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PySpark for Data Engineers and Data Analysts in 1h30

Name: PySpark for Data Engineers and Data Analysts in 1h30
Rating: 4.3 (10 reviews)

Mastering PySpark with an End-to-End Project for Data Engineers, Data Scientists and Data Analysts.

Created byMikail Altundas

Last updated 3/2026

English

English [Auto],

What you'll learn

Gain solid PySpark skills with no prerequisites
Avoid beginner pitfalls and master best practices
Pass PySpark exams, certifications and technical tests
Receive a training certificate at the end of the course
Have the necessary PySpark skills to compete for job opportunities
Gain a solid understanding of key PySpark concepts
Realize professional projects with PySpark
Work as a freelance with PySpark.
Acquire the skills needed to work with companies that use PySpark
Master the essential concepts in PySpark to become a Data Engineer, Data Scientist or Data Analyst

Course content

5 sections • 21 lectures • 1h 40m total length

Let's Get Started With PySpark!1:41
Master PySpork, a powerful Python framework for large-scale data processing. Advance from basics to a major project with self-paced resources and a completion certificate.

Project Overview4:23
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.
Data Source0:16
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 PySpark on Google Colab and Create a Spark Session6:58
Import Data Into a Spark DataFrame7:28
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 Drive4:01
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 and Delete Columns in a Data Table7:19
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 New Columns in a Spark DataFrame6:24
Before Continuing the Course...0:12
Rate this course, share your feedback, and share your thoughts on the content and your overall PySpark learning experience.
Filter a PySpark Dataframe with Conditions6:11
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.
Group Data and Create New Columns Based on Existing Data8:16
Join PySpark Dataframes5:27
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.
Perform Operations With Columns and Delete Unnecessary Data6:38
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.
Create Statistical Columns9:19
Use Window Functions9:41
Create The Final Spark Data Table3:09
End of The PySpark Project2:46
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.

Requirements

This course covers everything from A to Z. There are no prerequisites.

Description

Mastering PySpark to Become a Data Engineer, Data Scientist, or Data Analyst

This course aims to train you in the PySpark framework on Python, which is widely used by Data Engineers, Data Scientists, and Data Analysts to handle large volumes of data.

Acquire Fundamental Skills in PySpark

No more hunting for information on Google; the essence of your learning is concentrated in this course.

Learn Quickly for Effective Skill Development

This course is designed to familiarize you with PySpark quickly and effectively. In just a few hours and through two projects, you will possess the necessary knowledge to stand out.

Recent Course, Regularly Updated

Recently updated, this training aligns with the skills currently in high demand by companies using PySpark.

Avoid Beginner's Traps

The course highlights the best practices of an experienced PySpark developer to help you produce professional-quality code.

Succeed in Your Exams, Technical Tests, and PySpark Certifications

The course content is structured to effectively prepare you for your university exams, certifications, and technical tests related to PySpark.

Secure a Position in a Company or Undertake Freelance Assignments

PySpark is among the most coveted frameworks in both corporate and freelance settings. Training in this library opens the door to numerous professional opportunities.

Train for In-Demand Careers

The demand for Data Scientists, Data Engineers, Data Analysts, and other Big Data-related roles is growing. Now is the perfect time to prepare for these careers by learning to master PySpark.

Work for Top Companies

Renowned companies such as Uber, Netflix, Airbnb, Amazon, Meta (formerly Facebook), and Microsoft, are currently seeking skilled professionals in PySpark.

Obtain a Completion Certificate

A certificate confirming that you have followed and completed the course will be awarded at the end of the training.

Who this course is for:

People with little or no programming experience who want to learn PySpark
People who want to develop expertise with PySpark
People who want to apply for jobs or freelance positions that require PySpark skills

PySpark for Data Engineers and Data Analysts in 1h30

What you'll learn

Explore related topics

Course content

Get Started With PySpark for Data Engineers, Data Scientists, and Data Analysts1 lecture • 2min

Project Setup1 lecture • 9min

Complete Big Data Project Using PySpark16 lectures • 1hr 28min

Download The Complete Python Code For The PySpark Project1 lecture • 1min

End of The PySpark Course2 lectures • 1min

Requirements

Description

Who this course is for: