Review of Python Courses (Part 4)

In Part 3, I summarized the 6th through 9th Datacamp courses I took. Today I will continue with the next three.

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

Course #10 was Introduction to Databases in Python. This course covers:

• Databases consist of tables
• Connecting to your database
• Introduction to SQL queries
• Filtering and targeting data
• Ordering query results
• Counting, summing, and grouping data
• SQLAlchemy and pandas for visualization
• Calculating values in a query
• SQL relationships
• Working with hierarchical tables
• Handling large ResultSets
• Creating databases and tables
• Inserting and updating data into a table
• Deleting data from a database
• Census case study
• Populating and querying the database

Course #11 was Introduction to Statistics in Python. This course covers:

• What is statistics?
• Measures of center
• Measures of spread and outliers (from scipy stats import iqr)
• What are the chances [probability and dataframe .sample() method from random module]?
• Discrete and continuous distributions
• Generating random numbers according to uniform distribution (from scipy stats import uniform)
• Computing cumulative distribution functions
• Binomial distribution (from scipy stats import binom)
• Normal distribution (from scipy stats import norm)
• Central limit theorem
• Poisson distribution (from scipy stats import poisson)
• Exponential distribution (from scipy stats import expon)
• Student’s t-distribution
• Log-normal distribution
• Correlation (and caveats)
• Experimental design and confounders

My twelfth course was Introduction to Data Visualization in Python. This course covers:

• Plotting multiple graphs
• Customizing axes
• Legends, annotations, and styles
• Working with 2-D arrays and meshgrid
• Visualizing bivariate functions (color bar, color map, axis tight, and contour plots)
• Visualizing bivariate distributions (rectangular and hexagonal binning)
• Working with images
• Visualizing regressions [sns.lmplot(), hue, col, sns.residplot()]
• Visualizing univariate distributions [sns.stripplot(), sns.swarmplot(), sns.violinplot()]
• Visualizing multivariate distributions [sns.jointplot(), kde, sns.pairplot(), hue, covariance sns.heatplot()]
• Visualizing time series (formatting datetime index)
• Time series with moving windows
• Histogram equalization in images