Advanced Methods in Data Science and Big Data Analytics

Course Details
Code: 4192
Tuition (USD): $5,000.00 • Classroom (5 days)

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. With a technology-neutral approach, this course utilizes several open-source tools to address big data challenges.

Suggested Audience

Skills Gained

  • MapReduce functionality
  • NoSQL databases and Hadoop Ecosystem tools for analyzing large-scale, unstructured data sets
  • 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

Who Can Benefit

  • Aspiring data scientists
  • Data analysts that have completed the associate level Data Science and Big Data Analytics course
  • Computer scientists wanting to learn MapReduce and methods for analyzing unstructured data such as text.

Course Details

1. MapReduce and Hadoop

  • The MapReduce Framework
  • Apache Hadoop
  • Hadoop Distributed File System
  • YARN

2. Hadoop Ecosystem and NoSQL

  • Hadoop Ecosystem
  • Pig
  • Hive
  • NoSQL--Not only SQL
  • HBase
  • Spark

3. Natural Language Processing

  • Introduction to NLP
  • Text Preprocessing
  • TFIDF
  • Beyond Bag of Words
  • Language Modeling
  • POS Tagging and HMM
  • Sentiment Analysis and Topic Modeling

4. Social Network Analysis

  • Introduction to SNA and Graph Theory
  • Most Important Nodes
  • Communities and Small World
  • Network Problems and SNA Tools

5. Data Science Theory and Methods

  • Simulation
  • Random Forests
  • Multinomial Logistic Regression

6. Data Visualization

In addition to lecture and demonstrations, this course includes labs designed to give you practical experience.