Data Science for Monitoring & Evaluation (DSME)

Data Science for Monitoring & Evaluation
(DSME)

Course Overview:

This course aims to equip students with the necessary skills and knowledge to apply data science techniques to monitoring and evaluation practices. Students will learn to collect, analyze, and interpret data to inform decision-making and improve program effectiveness. The course combines theoretical knowledge with practical application, emphasizing real-world case studies in various sectors.

Learning Objectives:

By the end of this course, students will be able to:

Understand and apply fundamental statistical concepts relevant to M&E.
Design data collection strategies suitable for monitoring and evaluating projects.
Utilize data visualization tools and techniques to communicate findings effectively.
Analyze quantitative and qualitative data using programming languages/tools such as R or Python.
Conduct impact evaluations and apply causal inference methods.
Critically assess and leverage existing M&E frameworks and standards.

Course Modules:

Introduction to Monitoring and Evaluation (Weeks 1-2)

- *Topics:*

- Overview of M&E principles and frameworks

- Importance of M&E in project implementation

- Key concepts: inputs, outputs, outcomes, impacts

- *Activities:*

- Reading: "Evaluation: A Systematic Approach" by Peter Rossi

- Group discussion on the role of M&E in program effectiveness

Data Collection Techniques (Weeks 3-4)

Introduction to Data Science (Weeks 5-6)

Data Analysis Techniques (Weeks 7-9)

Data Visualization (Weeks 10-11)

Advanced Topics in M&E (Weeks 12-13)

Capstone Project (Weeks 14-15)

Suggested Resources:

- *Textbooks:*

- "Data Science for Business" by Foster Provost and Tom Fawcett

- "The Data Warehouse Toolkit" by Ralph Kimball

- "Practical Statistics for Data Scientists" by Peter Bruce and Andrew Bruce

- *Online Platforms:*

- Coursera / edX for additional courses on R, Python, and statistics

- Kaggle for access to practice datasets and competitions

- *Software:*

- R and RStudio or Python and relevant libraries (pandas, scikit-learn, etc.)

- Visualization tools (Tableau, Power BI)

Assessment Methods:

- Weekly quizzes based on readings and lectures

- Group projects and presentations

- Midterm exam covering the first half of the course

- Capstone project and presentation at the end of the semester

Additional Considerations:

- Guest speakers from the M&E field to provide insights and real-world applications of data science in M&E

- Workshops on using cloud computing services for data storage and processing (e.g., AWS, Google Cloud)

This structured approach ensures that students not only learn theory but also gain hands-on experience, preparing them for careers in data science and M&E.