Big Data Analytics for Technical Professionals

Big Data Analytics for Technical Professionals

Intermediate 10-day outline

Welcome to the Intermediate level of "Big Data Analytics for Technical Professionals!" This 10-day program is structured to further refine your analytics skills, delving into advanced topics like inferential statistics, A/B testing, and time series forecasting. Just like the beginner's course, we've tailored this intermediate module with a Malaysian context in mind, incorporating real-world examples and case studies from various industries in Malaysia. Expect engaging hands-on projects and interactive lessons that will take your analytics game to the next level.

Inferential Statistics

(2 Days)

Move beyond basic descriptive statistics and dive into inferential statistics. This 2-day section focuses on hypothesis testing, confidence intervals, and p-values, among other topics.

Learning Outcomes

• Understand the basic principles of inferential statistics
• Conduct hypothesis testing
• Calculate and interpret confidence intervals and p-values

Prerequisites

• Basic understanding of Python programming
• Fundamentals of descriptive statistics

Outline

• Day 1: Introduction to Inferential Statistics
  • Sampling Distribution
  • Confidence Intervals
  • Hands-On: Applying Inferential Statistics to Malaysian Census Data
• Day 2: Hypothesis Testing and p-values
  • Null and Alternative Hypotheses
  • p-value and Significance Level
  • Mini Project: Testing Hypotheses in Malaysian eCommerce

A/B Testing

(2 Days)

A/B testing is crucial for making data-informed decisions. In this 2-day module, learn how to set up, run, and analyze A/B tests, focusing on practical applications in a Malaysian context.

Learning Outcomes

• Understand the fundamentals of A/B testing
• Set up and run an A/B test
• Analyze and interpret A/B test results

Prerequisites

• Basic understanding of inferential statistics
• Familiarity with Python or SQL for data analysis

Outline

• Day 1: Setting Up A/B Tests
  • Importance of A/B Testing
  • Experimental Design
  • Hands-On: Designing an A/B Test for a Malaysian Website
• Day 2: Analyzing A/B Tests
  • Statistical Methods for Analysis
  • Interpreting Results
  • Group Project: A/B Testing a Malaysian Online Advertisement Campaign

Regression Modeling & Model Evaluation

(2 Days)

Understanding regression models is essential in predictive analytics. This 2-day section provides a deep dive into building and evaluating regression models.

Learning Outcomes

• Build regression models using Python
• Evaluate models using metrics like R-squared and RMSE
• Interpret regression coefficients

Prerequisites

• Familiarity with Python programming and libraries like Pandas and scikit-learn
• Basic understanding of descriptive and inferential statistics

Outline

• Day 1: Building Regression Models
  • Types of Regression Models
  • Fitting Models using Python
  • Hands-On: Building a Model to Predict Malaysian House Prices
• Day 2: Model Evaluation
  • R-squared and Adjusted R-squared
  • RMSE (Root Mean Square Error)
  • Mini Project: Evaluating Predictive Models for Malaysian GDP

Time Series Forecasting (2 Days)

Time Series Forecasting is ubiquitous in finance, sales, and resource planning. In this 2-day section, understand how to analyze time-dependent data and make future predictions.

Learning Outcomes

• Understand time series components
• Learn various time series forecasting models
• Apply time series forecasting in Python

Prerequisites

• Familiarity with Python programming
• Basic understanding of statistical modeling

Outline

• Day 1: Introduction to Time Series
  • Components of Time Series
  • Exploratory Time Series Data Analysis
  • Hands-On: Analyzing Malaysian Stock Market Trends
• Day 2: Time Series Forecasting Models
  • ARIMA Models
  • Seasonal Decomposition
  • Group Project: Forecasting Malaysia's Tourism Revenue Post-COVID

Capstone Project

(2 Days)

Culminate your learning experience by undertaking a capstone project. This 2-day section allows you to apply all the skills you've gained in a project of your choice, based on Malaysian datasets.

Learning Outcomes

• Apply all learned techniques in a comprehensive project
• Develop a presentation summarizing key findings and insights
• Receive peer and instructor feedback on the capstone project

Prerequisites

• Completion of all previous modules

Outline

• Day 1: Capstone Project Work
  • Selection of Project Topic
  • Data Collection and Cleaning
  • Data Analysis and Model Building
• Day 2: Capstone Project Presentation
  • Finalizing Analysis
  • Presentation of Findings
  • Peer and Instructor Feedback
  • Celebratory Close