Program Objectives

• Equip Managers with Python Skills: To provide managers with the fundamental Python programming skills needed for data analysis without requiring prior programming experience.
• Understanding of Statistical Analysis: To deepen the knowledge of statistical methods and their application in business decision-making through practical examples and datasets.
• Data-Driven Decision Making: To enable participants to make informed decisions by interpreting data trends and insights, leveraging Python's powerful data analysis tools.
• Application of Analytical Tools in Business: To demonstrate how Python can solve real-world business problems, from financial analysis to customer behavior insights.
• Confidence in Data Interpretation: To build confidence in managers to present data findings and strategic recommendations to stakeholders, enhancing their leadership and decision-making capabilities.

Pedagogy

• Interactive Lectures: Sessions introducing core concepts of statistics, Python programming, and data analysis, enriched with real-world examples relevant to management. Lectures incorporate Q&A segments to engage learners and clarify complex topics.
• Hands-On Workshops: Practical workshops where participants apply what they've learned in lectures by working on Python coding exercises, data manipulation tasks, and statistical analysis projects. These sessions focus on solving business-related problems, ensuring skills are directly applicable to managerial roles.
• Case Studies: Analysis of real-world business scenarios where data-driven decision-making was crucial. Participants dissect these cases, apply Python tools to analyze data, and propose solutions, fostering critical thinking and practical application of skills.

Program Content

Day 1: Introduction to Python and Basic Statistics for Managers

Session 1: Introduction to Python for Data Analysis

Session 2: Managing Data with Python

Session 3: Basics of Descriptive Statistics

Session 4: Data Visualization Fundamentals

Day 2: Intermediate Statistics and Data Analysis

Session 1: Probability and Distributions

Session 2: Inferential Statistics: Estimation and Hypothesis Testing

Session 3: Comparing Groups and ANOVA

Session 4: Correlation and Regression Analysis

Day 3: Advanced Statistical Methods and Time Series Analysis

Session 1: Multivariate Statistics

Session 2: Time Series Analysis Basics

Session 3: Advanced Data Visualization Techniques

Session 4: Integrating Statistics into Business Strategy

Profile of Programme Director(s)

Arulanantha Prabu P M is a faculty in operations management and decision sciences area at the Indian Institute of Management Trichy, India. He received his Ph.D. from Indian Institute of Management Ahmedabad in the Production and Quantitative Methods area. Before joining IIMT, he taught at Indian Institute of Management Ranchi for two years. His research papers are published in leading journals like European journal of operational research, International journal of production economics, and International journal of production research. He teaches operations management, operations research, and business statistics.

Profile of Expected Participants

• Mid to Senior-Level Managers: Individuals holding managerial positions responsible for decision-making processes within their departments or teams.
• Business Analysts: Professionals who rely on data to inform business strategies and improve processes but wish to enhance their technical skills.
• Project Managers: Leaders overseeing projects who can benefit from data analytics to enhance project outcomes.
• Entrepreneurs and Business Owners: Individuals who manage their businesses and are looking to leverage data for strategic planning, marketing,and operational efficiency.
• Consultants: Professionals who provide strategic advice to businesses must incorporate data analysis into their consulting practices.