Advanced Methods in Data Science and Big Data Analytics (AMDSBDA)

 

Course 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.

Who should attend

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.

Prerequisites

Course Objectives

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

Course Content

Module 1: MapReduce and Hadoop
  • Lesson 1: The MapReduce Framework
  • Lesson 2: Apache Hadoop
  • Lesson 3: Hadoop Distributed File System
  • Lesson 4: YARN
Module 2: Hadoop Ecosystem and NoSQL
  • Lesson 1: Hadoop Ecosystem
  • Lesson 2: Pig
  • Lesson 3: Hive
  • Lesson 4: NoSQL - Not Only SQL
  • Lesson 5: HBase
  • Lesson 6: Spark
Module 3: Natural Language Processing
  • Lesson 1: Introduction to NLP
  • Lesson 2: Text Preprocessing
  • Lesson 3: TFIDF
  • Lesson 4: Beyond Bag of Words
  • Lesson 5: Language Modeling
  • Lesson 6: POS Tagging and HMM
  • Lesson 7: Sentiment Analysis and Topic Modeling
Module 4: Social Network Analysis
  • Lesson 1: Introduction to SNA and Graph Theory
  • Lesson 2: Most Important Nodes
  • Lesson 3: Communities and Small World
  • Lesson 4: Network Problems and SNA Tools
Module 5: Data Science Theory and Methods
  • Lesson 1: Simulation
  • Lesson 2: Random Forests
  • Lesson 3: Multinomial Logistic Regression
Module 6: Data Visualization
  • Lesson 1: Perception and Visualization
  • Lesson 2: Visualization of Multivariate Data

In addition to lecture and demonstrations, this course includes labs designed to allow practical experience for the participant.

Prices & Delivery methods

Online Training

Duration
5 days

Price (excl. tax)
  • US$ 5,000.—
Classroom Training

Duration
5 days

Price (excl. tax)
  • Germany: US$ 5,000.—
 

Schedule

Instructor-led Online Training:   Course conducted online in a virtual classroom.

English

Time zone: Central European Time (CET)   ±1 hour

Online Training Time zone: Central European Time (CET)