PySpark Essentials for Data Scientists (Big Data + Python)

PySpark Essentials for Data Scientists (Big Data + Python), available at $19.99, has an average rating of 4.05, with 109 lectures, 9 quizzes, based on 802 reviews, and has 5523 subscribers.

You will learn about Use Python with Big Data on a distributed framework (Apache Spark) Work with REAL datasets on realistic consulting projects How to streaming LIVE data from Twitter using Spark Structured Streaming Learn how to create a "Pandora Like" app that classifies songs into genres using machine learning Flag suspicious job postings using Natural Language Processing Use machine learning to predict optimal cement strength and the factors that affect it Classify Christmas cooking recipes using Topic Modeling (LDA) Customer Segmentation using Gaussian Mixture Modeling (Clustering) Use cluster analysis to develop a strategy designed to increase college graduation rates for under-priveleged populations How to use the k-means clustering algorithm to define a marketing outreach strategy Integrate a UI to monitor your model training and development process with MLflow Theory and application of cutting edge data science algorithms Manipulate, Join and Aggregate Dataframes in Spark with Python Learn how to apply Spark's machine learning techniques on distributed Dataframes Cross Validation & Hyperparameter Tuning Frequent Pattern Mining Techniques Classification & Regression Techniques Data Wrangling for Natural Language Processing How to write SQL Queries in Spark This course is ideal for individuals who are Data Scientists interested in learning PySpark or PySpark developers looking to strengthen their coding skills or Python developers who need to work with big data or Data Scientists who want to learn to work with big data It is particularly useful for Data Scientists interested in learning PySpark or PySpark developers looking to strengthen their coding skills or Python developers who need to work with big data or Data Scientists who want to learn to work with big data.

Enroll now: PySpark Essentials for Data Scientists (Big Data + Python)

Summary

Title: PySpark Essentials for Data Scientists (Big Data + Python)

Price: $19.99

Average Rating: 4.05

Number of Lectures: 109

Number of Quizzes: 9

Number of Published Lectures: 109

Number of Published Quizzes: 9

Number of Curriculum Items: 118

Number of Published Curriculum Objects: 118

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Use Python with Big Data on a distributed framework (Apache Spark)
Work with REAL datasets on realistic consulting projects
How to streaming LIVE data from Twitter using Spark Structured Streaming
Learn how to create a "Pandora Like" app that classifies songs into genres using machine learning
Flag suspicious job postings using Natural Language Processing
Use machine learning to predict optimal cement strength and the factors that affect it
Classify Christmas cooking recipes using Topic Modeling (LDA)
Customer Segmentation using Gaussian Mixture Modeling (Clustering)
Use cluster analysis to develop a strategy designed to increase college graduation rates for under-priveleged populations
How to use the k-means clustering algorithm to define a marketing outreach strategy
Integrate a UI to monitor your model training and development process with MLflow
Theory and application of cutting edge data science algorithms
Manipulate, Join and Aggregate Dataframes in Spark with Python
Learn how to apply Spark's machine learning techniques on distributed Dataframes
Cross Validation & Hyperparameter Tuning
Frequent Pattern Mining Techniques
Classification & Regression Techniques
Data Wrangling for Natural Language Processing
How to write SQL Queries in Spark

Who Should Attend

Data Scientists interested in learning PySpark
PySpark developers looking to strengthen their coding skills
Python developers who need to work with big data
Data Scientists who want to learn to work with big data

Target Audiences

Data Scientists interested in learning PySpark
PySpark developers looking to strengthen their coding skills
Python developers who need to work with big data
Data Scientists who want to learn to work with big data

This course is for data scientists (or aspiring data scientists) who want to get PRACTICAL training in PySpark (Python for Apache Spark) using REAL WORLD datasets and APPLICABLE coding knowledge that you’ll use everyday as a data scientist! By enrolling in this course, you’ll gain access to over 100 lectures, hundreds of example problems and quizzes and over 100,000 lines of code!

I’m going to provide the essentials for what you need to know to be an expert in Pyspark by the end of this course, that I’ve designed based on my EXTENSIVE experience consulting as a data scientist for clients like the IRS, the US Department of Labor and United States Veterans Affairs.

I’ve structured the lectures and coding exercises for real world application, so you can understand how PySpark is actually used on the job. We are also going to dive into my custom functions that I wrote MYSELF to get you up and running in the MLlib API fast and make getting started building machine learning models a breeze! We will also touch on MLflow which will help us manage and track our model training and evaluation process in a custom user interface that will make you even more competitive on the job market!

Each section will have a concept review lecture as well as code along activities structured problem sets for you to work through to help you put what you have learned into action, as well as the solutions to each problem in case you get stuck. Additionally, real world consulting projects have been provided in every section with AUTHENTIC datasets to help you think through how to apply each of the concepts we have covered.

Lastly, I’ve written up some condensed review notebooks and handouts of all the course content to make it super easy for you to reference later on. This will be super helpful once you land your first job programming in PySpark!

I can’t wait to see you in the lectures! And I really hope you enjoy the course! I’ll see you in the first lecture!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Frequently Asked Questions

Lecture 2: Course Introduction

Lecture 3: Course Orientation

Lecture 4: Course Materials Bulk Download

Lecture 5: Resources for Setting up PySpark

Lecture 6: Python Cheatsheet Resources

Lecture 7: Introduction to PySpark

Lecture 8: Transitioning from Python to PySpark Concept Review

Lecture 9: Transitioning from Python to PySpark Code Along Activity

Chapter 2: Dataframe Essentials: Read, Write, Validate & Explore

Lecture 1: Dataframe Essentials Concept Review

Lecture 2: A little something to keep you going….

