Data Science for Business Decision Making Course

Introduction

This course equips professionals with practical data science skills to extract insights, automate analysis, and support evidence-based decisions. You’ll learn how to handle data, build predictive models, and visualize results using modern tools and techniques.

Objectives

• Understand the data science lifecycle

• Explore statistical and machine learning models

• Apply Python and data tools to real datasets

• Communicate insights through effective visualization

• Support business decisions with predictive analytics

Course Outline

Day 1: Introduction to Data Science and Analytics

• The role of data science in business

• Overview of the data science process

• Key concepts in data types and structures

• Exploratory data analysis

• Introduction to Python for data science

Day 2: Working with Data and Visualization

• Data cleaning and preprocessing

• Handling missing values and outliers

• Visualizing data with Matplotlib and Seaborn

• Storytelling with data

• Hands-on visualization exercises

Day 3: Statistics and Predictive Modeling

• Descriptive and inferential statistics

• Correlation and regression analysis

• Introduction to supervised learning

• Building simple predictive models

• Evaluating model performance

Day 4: Machine Learning Essentials

• Classification vs. regression models

• Decision trees, k-nearest neighbors, and SVM

• Cross-validation techniques

• Model tuning and feature selection

• Applying models to business scenarios

Day 5: Projects, Strategy, and Applications

• Building an end-to-end data science project

• Deploying insights into business decisions

• Automation using basic scripts and pipelines

• Best practices for data science in organizations

• Final project presentation and feedback