Dive into the world of data analysis with Python and PySpark in this Video Edition by Jonathan Rioux: "Data Analysis with Python and PySpark." You will see how easily PySpark allows you to work confidently with data across many machines. This hands-on resource fully equips you with the core techniques you need to take on complex data challenges.
You will learn everything from ingesting data from various sources to dealing with messy data sets. You will explore in detail the robust tools PySpark offers when working with your data. Then, immerse yourself in exploratory data analysis to find insights and patterns to lay the ground for informed decision-making. Use Data Analysis with Python and PySpark, Video Edition - Jonathan Rioux, to master building automated data pipelines that'll drive your projects efficiently.
No prior experience with Spark is necessary when you get started with data analysis with Python and PySpark, as well as video editions with Jonathan Rioux. Through hands-on exercises and real-world examples, data analysis with Python and PySpark, the video interpreter empowers you to harness the full potential of PySpark, transforming the way you perform data analysis and machine learning projects
Data Analysis with Python and PySpark, Video Edition - Jonathan Rioux Table of Contents:
- Chapter 1. Introduction
- Part 1. Get acquainted: First steps in PySpark
- Chapter 2. Your first data program in PySpark
- Chapter 3. Submitting and scaling your first PySpark program
- Chapter 4. Analyzing tabular data with pyspark.sql
- Chapter 5. Data frame gymnastics: Joining and grouping
- Part 2. Get proficient: Translate your ideas into code
- Chapter 6. Multidimensional data frames: Using PySpark with JSON data
- Chapter 7. Bilingual PySpark: Blending Python and SQL code
- Chapter 8. Extending PySpark with Python: RDD and UDFs
- Chapter 9. Big data is just a lot of small data: Using pandas UDFs
- Chapter 10. Your data under a different lens: Window functions
- Chapter 11. Faster PySpark: Understanding Spark’s query planning
- Part 3. Get confident: Using machine learning with PySpark
- Chapter 12. Setting the stage: Preparing features for machine learning
- Chapter 13. Robust machine learning with ML Pipelines
- Chapter 14. Building custom ML transformers and estimators
- Appendix C. Some useful Python concepts
Who is this course for?
- Data scientists and data engineers who are comfortable with Python.
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