3.8
stars
78 ratings

Karthik Muthuraman +1 more instructor

9,725 already enrolled

Offered By

IBM Skills Network

About this Course

47,641 recent views

Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. 
In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering.

The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case.

NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks.  The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.

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Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Beginner Level

Computer and IT literacy.

Approx. 7 hours to complete

English

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What you will learn

Explain how streaming data and Spark Structured Streaming empower machine learning and AI tasks.
Define graph theory, describe Apache Spark GraphFrames, and identify data suitable for GraphFrames.
Describe how ETL processes work with Apache Spark and machine learning and extend that knowledge to Spark MLlib capabilities and related benefits.
Explain supervised learning, unsupervised learning, and clustering, and explain how to use the k-means clustering algorithm with Spark MLlib.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Computer and IT literacy.

Approx. 7 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

3.46/5 (15 Ratings)

Karthik Muthuraman

Software Engineer (Machine Learning)

IBM Center for Open-Source Data and AI Technologies

25,146 Learners

2 Courses

Romeo Kienzler

Chief Data Scientist, Course Lead

IBM Watson IoT

496,566 Learners

9 Courses

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Spark for Data Engineering

In this first of two modules, learn what streaming data is and get the essential knowledge to use Spark for Structured Streaming. Learn about data sources, streaming output modes, and supported data destinations. Learn about data operations considerations and discover how Spark Structured streaming listeners and checkpointing benefit streaming data processing. Discover how graph theory works with streaming data. You’ll gain insights into the advantages that Apache Spark GraphFrames offers and learn what qualities make data suitable for GraphFrames processing. Then, explore ETL and learn how to use Apache Spark for data extraction, transformation, and loading, put your newfound knowledge to practice, and gain practical, real-world skills in the ETL for Machine Learning Pipelines hands-on lab.

2 hours to complete

4 videos (Total 25 min), 2 readings, 3 quizzes

Week2

Week 2

3 hours to complete

SparkML

This module demystifies the concepts and practices related to machine learning using SparkML and the Spark Machine learning library. Explore both supervised and unsupervised machine learning. Explore classification and regression tasks and learn how SparkML supports these machine learning tasks. Gain insights into unsupervised learning, with a focus on clustering, and discover how to apply the k-means clustering algorithm using the Spark MLlib. Complete this learning with the lab that solidifies your learning and gain real-world experience with Spark ML.

3 hours to complete

3 videos (Total 13 min), 1 reading, 4 quizzes

Week3

Week 3

3 hours to complete

Final Project

3 hours to complete

3 readings

Reviews

3.8

23 reviews

5 stars
50.63%
4 stars
16.45%
3 stars
11.39%
2 stars
7.59%
1 star
13.92%

TOP REVIEWS FROM DATA ENGINEERING AND MACHINE LEARNING USING SPARK

by RSJul 1, 2022

Fantastic delivery.

The instructions in the lab could be clearer.

View all reviews

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Data Engineering and Machine Learning using Spark

About this Course

What you will learn