualization with Bokeh

Lead by Team Anaconda, Data Science Training

Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.

• Glyphs
  • Visual shapes
• circles, squares, triangles

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Object-Oriented Programming in Python

Object-oriented programming (OOP) is a widely used programming paradigm that reduces development times—making it easier to read, reuse, and maintain your code. OOP shifts the focus from thinking about code as a sequence of actions to looking at your program as a collection of objects that interact with each other. In this course, you’ll learn how to create classes, which act as the blueprints for every object in Python. You’ll then leverage principles called inheritance and polymorphism to reuse and optimize code.

By Alex Yarosh, Curriculum Developer @ Cockroach Labs

OOP Fundamentals

Learn what object-oriented programming (OOP) is, how it differs from procedural-programming, and how it can be applied. Define your own classes, and create methods, attributes, and constructors.

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Introduction to Data Visualization in Python

Lead by Team Anaconda, Data Science Training

This course provides a stronger foundation in data visualization in Python, broader coverage of the Matplotlib library and an overview of seaborn, a package for statistical graphics. Topics covered include customizing graphics, plotting two-dimensional arrays (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and regressions), and working with time series and image data.

Customizing plots

Review of basic plotting with Matplotlib, customizing plots using Matplotlib. Overlaying plots, making subplots, controlling axes, adding legends and annotations, and using different plot styles.

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Summary of "Merging DataFrames with pandas

Lead by Timo Grossenbacher, Project Lead Automated Journalism at Tamedia

You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Your first database

Create your very first database with a set of simple SQL commands. Next, migrate data from existing flat tables into that database. Learn how meta-information about a datab

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Introduction to SQL

This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Lead by Nick Carchedi, Product Manager at DataCamp

Selecting columns

Brief introduction to working with relational databases. Learn about their structure, how to talk about them using database lingo, and how to begin an analysis using simple SQL commands to select and summarize columns from database tables.

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Analyzing Police Activity with pandas

You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. Practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset.

Lead by Kevin Markham Founder of Data School

Preparing the data for analysis

Examine and clean the dataset, to make working with it a more efficient process. Fix data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

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Statistical Thinking in Python (Part 1)

After acquiring data and getting them into a form you can work with, you want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. You will start building the foundation to think statistically, speak the language of your data, and understand what your data is telling you. Get up-to-speed and begin thinking statistically.

By Justin Bois Lecturer at the California Institute of Technology

Graphical exploratory data analysis

Before diving into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data.