Dell - Data Science & Big Data Analytics

$3,120.00 USD

5 Days


Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

This 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. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real world business challenges by a practicing data scientist. The course takes an Open, or technology-neutral approach, and includes a final lab which addresses a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle. The course prepares the student for the Dell EMC Proven Professional Data Scientist Associate certification exam.

Course Objectives

  • 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
  • Who Should Attend?

  • Managers of teams of business intelligence, analytics, and big data professionals
  • Current Business and Data Analysts looking to add big data analytics to their skills.
  • Data and database professionals looking to exploit their analytic skills in a big data environment
  • Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of data science and big data
  • Individuals seeking to take advantage of the EMC ProvenTM Professional Data Scientist Associate (EMCDSA) certification
    • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
    • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
    • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
    • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
    • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
    • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

    Course Prerequisites

  • A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course
  • Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open source statistical tool and programming
  • Experience with SQL
  • Agenda

    1 - Introduction to Big Data analytics

    • Big Data and its characteristics Lesson
    • Business value from Big Data
    • Data scientist

    2 - Data Analytics Lifecycle

    • Data analytics lifecycle overview
    • Discovery phase
    • Data preparation phase
    • Model planning phase
    • Model building phase
    • Communicate results phase
    • Operationalize phase

    3 - Basic data analytics methods using R

    • Introduction to the R programming language
    • Analyzing and exploring data
    • Statistics for model building and evaluation

    4 - Advanced analytics theory and methods

    • Introduction to advanced analytics—theory and methods
    • K-means clustering
    • Association rules
    • Linear regression
    • Logistic regression
    • Text analysis
    • Naïve Bayes
    • Decision trees
    • Time series analysis

    5 - Advanced analytics—technology and tools

    • Introduction to advanced analytics—technology and tools
    • Hadoop ecosystem
    • In-database analytics SQL essentials
    • Advanced SQL and MADlib

    6 - Putting it all together

    • Preparing to operationalize
    • Preparing project presentations
    • Data visualization techniques

    Get in touch to schedule training for your team
    We can enroll multiple students in an upcoming class or schedule a dedicated private training event designed to meet your organization’s needs.


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