Supply Chain Analytics -

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

Prerequisites:

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

Assessment:

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.

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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Supply Chain Analytics