MR-1CP-ETAAMUSD: Advanced Methods in Data Science and Big Data Analytics

Overview

This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas cover Hadoop (including Pig, Hive, and HBase), Natural Language Processing, Social Network Analysis, Simulation, Random Forests, Multinomial Logistic Regression, and Data Visualization. Taking an “Open” or technology-neutral approach, this course utilizes several open-source tools to address big data challenges.

Skills Covered

Upon successful completion of this course, participants should be able to:

Develop and execute MapReduce functionality
Gain familiarity with NoSQL databases and Hadoop Ecosystem tools for analyzing large-scale, unstructured data sets
Develop a working knowledge of Natural Language Processing, Social Network Analysis, and Data Visualization concepts
Use advanced quantitative methods and apply one of them in a Hadoop environment
Apply advanced techniques to real-world datasets in a final lab

Prerequisites

Completion of the Data Science and Big Data Analytics course
Proficiency in at least one programming language such as Java or Python

Target Audience

This course is intended for aspiring Data Scientists, data analysts that have completed the associate level Data Science and Big Data Analytics course, and computer scientists wanting to learn MapReduce and methods for analyzing unstructured data such as text.