Review of Python Courses (Part 17)

In Part 16, I summarized my Datacamp courses 47-49. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #50 was Introduction to Shell. This course covers:

• How does the shell compare to a desktop interface?
• Where am I and how can I identify files and directories?
• How can I move to another directory (~ is home)?
• How to copy, rename, move, and delete files
• How to create and delete directories
• How to view file contents
• Modifying commands with flags
• Getting help for a command
• Selecting columns from a file
• Repeating commands
• Selecting lines with certain values
• Storing command output to a file or using as input
• Combining commands with pipe symbol
• Counting records in a file
• Specifying multiple files at once
• Wildcards
• Sorting lines of text and removing duplicate lines
• How to stop a running program
• Printing a variable’s value
• How does the shell store information?
• Repeating commands many times or once for each file
• Recording names of a set of files
• Variable’s name versus its value
• Running many commands in a single loop
• Using semicolons to do multiple things in a single loop
• Editing a file
• Saving commands to rerun later
• Reusing pipes
• Passing filenames to scripts
• Processing a single argument
• Writing loops in a shell script

My course #51 was Generalized Linear Models (GLM) in Python. This material is thick and really demands a third look (for me). This course covers:

• Going beyond linear regression (import statsmodels.api as sm; from statsmodels.formula.api import glm)
• How to build a GLM?
• How to fit a GLM in Python?
• Binary data and logistic regression (odds, odds ratio, and probability)
• Interpreting coefficients
• Interpreting model inference
• Computing and describing predictions
• Count data and Poisson distribution
• Interpreting model fit
• The problem of overdispersion
• Multivariable logistic regression (from statsmodels.stats.outliers_influence import variance_inflation_factor)
• Comparing models
• Model formula (from patsy import dmatrix)
• Categorical and interaction terms

My course #52 was Pandas Joins for Spreadsheet Users. This course covers:

• Joining data: a real-world necessity
• Concatenation
• Power and flexibility
• Types of joins
• A closer look at one-to-one joins
• Combining common data with inner joins
• “Out of many, one”
• Joining on key columns
• Index-based joins
• Joining data in real life
• Working with time data

I will review more classes next time.