Course Duration : 5 Days

Advanced Methods in Data Science and Big Data Analytics

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.

  1. 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

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.

Module 1: MapReduce and Hadoop

  • ▬  Lesson 1: The MapReduce Framework
  • ▬  Lesson 2: Apache Hadoop
  • ▬  Lesson 3: Hadoop Distributed File System
  • ▬  Lesson 4: YARNModule 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: SparkModule 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 ModelingModule 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 ToolsModule 5: Data Science Theory and Methods
  • ▬  Lesson 1: Simulation
  • ▬  Lesson 2: Random Forests
  • ▬  Lesson 3: Multinomial Logistic RegressionModule 6: Data Visualization
  • ▬  Lesson 1: Perception and Visualization
  • ▬  Lesson 2: Visualization of Multivariate Data