Basic Data Analytics for Retail
About This Course
Course Overview:
This course, "Basic Data Analytics for Retail," provides a comprehensive introduction to the world of data, focusing on its application in the retail industry. Learners will explore various types of data, understand decision-making processes using logic and probability, and learn about data visualization techniques. The course also delves into the significance of unstructured data and its impact. Key retail applications, including emerging technologies, CRM, churn analysis, segmentation, cost optimization, and product recommendation, will be covered. Finally, the course will introduce common analytical tools and methods for measuring the quality of analytical output, equipping participants with essential skills for retail analytics.
What You'll Learn
1. Organize and prepare data to facilitate the performance of data analytics measurements.
2. Perform routine analytics to understand customer activities and behaviors across all digital platforms.
3. Update data as well as prepare reports and dashboards to communicate quantitative findings, trends and consumer insights.
4. Participate in analytics activities like predictive modelling, contextual targeting, churn analysis, revenue growth and growth optimization
Gather information to gauge the effectiveness of data-driven analytics tools and technologies deployed.
Topics:
1. Introduction to the world of data
2. Types of Data: -categorical v/s numerical/ordinal - appropriate measurements for data
3. Making Decisions with Data - Introduction to logic - cognitive biases - induction/deduction - logic and datasets
4. Introduction to Probability - Intuition - Distribution - Measures of Centrality - Measures of Dispersion
5. Data is more than numbers - Unstructured Text - Social - Images
6. Data Visualization - Common graphs (visualizations) and their strengths - Basic Storytelling - Dashboarding
7. Application to Retail - Emerging Technologies - CRM online and offline - Churn - Segmentation - Cost Optimization - Product Recommendation - Trends Analysis
8. Measuring Quality of Analytical Output - Metrics
9. Some common analytical tools
Entry Requirements
1. Minimum GCE 'N' Level
2. IELTS score 4.5 or TOEFL score 32 AND
3. High School Diploma equivalent to GCE ‘N’ Levels with minimum grade C6 for 3 subjects, including English and Mathematics
4. ALL Students are required to have basic computing skills - i.e. able to turn on the laptop, use the mouse, drag and drop files, use online search engines for research; and be a competent user of Microsoft Office.