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Introduction to Data Science – Analytics & Visualization

Who Should Attend?

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Duration: 2 Day

As the world entered the era of big data, one of the main focuses is to process the huge amount of data. Data Science is the secret sauce for the focus – turning Hollywood sci-fi movies into reality by Data Science. Therefore, it is crucial to understand what is Data Science and how can it add value to your business. This course will introduce the two main phases in the Data Science cycle – Analytics and Visualization, targeting on participants from any background. A few hands-on will be conducted to the participants for better experience on analytics and visualization.

  • Understand and apply statistical concepts into business analytics
  • Explore and prepare data for business analytics
  • Apply machine learning approaches to extract useful business information from data
  • Feel competent in producing visual dashboards independently
  1. Introduction
    • Data Science Overview
    • Journey of Data Driven Insights
    • Data and Variables
    • Data Cleaning – Data Errors, Missing Values and Outliers
    • Descriptive Statistics
  2. Basics of Data Visualization
    • Introduction to Visualization
    • Supported Files and Database Connections
    • Appropriate Chart Types for Presentation and Analysis
    • Data Visualization Best Practices
  3. Functions and Analysis
    • Working with Data: Excel and Database
    • Data Treatment Features
    • Techniques in Data Transformation, Join Table and Union
    • Perform User Defined Calculation in Visualization Tool
    • Advanced Calculation with Table Calculation Functions
    • Working with Dates and Time
    • Data Filtering, Formatting, Sorting, Set and Grouping
    • Analytics Functions
  4. Dashboard Building
    • Introduction to dashboard building elements
    • Types of dashboards for different target users
    • Creating a dashboard for visual analysis
    • Using actions and guided analytics function
    • Mini project to build a complete management dashboard
  5. Introduction to Analytics
    • Overview of Machine Learning
    • Supervised Learning & Unsupervised Learning
    • CRISP-DM
    • Predictive Analytics – Classification
  6. Advanced Data Visualization
    • Advanced Dashboard with Analytics

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