Data Science & Big Data Analytics Logo
Powered By

EMC Logo

Data Science & Big Data Analytics Training

Live Online & Classroom Enterprise Training

Powered By

EMC Logo

Data science is all about Data cleansing, preparation and analysis and Big data Analytics is used to analyze insights which can lead to better decisions and strategic moves. So the combination of these two makes this course an astonishing way to become a Big data analyst with a pinch of Data science.

Looking for a private batch ?

REQUEST A CALLBACK
Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

PDP BG 1
SpringPeople Logo

What is Data Science & Big Data Analytics training about?

This course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It establishes a baseline of skills that can be further enhanced with additional training and real-world experience. The course provides an introduction to big data and a Data Analytics Lifecycle Process to address business challenges that leverage big data. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools including MapReduce and Hadoop. The course has extensive labs throughout to provide practical opportunities to apply these methods and tools to real-world business challenges and includes a final lab in which students address 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 Proven\ Professional Data Scientist Associate EMCDSA) certification exam.

What are the objectives of Data Science & Big Data Analytics training?

At the end of Data Science and Big Data Analytics training course, participants will be able to:

  • Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by
  • Deploy the Data Analytics Lifecycle to address big data analytics projects
  • Reframe a business challenge as an analytics challenge
  • Apply appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
  • Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
  • Use tools such as: R and RStudio, MapReduce/Hadoop, in-database analytics, Window and MADlib functions
  • Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst
Available Training Modes

Live Online Training

Classroom Training

 

PDP BG 2

Who is Data Science & Big Data Analytics training for?

  • Anyone who wants to add Data Science & Big Data Analytics skills to their profile
  • Teams getting started on Data Science & Big Data Analytics projects
  • What are the prerequisites for Data Science & Big Data Analytics training?

    • 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 e... Read More

    Course Outline

    • 1. Introduction to Big Data Analytics
      • Big Data Overview
      • State of the Practice in Analytics
      • The Data Scientist
      • Big Data Analytics in Industry Verticals
    • 2. Data Analytics Lifecycle
      • Discovery
      • Data Preparation
      • Model Planning
      • Model Building
      • Communicating Results
      • Operationalizing
    • 3. Review of Basic Data Analytic Methods Using R
      • Using R to Look at Data - Introduction to R
      • Analyzing and Exploring the Data
      • Statistics for Model Building and Evaluation
    • 4. Advanced Analytics - Theory And Methods
      • K Means Clustering
      • Association Rules
      • Linear and Logistic Regression
      • Naive Bayesian Classifier
      • Decision Trees
      • Time Series Analysis
      • Text Analysis
    • 5. Advanced Analytics - Technologies and Tools
      • Analytics for Unstructured Data - MapReduce and Hadoop
      • The Hadoop Ecosystem:In-database Analytics - SQL Essentials
      • Advanced SQL and MADlib for In-database Analytics
    • 6. The Endgame, or Putting it All Together
      • Operationalizing an Analytics Project
      • Creating the Final Deliverables
      • Data Visualization Techniques
      • Final Lab Exercise on Big Data Analytics

    Who is the instructor for this training?

    The trainer for this Data Science & Big Data Analytics has extensive experience in this domain, including years of experience training & mentoring professionals.

    Reviews