Review of Python Courses (Part 11)

In Part 10, I summarized my Datacamp courses 28-30. Today I will continue with the next four.

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

My course #31 was Customer Analytics and A/B Testing in Python. This course covers:

• What is A/B testing?
• Identifying and understanding KPIs
• Exploratory analysis of KPIs
• Calculating KPIs—a practical example
• Working with time series data in pandas
• Creating time series graphs with matplotlib
• Understanding and visualizing trends in customer data
• Events and releases
• Introduction to A/B testing
• Initial A/B test design
• Preparing to run an A/B test
• Calculating sample size
• Analyzing the A/B test results
• Understanding statistical significance (get_pvalue, get_ci)
• Interpreting your test results

My course #32 was Machine Learning with Tree-Based Models in Python. This course covers:

• Decision-tree for classification (from sklearn.tree import DecisionTreeClassifier)
• Classification-tree learning
• Decision-tree for regression
• Generalization error (bias-variance tradeoff)
• Diagnosing bias and variance problems
• Ensemble learning
• Bagging (from sklearn.ensemble import BaggingClassifier)
• Out of bag evaluation
• Random forests
• AdaBoost (from sklearn.ensemble import AdaBoostClassifier)
• Gradient boosting (from sklearn.ensemble import GradientBoostingRegressor)
• Stochastic gradient boosting
• Tuning a CART’s hyperparameters
• Tuning an RF’s hyperparameters

My course #33 was Introduction to PySpark. This is a data engineering course—a field in which I found myself not very enthusiastic. This course covers:

• What is Spark, anyway?
• Using Spark in Python
• Using dataframes
• Joining
• Maching learning pipelines
• Data types
• Strings and factors

My course #34 was Cleaning Data with PySpark. This course covers:

• Intro to data cleaning with Apache Spark
• Immutability and lazy processing
• Understanding Parquet
• Dataframe column operations
• Conditional dataframe column operations
• User defined functions
• Partitioning and lazy processing
• Caching
• Improve import performance
• Cluster sizing tips
• Performance improvements
• Introduction to data pipelines
• Data handling techniques
• Data validation
• Final analysis and delivery

I will review more classes next time.