Data Science Introductory Training

Data Science Introductory Training

There is great demand for professionals who can turn data analysis into competitive advantage for their organizations. This learning path will train you to use development frameworks such as Hadoop, Spark & R to process huge amounts of data and thrive in your big data career.

The field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment.

 

2 Days Program

Course Outline

Day 1

  • Introduction to Data Science

    • What is Data?
    • Types of Data
    • What is Data Science?
    • Knowledge Check
    • Lab Activity

    Data Science Workflow

    • Data Gathering
    • Data Preparation & Cleansing
    • Data Analysis – Descriptive, Predictive & Prescriptive

    Data Visualisation & Model

    • Deployment
    • Knowledge Check

    Life of Data Scientist

    • What is a Data Scientist

    Data Scientist Roles

    • What Does a Data Scientist Look Like?
    • T-Shaped Skillset
    • Data Scientist Roadmap
    • Data Scientist Education Framework
    • Thinking like a Data Scientist
    • Knowns & Unknowns
    • Demand & Opportunity
    • Labour Market
    • Applications of Data Science
    • Data Science Principles
    • Data-Driven Organisation
    • Developing Data Products
    • Knowledge Check

    Data Gathering

    • Obtaining Data from Online Repositories

    Importing Data from Local File Formats (json, xml)

    • Importing Data Using Web API
    • Scraping Website for Data
    • Knowledge Check

Day 2

      • Data Science Prerequisites

        • Probability and Statistics
        • Linear Algebra
        • Calculus
        • Combinatorics
        • Programming

        Beginning Databases

        • Types of Databases
        • Relational Databases
        • NoSQL
        • Hybrid Databases
        • Lab Activity

        Structured Query Language (SQL)

        • Performing CRUD (Create, Retrieve, Update, Delete)
        • Designing a Real World Database
        • Normalising a Table
        • Knowledge Check
        • Lab Activity

        Introduction to Phyton

        • Basics of Python Language
        • Functions and Packages
        • Python Lists
        • Functional Programming in Python
        • Numpy & Scipy
        • iPython
        • Knowledge Check

        Lab Activity: Exploring Data Using Python

FAQ

Who should take the course?

  • Software developers
  • IT managers
  • Service management professionals
  • Technology Managers
  •  
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