Big Data

Syllabus

Module 1 - What is Big Data?

Characteristics of Big Data
What are the V’s of Big Data?
The Impact of Big Data

Module 2 - Big Data - Beyond the Hype

Big Data Examples
Sources of Big Data
Big Data Adoption

Module 3 - The Big Data and Data Science

The Big Data Platform
Big Data and Data Science
Skills for Data Scientists
The Data Science Process

Module 4 - Big Data Use Cases

Big Data Exploration
The Enhanced 360 View of a Customer
Security and Intelligence
Operations Analysis

Module 5 - Processing Big Data

Ecosystems of Big Data
The Hadoop Framework

Through narrated lecture, recorded demonstrations, and hands-on exercises,you will learn how to:
How to use Apache Spark to run data science and machine learning workflows at scale
How to use Spark SQL and DataFrames to work with structured data
How to use MLlib, Spark’s machine learning library
How to use PySpark, Spark’s Python API
How to use sparklyr, a dplyr-compatible R interface to Spark
How to use Cloudera Data Science Workbench (CDSW)
How to use other Cloudera platform components including HDFS, Hive,
Impala, and Hue