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This Introduction to Python and PySpark Development training course is designed to provide Developers and/or Data Analysts a gentle immersive hands-on introduction to the Python programming language and Apache PySpark.

Introduction to Python and PySpark Development

Course Code



2 Days or 3 Days

Course Fee




Target Audience

  • Developers and/or Data Analysts

Attendee Requirements

  • To practice working with Python and PySpark, participants are given a half-day project to complete on their own. A case study will be presented, describing the current situation and problems that needs to be solved. Beyond that, it is up to the participant to solve the problems using the tools and techniques covered in class. The project will require students to read structured and unstructured data from file, database and HDFS. Participants will start with simple challenges and work their way into consolidating, enriching, filtering, aggregating, analyzing and reporting from different data sources using a mixture of Resilient Distributed Datasets (RDD) and DataFrame. A solution will be provided at the end of the day, allowing the participants to contrast different approach to solving problems using PySpark.

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Course Description

This Introduction to Python and PySpark Development training course is designed to provide Developers and/or Data Analysts a gentle immersive hands-on introduction to the Python programming language and Apache PySpark.

Course Outline

Chapter 1. Introduction to Python

  •     What is Python
  •     Installing Python (minimal with mention of Anaconda)
  •     Using the Python shell
  •     IPython
  •     Jupyter notebooks
  •     Getting help
  •     Lab – learning the environment

Chapter 2. Writing scripts

  •     What are scripts
  •     Writing scripts
  •     Running scripts

Chapter 3. Data types and variables

  •     Understanding basic Python types
  •     Variables
  •     String literals
  •     Numbers

Chapter 4. Control statement

  •     If statement
  •     Handling exception with try

Chapter 5. Looping

  •     While loop
  •     For-in loop
  •     Range function
  •     Break and continue (optional)

Chapter 6. Functions

  •     What are functions
  •     Creating a function
  •     Arguments and return values
  •     Variable scope

Chapter 7. List and Tuples

  •     What are lists
  •     Creating list
  •     Working with lists
  •     Sorting lists
  •     Introduction to tuples

Chapter 8. Sequences

  •     What are sequences
  •     Lists, sets and strings
  •     Slicing
  •     List comprehension

Chapter 9. Sets and Dictionary

  •     What are sets
  •     Working with sets
  •     Sets vs lists
  •     Introduction to dictionaries
  •     Using dictionaries

Chapter 10. Data structures

  •     Comparison of list, tuples, sets and dictionaries
  •     Combining different data structures together
  •     Converting from one to another
  •     Common use cases

Chapter 11. Reading and writing text files (optional)

  •     Opening and closing files
  •     Reading from a file
  •     Writing to a file
  •     Using the with statement (optional)

Chapter 12. Functional Programming Primer

  •     What is functional programming
  •     Functions as data
  •     Functions as arguments
  •     Lambda expressions
  •     Using map, filter and other functions
  •     Difference between simple loops and map/filter/etc

Chapter 13. Hadoop/MapReduce Introduction

Chapter 14. Introduction to PySpark

  •     What is Apache Spark
  •     Spark use cases
  •     Architectural overview

Chapter 15. PySpark Shell

  •     What is the PySpark Shell
  •     Starting and using the shell
  •     Spark context
  •     PySpark Shell vs Spark Shell

Chapter 16. Resilient Distributed Dataset

  •     What are Resilient Distributed Dataset (RDD)
  •     Creating RDDs
  •     Transformations and operations

Chapter 17. Parallel Processing

  •     Spark cluster
  •     Data partitioning
  •     Applications, jobs and tasks

Chapter 18. Shared Variables

  •     What are shared variables
  •     Broadcast variables
  •     Accumulators

Chapter 19. Spark SQL

  •     What is Spark SQL
  •     Uniform data
  •     Hive
  •     SQL Context object

Chapter 20. Putting it all together

  •     Case study
  •     Final project

Optional Topics:
Chapter 21. Advanced Functions

  •     Default arguments
  •     Arbitrary arguments
  •     Keyword arguments
  •     What are modules
  •     Importing modules
  •     Creating simple modules

Chapter 22. Modules

Learning Path
Ways to Attend
  • Attend a public course, if there is one available. Please check our schedule, or register your interest in joining a course in your area.
  • Private onsite Team training also available, please contact us to discuss. We can customise this course to suit your business requirements.

Private Team Training is available for this course

We deliver this course either on or off-site in various regions around the world, and can customise your delivery to suit your exact business needs. Talk to us about how we can fine-tune a course to suit your team's current skillset and ultimate learning objectives.

Private Team Training | Contact us

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