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Advanced Methods in Data Science and Big Data Analytics

Learn to use several open source tools to address big data challenges.

GK# 4192

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

Delivery Format Options

  • Classroom Live

    Classroom Live

    Receive face-to-face instruction at one of our training center locations.

    From

    $6760 CAD

    5 day

  • Private Group Training

    Private Group Training

    Train your entire team in a private, coordinated professional development session at the location of your choice.

    Receive private training for teams online and in-person.

Request a date or location for this course.

What You'll Learn

  • 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

Prerequisites

Who Needs To Attend

  • 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 Outline

Download Course Outline