PySpark for Data Engineers and Data Analysts in 1h30

Discover how to use Google Colab to write and run Python in the cloud for free, save notebooks to Google Drive, and share code, with Kaggle as an alternative.

Explore a hands-on PySpark project that analyzes German football match data to rank top-scoring teams per season, using more than 24,000 lines of data.

Point out the project data source from a GitHub repository and invite students to explore it. Acknowledge the author’s contribution as the dataset for the PySpark project.

Install PySpork on Google Colab and create a spark session for data analysis using builder.appname and get or create, then verify the session.

Learn to import a CSV dataset into a Spark DataFrame with PySpark, handling headers and loading footballmatches.csv. Preview the results with show to verify the data in Spark.

Store data files on Google Drive to persist across Colab sessions, link Google Drive to Colab, and import data directly from Drive for seamless reuse of footballmatches.csv.

Rename spark data table columns with selectExp, renaming fdhd to home team goals, ftag to away team goals, and ftr to final result; drop columns and preview dataframe in Colab.

Create and add three binary columns: homeTeamWin, awayTeamWin, and gameTie in a Spark DataFrame, using withColumn and when/otherwise in PySpark to simplify downstream data operations.

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Filter a PySpark dataframe to keep only Division 1 Bundesliga matches from 2000 to 2015, using filter with col and boolean operators, and convert to pandas for visualization.

Learn to group data by season and team in PySpark, create new columns for home and away metrics (wins, losses, ties, goals), and rename fields for clearer analysis.

Join PySpark dataframes by merging df_home_matches and df_away_matches on season and team, creating df_merged to compare home and away performance.

Group home and away columns to create total scores and results using withColumn, building a df_totals with goals scored, goals against, wins, losses, and ties, then drop columns to df_cleaned.

Learn to create statistical columns in a data frame, such as goal differentials (goals scored minus goals against) and win percentage (wins divided by total games), with df_processed and df_cleaned.

Learn to compute season rankings with window functions in PySpark by partitioning on season and ordering by win percentage and goal differential, creating a rank column.

Create a final spark data table by filtering the ranked data to show only first-place teams for each season, using PySpark data frames.

Conclude the PySpark data analysis project by applying core data processing tasks—transforming and creating columns, filtering, and grouping—and standardize outputs to pandas, with downloadable notebooks or .py files.

Complete the PySpork course to enrich your knowledge and skills, and highlight PySpork proficiency on your resume and LinkedIn to attract recruiters seeking data engineers, data analysts, and data scientists.

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