Lecture 3: Read, Write and Validate Dataframes Code Along Activity

Lecture 4: Read, Write and Validate Data HW

Lecture 5: Read, Write and Validate Data HW Solutions Code Review

Lecture 6: A little something to keep you going….

Lecture 7: Search and Filter Dataframes Code Along Activity

Lecture 8: Search and Filter Dataframes HW

Lecture 9: Search and Filter Dataframes HW Solution Code Review

Lecture 10: A little something to keep you going….

Lecture 11: SQL Options in Spark/PySpark Code Along Activity

Lecture 12: SQL Options in Spark/PySpark HW

Lecture 13: SQL Options in Spark/PySpark HW Solutions

Lecture 14: A little something to keep you going….

Chapter 3: Dataframe Essentials: Clean, Manipulate, Join, Aggregate

Lecture 1: Manipulating Dataframes Code Along Activity

Lecture 2: Manipulating Dataframes HW

Lecture 3: Manipulating Dataframes HW Solution

Lecture 4: A little something to keep you going….

Lecture 5: Aggregating Data in Dataframes Code Along Activity

Lecture 6: Aggregating Data in Dataframes HW

Lecture 7: Aggregating Data in Dataframes HW Solution

Lecture 8: A little something to keep you going….

Lecture 9: Joining and Appending Dataframes Code Along Activity

Lecture 10: Joining and Appending Dataframes HW

Lecture 11: Joining and Appending Dataframes HW Solution Code Review

Lecture 12: A little something to keep you going….

Lecture 13: Handling Missing Data in Dataframes Code Along Activity

Lecture 14: Handling Missing Data in Dataframes HW

Lecture 15: Handling Missing Data in Dataframes HW Solution

Lecture 16: Dataframe Essentials Coding Master Review

Lecture 17: A little something to keep you going….

Chapter 4: Introduction to Spark MLlib

Lecture 1: Introduction to Machine Learning Concept Review

Lecture 2: Introduction to MLlib Concept Review

Lecture 3: Model Selection and Tuning in MLlib Concept Review

Lecture 4: Two Links to Bookmark

Lecture 5: A little something to keep you going….

Chapter 5: Classification in MLlib

Lecture 1: Introduction to Classification in MLlib Concept Review

Lecture 2: A little something to keep you going….

Lecture 3: Classification in MLlib Code Along Part 1: Data Formatting and Transformations

Lecture 4: Classification in MLlib Code Review Part 2.0: Train and Evaluate Models [Intro]

Lecture 5: Classification in MLlib Code Review Part 2.1: Train & Test Models [Logistic]

Lecture 6: Classification in MLlib Code Review Part 2.2: Train & Test Models [1 vs Rest]

Lecture 7: A little something to keep you going….

Lecture 8: Classification in MLlib Code Review Part 2.3: Train & Test Models[Multilayer PC]

Lecture 9: Classification in MLlib Code Review Part 2.4: Train & Test Models [Naive Bayes]

Lecture 10: Classification in MLlib Code Review Part 2.5: Train & Test Models [Linear SVM]

Lecture 11: Classification in MLlib Code Review Part 2.6: Train & Test Models[Decision Tree]

Lecture 12: Classification in MLlib Code Review Part 2.7: Train & Test Models[Random Forest]

Lecture 13: Classification in MLlib Code Review Part 2.8: Train & Test Models [GBT]

Lecture 14: A little something to keep you going….

Lecture 15: BONUS: Add loop functions to your training and evaluation script

Lecture 16: BONUS: Leverage MLflow to better track and manage your results

Lecture 17: Classification Project

Lecture 18: Remember to be creative with this project!

Lecture 19: Classification Project Solution

Chapter 6: Natural Language Processing in MLlib

Lecture 1: Introduction to Natural Language Processing

Lecture 2: Natural Language Processing Concept Review [Part 1: Feature Transformers]

Lecture 3: Natural Language Processing Concept Review [Part 2: Feature Extractors]

Lecture 4: A little something to keep you going….

Lecture 5: Natural Language Processing Code Along Activity Part 1: Data Prep

Lecture 6: Natural Language Processing Code Along Activity Part 2: Vectorize, Train & Eval

Lecture 7: Natural Language Processing Project

Lecture 8: Natural Language Processing Project Solution

Lecture 9: A little something to keep you going….

Chapter 7: Regression in MLlib

Lecture 1: Regression in MLlib Concept Review

Lecture 2: Regression in MLlib Code Review Introduction

Lecture 3: Regression in MLlib Code Review Part 1: Data Prep

Lecture 4: Regression in MLlib Code Review Part 2.0: Linear Regression

Lecture 5: A little something to keep you going….

Lecture 6: Regression in MLlib Code Review Part 2.1: Decision Tree Regression

Lecture 7: Regression in MLlib Code Review Part 2.2: Random Forest Regression

Lecture 8: Regression in MLlib Code Review Part 2.3: Gradient Boosted Tree Regression

Lecture 9: A little something to keep you going….

Lecture 10: BONUS: Add loop functions to your regression training and evaluation script

Lecture 11: Regression Project

Lecture 12: And finally… have FUN with this project and LOVE what you do!

Lecture 13: Regression Project Solution Code Along Activity

Instructors

Layla AI
Seasoned Data Scientist Consultant & Passionate Instructor

Rating Distribution

1 stars: 11 votes
2 stars: 9 votes
3 stars: 67 votes
4 stars: 204 votes
5 stars: 511 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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