Data Science Comprehensive Training

Data Science Comprehensive Training

Your working lives are flooded by large amounts of information, but not all of them are useful data. Therefore, it is essential for us to learn how to apply data science into every aspect of our daily lives from personal finances, reading and lifestyle habits, to making informed business decisions. In this course you will learn how to leverage on data to ease life or unlock new economic value for a business.

This course is a hands-on guided course for you to learn the concepts, tools, and techniques that you need to begin learning data science. We will cover the key topics from data science to Big Data, and the processes of gathering, cleaning and handling data. This course has a good balance between theory and practical applications, and key concepts are taught using case study references. Upon completion, participants will be able to perform basic data handling tasks, collect and analyse data, and present them using industry standard tools.

5 Days Workshop

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

    What are the course objectives?

    • Identify the appropriate model for different data types.
    • Create your own data process and analysis workflow.
    • Define and explain the key concepts and models relevant to data science.
    • Differentiate key data ETL process, from cleaning, processing to visualisation.
    • Implement algorithms to extract information from dataset.
    • Apply best practices in data science and become familiar with standard tools.
    • Data Visualisation & Model Deployment
    • Knowledge Check

    Life of a 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 Python

        • Basics of Python Language
        • Functions and Packages
        • Python Lists
        • Functional Programming in Python
        • Numpy & Scipy
        • iPython
        • Knowledge Check
        • Lab Activity: Exploring Data Using Python

Day 3

  • Data Preparation & Cleansing

    • Extract, Transform & Load (ETL) – Pentaho, Talend, etc.
    • Data Cleansing with OpenRefine
    ●   Aggregation, Filtering, Sorting, & Joining
    Knowledge Check
    Lab Activity
    Data Quality
    ●   Raw vs Tidy Data
    ●   Key Features of Data Quality
    ●   Maintenance of Data Quality
    Data Profiling
    • Data Completeness & Consistency
    Exploratory Data Analysis (Descriptive)
    What is EDA?
    Goals of EDA
    ●   The Role of Graphics
    Handling Outliers
    Dimension Reduction

    Introduction to R

    • Packages for Data Import, Wrangling, & Visualization
    • Conditionals & Control Flow
    • Loops & Functions
    Knowledge Check
    Lab Activity
    ●   Lab: Exploring Data Using R

Day 4

  • Machine Learning (Predictive)

    ·         Bayes’ Theorem

    ·         Information Theory

    ·         Natural Language Processing

    ·         Statistical Algorithms

    ·         Stochastic Algorithms


    Introduction to Text Mining

    ●   What is Text Mining?

    Natural Language Processing

    ●   Pre-Processing Text Data

    ●   Extracting Features from Documents

    Using BeautifulSoup

    Measuring Document Similarity

    Knowledge Check

    Lab Activity


    Supervised, Unsupervised, & Semi-

    Supervised Learning

    ●   What is Prediction?

    ●   Sampling, Training Set, & Testing Set

    ●   Constructing a Decision Tree

    ●   Knowledge Check

    ●   Lab Activity

Day 5

  • Data Visualisation

    ●   Choosing the Right Visualisation

    ●   Plotting Data Using Python Libraries

    ●   Plotting Data Using R

    ●   Using Jupyter Notebook to Validate Scripts

    ●  Knowledge Check

    ●  Lab Activity

    Data Analysis Presentation

    ●  Using Markdown Language

    ●   Converting Your Data Into Slides

    ●  Data Presentation Techniques

    ●   The Pitfall of Data Analysis

    ●  Knowledge Check

    ●  Lab Activity

    ●   Group Presentation Lab: Mini Project

    Big Data Landscape

    ●   What is Small Data?

    ●   What is Big Data?

    ●   Big Data Analytics vs Data Science

    ●   Key Elements in Big Data

    ●   Extracting Values from Big Data

    ●   Challenges in Big Data

    Big Data Tools & Applications

    ●   Introducing Hadoop Ecosystem

    ●   Cloudera vs Hortonworks

    ●   Real World Big Data Applications

    ●   Knowledge Check

    ●   Group Discussion


Who should take the course?

  • This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.

    All participants should have a basic understanding of data, relations, and mathematics.

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