Mastering Data Wrangling with PySpark in Databricks

Mastering Data Wrangling with PySpark in Databricks, available at $19.99, has an average rating of 4.35, with 56 lectures, 5 quizzes, based on 13 reviews, and has 165 subscribers.

You will learn about Understand the fundamental concepts of PySpark and Databricks and their significance in the world of big data analytics. Learn how to set up and configure your Databricks environment, including creating an account and managing clusters. Explore PySpark's data structures, DataFrames, and Datasets, and learn to create and work with structured data. Master the essential data manipulation techniques in PySpark, including selecting, filtering, transforming, aggregating, and handling missing data. Discover how to use PySpark SQL for structured queries, compare it with DataFrame operations, and understand when to use each. Learn the essentials of ETL (Extract, Transform, Load) processes with PySpark, including reading and writing data, data cleaning, and partitioning. Gain an overview of PySpark's MLlib library and different types of machine learning tasks. Dive into feature engineering, model selection, evaluation, and hyperparameter tuning for building robust machine learning models using PySpark. Discover performance optimization techniques in PySpark, including data caching, broadcast variables, and query optimization. Explore strategies for scaling PySpark workloads, including best practices for handling large datasets. This course is ideal for individuals who are Data Scientists who are new to PySpark and Databricks and need to get up to seep with this technology. or Professionals who are starting a new role and need to master Databricks for data analysis. or Enthusiasts and curious professionals eager to learn a new skill. It is particularly useful for Data Scientists who are new to PySpark and Databricks and need to get up to seep with this technology. or Professionals who are starting a new role and need to master Databricks for data analysis. or Enthusiasts and curious professionals eager to learn a new skill.

Enroll now: Mastering Data Wrangling with PySpark in Databricks

Summary

Title: Mastering Data Wrangling with PySpark in Databricks

Price: $19.99

Average Rating: 4.35

Number of Lectures: 56

Number of Quizzes: 5

Number of Published Lectures: 56

Number of Published Quizzes: 5

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 61

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamental concepts of PySpark and Databricks and their significance in the world of big data analytics.
Learn how to set up and configure your Databricks environment, including creating an account and managing clusters.
Explore PySpark's data structures, DataFrames, and Datasets, and learn to create and work with structured data.
Master the essential data manipulation techniques in PySpark, including selecting, filtering, transforming, aggregating, and handling missing data.
Discover how to use PySpark SQL for structured queries, compare it with DataFrame operations, and understand when to use each.
Learn the essentials of ETL (Extract, Transform, Load) processes with PySpark, including reading and writing data, data cleaning, and partitioning.
Gain an overview of PySpark's MLlib library and different types of machine learning tasks.
Dive into feature engineering, model selection, evaluation, and hyperparameter tuning for building robust machine learning models using PySpark.
Discover performance optimization techniques in PySpark, including data caching, broadcast variables, and query optimization.
Explore strategies for scaling PySpark workloads, including best practices for handling large datasets.

Who Should Attend

Data Scientists who are new to PySpark and Databricks and need to get up to seep with this technology.
Professionals who are starting a new role and need to master Databricks for data analysis.
Enthusiasts and curious professionals eager to learn a new skill.

Target Audiences

Data Scientists who are new to PySpark and Databricks and need to get up to seep with this technology.
Professionals who are starting a new role and need to master Databricks for data analysis.
Enthusiasts and curious professionals eager to learn a new skill.

Explore the world of big data analytics with our comprehensive course, ‘Mastering Data Processing with PySpark in Databricks.’

In this course, we equip you with the practical skills and knowledge required to navigate the complexities of PySpark and Databricks, two industry-leading tools for efficient data processing, analysis, and the extraction of valuable insights from large datasets.

As technology evolves, the access to Big Data is easier each day, making professionals with the skill to process and extract insights from those large datasets wanted by the Big Tech Companies. Learning how to use Databricks will upskill you to be that wanted professional!

Gain practical skills in PySpark and Databricks to efficiently process, analyze, and extract valuable insights from vast datasets. Discover data processing, transformation, query optimization, and machine learning techniques from the basic.

In the age of data-driven decision-making, understanding PySpark in Databricks is not just an advantage but a necessity. By enrolling in this course, you’ll be poised to take your data analytics capabilities to the next level, making you a sought-after professional in a data-centric world.

Join us and take the first step towards optimizing your data processing skills.

By the end of this course, you will be ready to add PySpark to your resume!

Enroll today to enhance your data analytics capabilities and boost your career in the data-driven world!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview

Lecture 2: Notebooks

Chapter 2: Getting Started with PySpark and Databricks

Lecture 1: Introduction to PySpark and Databricks

Lecture 2: Setting up Your Databricks Environment

Lecture 3: Inside Databricks

Lecture 4: Transformations vs Actions

Chapter 3: Basics of PySpark

Lecture 1: PySpark Data Structures

Lecture 2: Schema and data types

Lecture 3: Creating DataFrames

Lecture 4: Creating DataFrames – Part 2

Lecture 5: Importing PySpark Functions in Databricks

Lecture 6: Loading and Displaying Data in Databricks

Lecture 7: Infer Schema

Chapter 4: Data Wrangling With PySpark

Lecture 1: Data Manipulation with PySpark

Lecture 2: Selecting, Adding and Removing Columns

Lecture 3: Renaming Columns

Lecture 4: Count, Count Distinct, Sort, Cast

Lecture 5: Filtering Data

Lecture 6: Filtering Contains and Like

Lecture 7: Between and isin

Lecture 8: Fill and Replace Values, Handling Missing Data

Lecture 9: Handling Missing Data 2

Lecture 10: Case When

Lecture 11: Aggregating Data

Lecture 12: Pivot Table

Lecture 13: Dealing with Date and Time

Lecture 14: Window

Lecture 15: Joining Datasets

Lecture 16: Percentile

Lecture 17: Median (Update)

Lecture 18: Other Useful Functions

Lecture 19: Other Useful Functions Part 2

Lecture 20: Data Caching

Lecture 21: Saving Data to CSV

Lecture 22: Saving Data to Databricks File System

Lecture 23: Exercises

Lecture 24: Exercises Solutions

Chapter 5: Query Optimization

Lecture 1: Query Optimization

Lecture 2: Cache and Persist

Lecture 3: Best practices for handling large datasets

Chapter 6: Databricks SQL

Lecture 1: DataFrame API vs. SQL API

Lecture 2: Working with SQL

Lecture 3: Basic SQL Queries

Chapter 7: Machine Learning with PySpark

Lecture 1: Introduction to Machine Learning with Pyspark

Lecture 2: MLlib Regression: Diamonds Prices

Lecture 3: MLlib Regression: Diamonds Prices (2)

Lecture 4: MLlib Regression: Diamonds Prices (3)

Lecture 5: ML Case 2 – Logistic Regression

Lecture 6: Feature engineering

Lecture 7: Preparing Data for Modeling

Lecture 8: Training and Evaluating Machine Learning Models

Lecture 9: Model Tunning

Chapter 8: Conclusion

Lecture 1: Course Conclusion

Lecture 2: Bloppers

Lecture 3: Bonus Materials

Lecture 4: Introduction to Polars

Instructors

Gustavo R Santos
Data Scientist

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 4 votes
5 stars: 8 votes

Frequently Asked Questions

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