Review of Python Courses (Part 6)

In Part 5, I summarized my Datacamp courses 13-15. Today I will continue with the next three.

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

My course #16 was Introduction to Data Science in Python. This course covers:

• Creating variables
• What is a function?
• What is pandas?
• Selecting columns
• Select rows with logic
• Creating line plots
• Adding labels and legends
• Adding some style (line color, width, style, markers, template)
• Making a scatter plot (marker transparency)
• Making a bar chart (horizontal, error bars, stacked)
• Making a histogram (bins, range, normalizing)

My course #17 was Joining Data with Pandas. This course covers:

• Inner join (changing df values with .loc accessor)
• One to many relationships
• Merging multiple DataFrames
• Left join (count number of rows in a column with missing data)
• Right and outer joins
• Merging a table to itself (i.e. self join)
• Merging on indexes
• Filtering joins (semi-joins, anti-joins)
• Concatenate DataFrames together vertically [.append()]
• verify_integrity=True identifies accidental duplicates while validate arg helps to identify relationship type
• Using merge_ordered() (for ordered/time-series data and to fill in missing values)
• Using .merge_asof() (matches on nearest-value rather than equal-value columns)
• Selecting data with .query()
• Reshaping data with .melt()

Introduction to Linear Modeling in Python was my eighteenth course. This covers:

• Introductory concepts about models (interpolation, extrapolation)
• Visualizing linear relationships [object-oriented (OOP) approach to matplotlib]
• Quantifying linear relationships (covariance, correlation, normalization)
• What makes a model linear (Taylor series, overfitting, defining function to plot graph)
• Interpreting slope and intercept
• Model optimization (RSS: sum of squared residuals)
• Least-squares optimization (by numpy, Scipy, Statsmodels)
• Modeling real data
• The limits of prediction
• Goodness of fit (deviations, residuals, and R-squared in code)
• Standard error (RMSE measures spread of residuals whereas SE measures uncertainty in model params)
• Inferential statistics concepts
• Model estimation and likelihood
• Model uncertainty and sample distributions (bootstrap in code)
• Model errors and randomness