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Data Science And Big Data Analytics Course

Gain practical knowledge in analytic methods, technology and tools

Develop Skills to Harness the Explosive Growth of Data

Get hands-on experience with Big Data Analytics to identify opportunities and gain insights that lead to competitive business advantages.

 

New! 8-week Online Course begins March 30, 2020.

Introductory price: 1400 USD, 1400 GBP

Course Overview

The course provides grounding in basic and advanced analytic methods as well as an introduction to Big Data Analytics technology and tools, including MapReduce and Hadoop. Recorded lab exercises offer opportunities for students to understand how these methods and tools may be applied to real-world business challenges by a practicing data scientist.

Course Objectives

Upon successful completion of this course, participants should be able to:

  • Immediately participate as a data science team member
  • Work with large data sets and generate insights
  • Build predictive and classification models
  • Manage a data analytics project through the entire lifecycle

Data Science skills are essential

Demand for data-savvy professionals with backgrounds in analytics, data visualization, data engineering, business intelligence and data science is increasing rapidly. Dell Technologies' Data Science and Data Analytics course can help consistently apply the tools, tips, and techniques learned. 

Course Benefits

This course provides practical experience for participants through lecture videos, recorded lab demonstrations and study materials (PDF).

Interactive user discussion forums - extemely useful in buliding a community of learners - are available.

Validate your skills and get certified as a Dell Technologies Proven Professional.

 
 

Course Outline

This 8-week course provides practical foundation-level training that enables immediate and effective participation in Big Data and other analytics projects. It includes an introduction to Big Data and the Data Analytics Lifecycle to address business challenges that leverage Big Data.

Week 1: Intro to Big Data Analytics and Data Analytics Lifecycle

  • Big Data Characteristics
  • Business Value from Big Data
  • Data Scientist
  • Intro to Data Analytics Lifecycle
  • Conclusion (Lifecycle Continuation)
  • Lab: Intro to the Lab Environment

Week 5: Advanced Analytics Theory and Methods, Part 3

  • Decision Trees: Algorithm, Use Cases, Diagnostics
  • Lab: Decision Trees
  • Time-Series Analysis: Algorithm, Use Case, Diagnostics
  • Lab: Time Series Analysis

Week 2: Basic Data Analytics Methods Using R

  • Intro to R Programming Language
  • R Studio Demo
  • Lab: Intro to R Programming
  • Analyzing and Exploring Data
  • Lab: Basic Statistics, Visualization and Hypothesis Testing

Week 6: Advanced Analytics Technology and Tools, Part 1

  • Intro to Advanced Analytics Technology and Tools
  • MapReduce
  • Hadoop Ecosystem
  • Lab: Hadoop, HDFS, Pig, Hive, and Spark

Week 3: Advanced Analytics Theory and Methods, Part 1

  • Intro to Advanced Analytics Theory and Methods
  • K-Means Clustering: Algorithm, Use Case, Diagnostics
  • Lab: K-Means Clustering
  • Association Rules: Algorithm, Use Cases, Diagnostics
  • Lab: Association Rules
  • Linear Regression: Algorithm, Use Cases, Diagnostics
  • Lab: Linear Regression

Week 7: Advanced Analytics Technology and Tools, Part 2

  • In-Database Analytics: SQL Essentials
  • Lab: In-Database Analytics
  • Advanced SQL and MADlib

Week 4: Advanced Analytics Theory and Methods, Part 2

  • Logistic Regression: Algorithm, Use Case, Diagnostics
  • Lab: Logistic Regression
  • Text Analytics - Methods and Metrics
  • Naïve Bayes: Algorithm, Use Cases and Diagnostics
  • Lab: Text Analytics and Naïve Bayes Classifier

Week 8: Putting It All Together

  • In-Database Analytics: SQL Essentials
  • Lab: In-Database Analytics
  • Advanced SQL and MADlib
 
CONTACT US

Engage your local Dell Learning Account Manager for local pricing information and scheduling classes. Visit us online at education.DellEMC.com or call +1 888 362 8764 (US).