Programming for Data Science with Python (Nanodegree Program)

DS colleagues, in the “Programming for Data Science with Python - Nanodegree Program” you will learn programming skills needed to uncover patterns and insights in large data sets, running queries with relational databases and working with Unix shell and Git. Skill-based training modules include: 1) Introduction to SQL - Learn SQL language fundamentals such as building basic queries and advanced functions like Window Functions, Subqueries and Common Table Expressions. Shell Workshop - a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer, 2) Introduction to Python - programming fundamentals such as data types and structures, variables, loops, and functions, Why Python Programming?, Data Types and Operators, data types and operators, built-in functions, type conversion, whitespace, and style guidelines, 3) Data Structures in Python - use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful built-in functions and operators, 4) Control Flow - build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions, 5) Functions - use functions to improve and reuse your code. Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators, 6) Scripting - set up your own programming environment to write and run Python scripts locally. Learn good scripting practices, interact with different inputs, and discover awesome tools, 7) NumPy - learn the basics of NumPy and how to use it to create and manipulate arrays, 8) Pandas - learn the basics of Pandas Series and DataFrames and how to use them to load and process data, 9) Advanced Topics - iterators and generators. Project 1 - Explore US Bikeshare Data: Use Python to understand U.S. bikeshare data. Calculate statistics and build an interactive environment where a user chooses the data and filter for a dataset to analyze, 10) Introduction to Version Control - use version control to save and share your projects with others, 11) Create a Git Repo - learn how to create a repository, 12) Commits, Tags, Conflicts - review an existing Git repository's history of commits is extremely important, 13) Remotes and Developer Repos - learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project, 14) Writing READMEs for Repos

Learn the importance of well documented code and see how to craft meaningful READMEs. Project 2: Post Your Work on GitHub - use your local git repository and your GitHub repository. Fork a repository, work on files, stage files and commit them to GitHub. You will also demonstrate how to hide files using .gitignore files.

Enroll today (teams & executives are welcome): https://imp.i115008.net/rQ7N6B 

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Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

3 - The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age (Audible) (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)