Review of Python Courses (Part 5)

In Part 4, I summarized my Datacamp courses 10-12. Today I will continue with the next three.

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

My thirteenth course was Pandas Foundations. This course covers:

• Review of pandas DataFrames
• Building DataFrames from scratch
• Importing and exporting data [pd.read_csv() args: header, names, na_values, parse_dates]
• Plotting (arrays, series, DataFrames) with pandas
• Visual exploratory data analysis (line, scatter, box plots, histogram, and different plotting idioms)
• Statistical exploratory data analysis
• Separating populations
• Indexing time series (creating and using a Datetime index)
• Resampling time series data
• Manipulating time series data
• Time series visualization (pandas, not matplotlib)
• Reading and cleaning the data (cleaning and tidying datetime data)

Class #14 for me was Statistical Thinking in Python (Part 1). This course covers:

• Introduction to exploratory data analysis
• Plotting a histogram
• Plot all of your data: Bee swarm plots [sns.swarmplot()]
• Plot all of your data: Empirical Cumulative Distribution Functions (ECDF)
• Introduction to summary statistics: the sample mean and median
• Percentiles, outliers, and box plots
• Variance and standard deviation
• Covariance and the Pearson correlation coefficient
• Probabilistic logic and statistical inference
• Random number generators and hacker statistics
• Probability distributions and stories: the Binomial distribution (binomial PMF and CDF)
• Poisson processes and the Poisson distribution
• Probability density functions
• Introduction to the Normal distribution
• The Normal distribution: properties and warnings
• The Exponential distribution

My fifteenth course was Introduction to Data Visualization with Matplotlib. This course covers:

• Adding data to axes
• Customizing your plots (adding markers, setting linestyle, color, axis labels)
• Small multiples with plt.subplots
• Plotting time-series data (using fig/ax, zooming in on datetime range)
• Plotting time-series with different variables (using twin axes, coloring vars and ticks, all-encompassing function)
• Annotating time-series data
• Quantitative comparisons: bar charts (stacking, adding legend, color)
• Quantitative comparisons: histograms
• Statistical plotting
• Quantitative comparisons: scatter plots (encoding time by color)
• Preparing your figures to share with others (choosing plot style)
• Sharing your visualizations with others [fig.savefig()]
• Automating figures from data