Data_Science_Fundamentals_Corporate_v2.pptx

Corporate use case of Data Science

1. Recap of Data Science Definition & System Approaches Definition: Interdisciplinary field using stats, ML, and domain knowledge Historical evolution: from statistics to AI-driven insights System approaches: CRISP-DM, OSEMN, DIKW pyramid Importance of interdisciplinary collaboration Applications: healthcare, finance, retail, manufacturing

2. Role Mapping – Data Scientist, Analyst, Engineer Data Scientist: modeling, hypothesis testing, experimentation Data Analyst: BI tools, SQL dashboards, reporting Data Engineer: building robust pipelines, ETL, infra ML Engineer: deploying and scaling models Collaboration and overlaps in modern teams

3. Case Study Discussion Shell – predictive maintenance with sensor data Dr. Pepper – sales optimization via DS-driven insights Financial services – fraud detection systems Healthcare – drug discovery using ML pipelines Lessons learned: importance of data quality and context

4. Data Science Workflow Walkthrough CRISP-DM phases overview Business understanding and data understanding Data preparation – cleaning, transformation Modeling and evaluation Deployment, monitoring, and feedback loops

5. Introduction to Systems Thinking in DS What is systems thinking? Importance in DS: handling complexity Feedback loops and causal diagrams Systems archetypes in analytics Case example: retail demand forecasting system

6. Hands-On Walkthrough (Tool-Aided) Load data with Pandas Clean and transform data EDA with visualizations Apply simple ML model with Scikit-learn Evaluate and interpret results

7. Pillars of Data Science Math & Statistics: probability, inference, distributions Linear Algebra & Calculus: ML foundations Programming: Python, R, SQL Data Handling: ETL, Big Data tools Communication: storytelling, visualization, dashboards