Kompetensi

Data Science

Data Science Essentials

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

Course Objective

1. Memahami dan menerapkan konsep statistik 
2. Menerokai dan menyediakan data untuk analisis 
3. Menghuraikan hubungan antara pemboleh ubah dan membina model ramalan 
4. Menerapkan pendekatan Machine Learning untuk mengekstrak maklumat yang berguna daripada data 

Course Outcomes

1. Understand and apply statistical concepts into business analytics
2. Explore and prepare data for business analytics
3. Describe the relationship between variables and construct predictive model
4. Apply machine learning approaches to extract useful business information from data

Course Outline

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