Duration
5 days
Course Description
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.
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.
Prerequisites
- Completion of the Data Science and Big Data Analytics course
- Proficiency in at least one programming language such as Java or Python
At Course Completion
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 Outline
The content of this course is designed to support the course objectives.
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.
This is a version-specific reference outline. Confirm the current syllabus and delivery arrangements with EIEIO.