3.85 out of 5
3.85
274 reviews on Udemy

Apache Spark 2.0 + Python : DO Big Data Analytics & ML

Project Based, Hands-on Practices, Spark SQL, Spark Streaming, Real life Full cycle Project
Instructor:
V2 Maestros, LLC
1,882 students enrolled
English [Auto-generated]
Acquire Knowledge of Apache Spark 2.0 fundamentals and architecture
Write Spark 2.0 scripts for Transformations, actions, Spark SQL and Spark Streaming
Execute Machine Learning / Data Science algorithms
Solve real world data problems with Apache Spark 2.0
Handle interviews for Apache Spark 2.0 confidently and get jobs

Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This course teaches you Apache Spark 2.0 with Python, trains you in building Spark Analytics and machine learning programs and helps you practice hands-on with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable.

Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages.

The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you

  • Learn solid fundamentals of the domain
  • See demos, train and execute solid examples
  • Practice hands-on and validate it with solutions provided
  • Apply knowledge you acquired in an end-to-end real life project

Taught by an expert in the field, you will also get prompt response to your queries and excellent support from Udemy.

Kick-start your learning

1
Meet Your Mentor

About the mentor and setting expectations.

2
Your learning Process - Learn, Train, Practice and Apply

Introduction to the Learn-Train-Practice-Apply methodology

3
Your Course Guide - Pathway to success
4
Resource Bundle - Code and Data

Introduction to Apache Spark

1
Start your Spark engines

Introduction to Apache Spark

2
Setup your Spark / Python environment

Download Apache Spark and setup your Python environment to use Spark

3
Run your first Spark Program !

Run your first Spark program - get your hands dirty !

4
Apache Spark eco-system

Overview of Spark and its various modules and libraries

5
RDD : The foundation of Spark

Overview of Resilient Distributed Data Sets (RDD)

6
Spark Architecture - How it all works.

Spark cluster architecture and scalability

7
Spark Project work flow - How it gets done.

Various steps/stages in a Spark project and how things happen.

8
Spark Architecture

Spark Programming with Python

1
Loading and Storing Data

How you load external data into Spark and how data gets saved.

2
Loading and Storing Data
3
@Practice() Loading and Storing Data
4
Transformations - Change how data looks
5
Transformations
6
@Practice() Transformations
7
Actions - Extract insights from Data
8
Actions
9
@Practice() Actions
10
Key-Value RDDs
11
Key-Value RDDs
12
@Practice() Key-Value RDDs
13
Advanced Spark

Broadcast variables, accumulators, partitioning and persistence

14
Advanced Spark - Enhanced Capabilities
15
@Practice() Advanced Spark

Spark SQL

1
Spark SQL Data Frames - the new era
2
SQL Data Frames
3
@Practice() SQL Data Frames
4
Temp Tables / Views - Easy querying
5
Temp Tables / Views
6
@Practice() Temp Tables/ Views

Spark Streaming

1
Spark Streaming - real time data processing
2
Spark Streaming Architecture - how it works.
3
Spark Streaming

Machine Learning with Spark

1
Types of Analytics - simple to predictive
2
Types of Machine Learning
3
Analyzing results and Errors
4
Spark ML Concepts - new data types
5
Linear Regression - fit to a line
6
Linear Regression Use Case
7
Decision Trees Classification
8
Decision Trees Use Case
9
Principal Component Analysis
10
Random Forest Classification
11
Random Forests and PCA Use Case
12
Text Pre-processing with TF-IDF
13
Naive Bayes Classification
14
Naive Bayes and Text Pre-processing Use Case
15
K-Means Clustering - grouping similar items
16
K-Means Clustering Use Case
17
Recommendation Engines
18
Recommendation Engines Use Case

APPLY : Your Course Challenge Project

1
Real world problem Statement - Credit Card defaulters
2
Hints to help you with the project
3
Final Solution Review - we did it !

The final solution is available in the resource bundle ( APPLY Project *.py)

Conclusion

1
Closing Remarks
2
BONUS Lecture - Your next steps & Discount coupons
You can view and review the lecture materials indefinitely, like an on-demand channel.
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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Includes

7 hours on-demand video
11 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion