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EMCADSA : Advanced Methods in Data Science and Big Data Analytics

Course and Certificate TRAINING AND CERTIFICATION
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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

Audience Profile

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

Prerequisities

  • 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

+- Module 1: MapReduce and Hadoop Click to collapse
+- Module 2: Hadoop Ecosystem and NoSQL Click to collapse
+- Module 3: Natural Language Processing Click to collapse
+- Module 4: Social Network Analysis Click to collapse
+- Module 5: Data Science Theory and Methods Click to collapse
+- Module 6: Data Visualization Click to collapse
RM12,250.00(+RM735.00 Tax)
* Training Dates:

* Price excludes Exam

Course Information
EXAM CODE
E20-065
DURATION
5 Days
CERTIFICATION
EMC Data Scientist Specialist
Schedule

22-26 May 2023
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