Introduction to Retail Data Science
( 24 Modules )

Module #1
Introduction to Retail Data Science
Overview of the retail industry and the role of data science in retail

Module #2
Understanding Retail Data
Types of retail data, data sources, and data quality issues

Module #3
Python for Retail Data Science
Introduction to Python and its libraries for data science (Pandas, NumPy, etc.)

Module #4
Data Visualization for Retail
Using visualization tools (Matplotlib, Seaborn, Plotly) to communicate insights

Module #5
Descriptive Analytics for Retail
Summary statistics, data aggregation, and data profiling

Module #6
Exploratory Data Analysis (EDA) for Retail
Using EDA techniques to uncover patterns and relationships in retail data

Module #7
Customer Segmentation
Clustering and segmentation techniques to understand customer behavior

Module #8
RFM Analysis
Using Recency, Frequency, and Monetary value to analyze customer behavior

Module #9
Market Basket Analysis
Analyzing customer purchase behavior and product relationships

Module #10
Forecasting Sales and Demand
Introduction to time series forecasting and regression analysis

Module #11
Price Optimization
Using data science to optimize pricing strategies

Module #12
Inventory Management
Using data science to optimize inventory levels and reduce stockouts

Module #13
Supply Chain Optimization
Using data science to optimize supply chain operations

Module #14
Retail Analytics Tools
Overview of retail analytics tools and platforms (e.g. Tableau, Power BI, etc.)

Module #15
Working with Unstructured Data
Analyzing text and image data in retail (e.g. social media, product reviews)

Module #16
Machine Learning for Retail
Introduction to machine learning concepts and algorithms

Module #17
Recommendation Systems
Building recommendation systems for retail using machine learning

Module #18
A/B Testing and Experimentation
Designing and analyzing A/B tests to measure the impact of retail initiatives

Module #19
Retail Data Science Case Studies
Real-world examples of data science applications in retail

Module #20
Communicating Insights in Retail
Effectively communicating data insights to stakeholders in retail

Module #21
Retail Data Science Tools and Technologies
Overview of tools and technologies used in retail data science (e.g. Apache Spark, Hadoop)

Module #22
Big Data in Retail
Managing and analyzing large datasets in retail

Module #23
Ethics in Retail Data Science
Ethical considerations in retail data science (e.g. data privacy, bias)

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Retail Data Science career

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Introduction to Retail Data Science ( 24 Modules )

Introduction to Retail Data Science
( 24 Modules )