Carpentries

I’ve been an instructor for Software/Data Carpentry (I.e., The Carpentries). They have a good set of short (half day) crash course materials around other tools used in Data Science, particularly Git, Bash, SQL, and Make

https://carpentries.org/

DataCamp

A good list of courses from DataCamp to continue your learning journey. For some of the classes, I’ve listed the chapters you can go through without taking the entire course. The list of DataCamp courses are growing, but this should be a good place to start.

• Parallel Computing with Dask
  • Take the Python workflows you currently have and easily scale them up to large datasets without the need for distributed computing environments.
• Cleaning Data in Python
  • Review from the class
• Supervised Learning with scikit-learn
  • All of it
  • Particularly, Ch4: Preprocessing and pipelines
• Unsupervised Learning in Python
  • Clustering
  • Projections
• Pandas Foundations
  • Ch3: Time series in pandas
• Manipulating Time Series Data in Python
  • All of it
• Intro to Python for Data Science
  • Ch4: NumPy
• Importing Data in Python (Part 1)
  • Importing data from other file types
  • SAS/Stata
  • HDF5
  • MATLAB
  • SQL
• Importing Data in Python (Part 2)
  • If you are working with web interfaces and APIs
• Importing & Managing Financial Data in Python
• Python Data Science Toolbox (Part 1)
  • Ch3: Lambda functions and error-handling
• Python Data Science Toolbox (Part 2)
  • More “pythonic” code
  • Ch3: Bringing it all together!
    • Iterators to load data in chunks
• Manipulating DataFrames with pandas
  • Ch2: Advanced indexing: MultiIndex
  • Ch4: Grouping data
• Intermediate Python for Data Science
  • Ch1: Matplotlib
• Introduction to Data Visualization with Python
• Interactive Data Visualization with Bokeh
• Statistical Thinking in Python (part 1 and part 2)
• Natural Language Processing Fundamentals in Python (as needed)
• Introduction to Databases in Python
  • Using SQLAlchemy in stead of raw SQL querry strings