Supply Chain Analytics

Categories: Analytics
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About Course

This course is designed to provide students with a comprehensive understanding of data analytics, including data acquisition, preparation, analysis, and visualization. Students will learn how to use various data analytics tools and techniques to make data-driven decisions and solve real-world problems. The course will cover key concepts in data analytics, such as data wrangling, descriptive and inferential statistics, machine learning, and data visualization.

Course Outline:


Introduction to Data Analytics
  • Definition of Data Analytics
  • Key Drivers and Trends in Data Analytics
  • The Role of Data in Business Decision Making
Data Acquisition and Preparation
  • Data Sources and Types
  • Data Cleaning and Transformation
  • Data Integration and Aggregation
Descriptive Statistics and Data Visualization
  • Data Exploration and Visualization
  • Measures of Central Tendency and Variability
  • Correlation and Regression Analysis
Inferential Statistics and Hypothesis Testing
  • Sampling Techniques and Distributions
  • Hypothesis Testing and Confidence Intervals
  • ANOVA and Chi-Square Analysis
Machine Learning for Data Analytics
  • Supervised and Unsupervised Learning
  • Classification and Regression Models
  • Clustering and Association Analysis
Big Data Analytics
  • Introduction to Big Data and Hadoop
  • Data Mining and Text Analytics
  • Streaming Analytics and Real-time Processing
Data Visualization and Storytelling
  • Data Visualization Principles and Best Practices
  • Tools and Techniques for Data Visualization
  • Effective Communication of Data Insights



  • Basic knowledge of statistics and mathematics
  • Familiarity with Excel or other data analysis tools

Recommended Textbook:

  • “Data Analytics Made Accessible” by Anil Maheshwari.


  • Midterm Exam (30%)
  • Final Exam (40%)
  • Group Project (30%)

Note: The course syllabus and assessment methods are subject to change based on instructor’s discretion.

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What Will You Learn?

  • Understand the role of data analytics in business decision making and problem-solving.
  • Acquire and prepare data from various sources, including cleaning, transforming, integrating, and aggregating data.
  • Analyze and visualize data using descriptive and inferential statistics, correlation and regression analysis, and hypothesis testing.
  • Apply machine learning techniques, including supervised and unsupervised learning, classification and regression models, and clustering and association analysis.
  • Analyze and interpret big data using data mining, text analytics, and streaming analytics techniques.
  • Develop and communicate data insights through effective data visualization and storytelling.

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