Data Science Project Planning

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Preview

Lecture 2: Welcome

Lecture 3: Context

Lecture 4: Data Science Project – Challenges

Lecture 5: Data Science Project Planning – An Overview

Chapter 2: Business Problem Definition

Lecture 1: Introduction

Lecture 2: Business Problem Definition – An Overview

Lecture 3: Understanding the Business Problem

Lecture 4: Stakeholder Analysis – I

Lecture 5: Stakeholder Analysis – II

Lecture 6: Review of Previous Work

Lecture 7: Framing the Business Problem

Chapter 3: Data Science Problem Formulation

Lecture 1: Introduction

Lecture 2: Data Science Project Lifecyle – An Overview of CRISP-DM

Lecture 3: Data Science Problem Formulation – An Overview

Lecture 4: Data Science Problem Type – Classification

Lecture 5: Data Science Problem Type – Regression

Lecture 6: Data Science Problem Type – Clustering

Lecture 7: Data Science Problem Type – Anomaly Detection

Lecture 8: Data Science Problem Type – Association

Lecture 9: Data Science Problem Type – Recommendation

Lecture 10: Summary of Data Science Problem Types

Lecture 11: Setting Project Goals

Lecture 12: Specifying Project Success Criteria – An Overview

Lecture 13: Evaluation Metrics for Classification Models

Lecture 14: Evaluation Metrics for Anomaly Detection Models

Lecture 15: Evaluation Metrics for Regression Models

Lecture 16: Evaluation Metrics for Clustering Models – I : Internal Evaluation

Lecture 17: Evaluation Metrics for Clustering Models – II: External Evaluation

Lecture 18: Evaluation Metrics for Association Models

Lecture 19: Evaluation Metrics for Recommendation Models – I

Lecture 20: Evaluation Metrics for Recommendation – II

Lecture 21: Model Deployment Criteria and Metrics

Lecture 22: Model Monitoring Metrics

Lecture 23: Data Flow Pipeline Metrics

Lecture 24: Documentation Criteria

Chapter 4: Situation Assessment

Lecture 1: Introduction

Lecture 2: Situation Assessment – An Overview

Lecture 3: Team Composition

Lecture 4: Resource Assessment

Lecture 5: Project Requirements, Assumptions & Constraints

Lecture 6: Risk Assessment

Lecture 7: Terminology

Lecture 8: Costs and Benefits

Chapter 5: Project Scheduling

Lecture 1: Introduction

Lecture 2: Scheduling – I

Lecture 3: Scheduling – II

Chapter 6: Emerging Methods

Lecture 1: Introduction

Lecture 2: Emerging Methods for Executing Data Science Projects

Lecture 3: Microsoft Team Data Science Process (TDSP)

Lecture 4: Agile Data Science 2.0

Chapter 7: Conclusion

Lecture 1: Introduction

Lecture 2: Recap of Key Points

Lecture 3: Project Plan Review – Checkpoints

Lecture 4: Closing Remarks

Lecture 5: Congratulations and Thanks