Introduction

1- What is Data Science? 2- Importance of Methodology 3- Overview of Common Frameworks 4- Roles and Applications in the Industry 5- Business Understanding 6- Defining Objectives and Questions 7- Framing Data Science Problems 8- Working with IDEs 9- Identifying Data Requirements 10- Data Sources 11- Basics of Data Collection & Ethics 12- Data Exploration Basics 13- Handling Missing or Inconsistent Data 14- Data Cleaning Essentials 15- Introduction to Data Wrangling 16- Introduction to Analytical Thinking 17- Overview of Analytical Methods 18- Introduction to Key Tools : Python and Excel 19- Summary Statistics 20- Measures of Spread (Variance, Standard Deviation) 21- Central Tendency and Dispersion 22- Interpreting Basic Statistical Outputs 23- Introduction to Data Analysis 24- Basic Data Visualization: Charts, Graphs, Plots 25- Extracting Insights From Data 26- Structuring a Data Science Report 27- Presenting Insights Visually and Textually 28- Introduction to Storytelling with Data 29- Ethical Considerations in Data Science 30- Reviewing the Data Science Workflow 31- Emerging Trends 32- Introduction