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Data Science Essentials

Who Should Attend?

DATA ANALYST and DATA SCIENTIST

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Duration: 3 Days
Training Date
  • 6 – 8 July 2020 (Penang)
  • 24 – 26 August 2020 (KL)
  • 19 – 21 October 2020 (Penang)
  • 23 – 25 November 2020 (KL)

Whether you are in finance, operations, sales & marketing or planning, you may be in touch with millions of data points every day without being aware of how to derive valuable information from this data. Business Analytics thus finds favor as it utilizes tools and techniques like data mining, pattern matching, data visualizations and predictive modeling to predict and optimize outcomes and derive value from the data. Equipped with this useful information, organizations can compete better in cut-throat markets both locally and globally.

This training is ideal for both researchers who focus on algorithms, and for professionals who intend to work on data analytics applications such as critical product analysis, targeted marketing, customer lifecycle management, social media analytics, fraud detection, and inventory management.

  1. Data Science and Analytics
    • Introduction to Data Science
    • Introduction to Analytics
    • Statistical Inference and Concepts
    • Introduction to Statistical Learning
  2. Data Management and Visualization
    • Data and Variables
    • Data Cleaning – Data Errors, Missing Values and Outliers
    • Descriptive Statistics
    • Exploratory Data Analysis (EDA)
    • Visualization
  3. Introduction to Machine Leaning
    • Overview of Machine Learning
    • Supervised Learning & Unsupervised Learning
    • Regression & Classification
  4. Unsupervised Learning
    • Clustering
    • Normalization
    • Association Analysis
  5. Regression Analysis
    • Linear Regression
    • Dummy Variables
    • Models Selection
    • Results Interpretation
  6. Classification
    • Data Validation
    • Performance Evaluation
    • Predictive Models
    • Models Selection
    • Results Interpretation
    • Factor Analysis

Introduction to Data Analytics

  • The Variety of Data
  • Types of Analytics
  • Importance of Data to Enterprises
  • Identifying Data Opportunities
  • Best Practices to Initiate Data Analytics Initiatives

Hands-On Case Study

  1. Unsupervised Learning: Clustering
    • City Clustering
  2. Unsupervised Learning: Association Analysis
    • Market Basket Analysis
  3. Regression Analysis
    • House Price Prediction
  4. Classification
    • Fraud Detection

Register Now

Drop us your entry if you are interested to join this course.

 


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