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. This training prepares the learner for Dell Technologies Proven Professional advanced analytics specialist-level certification exam (E20-065).
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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.
What will you learn
- 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 Structure
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 Module In addition t
- Lecture and demonstrations, the classroom options include handson lab exercises designed to allow practical experience for the participant. The
on-demand course provides recordings of the lab exercises.
Prerequisites
Completion of the Data Science and Big Data Analytics course • Proficiency in at least one programming language such as Java or Python
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