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Judgment and Decision Making with Data (PPG-820/DS-820)
- Instructor Name: Dr. Saud Ahmed Khan
- Credit Hours: 3
- PSPDG
- E-mail: [email protected]
Prerequisites For this Course:
None
Text Book(s):
- Data-driven talent management: Using analytics to improve employee experience by Kristin Saling.
- Public policy analytics: Code and context for data science in government. By Ken Steif
- Big data science and analytics for smart sustainable urbanism. by Simon Elias Bibri
- Data science for public policy by Jeffrey C. Chen, Edward A. Rubin and Gary J. Comwall
- R for political data science: A practical guide by Francisco Urdinez and Andres Cruz
- Be data driven: How organizations can harness the power of data by Jordan Morrow
- Value driven data: Identifying, communicating and delivering effective business solutions with data by Edosa Odaro.
Reference Book(s):
- Data-driven systems and intelligent applications by Mangesh M. Ghonge, N. Krishna Chaitanya, Pradeep N. Harish Garg, and Alessandro Bruno.
- Applied data science: Lessons learned for data-driven business by Martin Braschler, Thilo Stadelmann and Kurt Stockinger.
- Predictive analytics for data-driven decision making: Tools and techniques for solving real-world problems by L. Ashok, R. Sujhata and B. Uma Maheswari
- Data science applied to sustainability analysis by Jennifer Dunn, and Prasanna Balaprakash
- Reimaging digital learning for sustainable development: How upskilling, data analytics, and educational technologies close the skills gap by Sheila Jagannathan
Web links:
- https://www.researchgate.net/publication/227389400_Theory_of_Decision_under_Uncertainty
- https://asana.com/resources/decision-making-process
- https://www.orientalsolutions.com/complete-guide-on-what-is-data-curation-and-its-life-cycle/
- https://asana.com/resources/decision-making-process
- https://www.orientalsolutions.com/complete-guide-on-what-is-data-curation-and-its-life-cycle/
- https://online.hbs.edu/blog/post/data-driven-decision-making#:~:text=What%20Is%20Data%2DDriven%20Decision,determine%20business%20opportunities%20or%20threats
- https://www.linkedin.com/pulse/from-data-decisions-impact-ai-strategic-planning-underhill-ppm–shbvf/
- https://bmsce.ac.in/Content/IS/Big_Data_Analytics_-_Unit_1.pdf
- https://www.coursera.org/learn/simulation-models-for-decision-making
- https://hyperproof.io/resource/how-to-conduct-a-gap-assessment/
- https://online.hbs.edu/blog/post/gap-analysis
- https://www.qlik.com/us/kpi
- https://www.klipfolio.com/resources/articles/what-is-a-key-performance-indicator
Course Description
This course is developed to equip students with skills needed for market-driven applications and academic research in the field of Judgment and Decision making. The course aims to learn, how to utilize data for efficient and relevant decision making in the fields of Public Policy and Development Studies. Specifically, the focus is on better decision-making on public policy and development issues of Pakistan.
Course Objectives
After successful completion the students would be able to demonstrate problem solving skills in the field of decision making using available information (Data). The student would be able to compare and debate on goals of an economy or an organization and conclude with plausible solutions. With hands on training, the participants would be able to summarize and conclude judgment and decision making problems independently. The participants will,
- Specialize in the field of Judgment and Decision Making with data
- Be equipped with ample knowledge of core concepts of supporting fields of Statistics, Mathematics and Data Analysis.
- Have understanding of theory of change.
- Be able to Compare and Debate on specific set of goals of an economy/organization.
Be capable of solving judgement and decision making problems through concluding and summarizing the relevant information (data sets) with the help of machine learning tools.
Learning Outcomes
The participants will,
- Specialize in the field of Judgment and Decision Making with data
- Be equipped with ample knowledge of core concepts of supporting fields of Statistics, Mathematics and Data Analysis.
- Have understanding of theory of change.
- Be able to Compare and Debate on specific set of goals of an economy/organization.
- Be capable of solving judgement and decision making problems through concluding and summarizing the relevant information (data sets) with the help of machine learning tools.
Lecture Plan
| Session | Topic | Readings | Activities | Relevant CLO |
|---|---|---|---|---|
| Module # 1: Theory of Decision making and Data Curation | ||||
| Week-1 | Orientation and Subject Related Discussion | TB/RB, Handout/ppt and link#1 | General discussion | |
| Planning and Designing of the Course | TB/RB, Handouts/ppt and link#1 | Group discussion | ||
| Week-2 | Pedagogical technique & Class assessment | TB/RB, Handouts/ppt and link#1 | General and Group discussion | |
| Types of Data (Primary, Secondary, Qualitative, Quantitative, etc.) | TB/RB, Handouts/ppt and link#2 | General Discussion, Subject Related Discussion | ||
| Week-3 | Time Series Data, Cross Sectional Data, Financial Time series Data | TB/RB, Handouts/ppt and link#2 | Skill: How to Download Book using LibGen, Scilab | |
| Data Curation Life Cycle | TB/RB, Handouts/ppt and link#2 | Access to Financial Data; Intro to data handling software; Assignment#1 | ||
| Module # 2: Essential tools – Mathematics and Statistics | ||||
| Week-4 | Introduction to Core concepts of Mathematics | TB/RB and Handouts/ppt | Subject specific Discussion; Hands-on practice | |
| Non-Conventional approach to Calculus; Theory of Numbers, Intervals, Functions. | TB/RB and Handouts/ppt | General Discussion; Hands-on practice | ||
| Week-5 | Straight Line, Polynomials, Algebraic, Exponential and Logarithmic functions. | TB/RB and Handouts/ppt | General Discussion; Hands-on practice | |
| Essential Statistical tools: MCTs, MDs, Data Visualization, Sampling, Inference | TB/RB and ppt | General Discussion; Hands-on practice | ||
| Week-6 | Hypotheses testing, ANOVA, Bivariate Analysis, Simple Linear regression, Basic probability, etc. | TB/RB and ppt | General Discussion; Hands-on practice; Lab-based Assignment#2 | |
| Data-driven decision making Case Studies: “Hand wash practices”, “Open defecation” | Handouts/ppt | Group discussion on the core material | ||
| Week-7 | Case Studies Continued: “Stipend for female students”, “Bonus or percentage in recovery” | Handouts/ppt | Group discussion; Quiz#1 | |
| Introduction to Big data: Sources & Types | Ppt/WebLink#8 (Coursera) | General discussion | ||
| Week-8 | Usability of Big Data in decision making | Ppt/WebLink#8 (Coursera) | General discussion | |
| MID TERM EXAM | ||||
| Module # 3: Data to decisions and AI | ||||
| Week-9 | Machine Learning techniques; R-studio | Weblinks#6and7 | General discussion and Hands on practice | |
| Continued… | Weblinks#6and7 | General discussion and Hands on practice | ||
| Week-10 | Python | Weblinks#6and7 | General discussion and Hands on practice | |
| Continued… | Weblinks#6and7 | General discussion and Hands on practice | ||
| Week-11 | LSTM, XGBoost | Weblinks#6and7 | General discussion and Hands on practice | |
| Continued… | Weblinks#6and7 | Assignment#3 | ||
| Week-12 | Data analytical frameworks; Data analytics and presentation skills | Weblink#9 and 10 | General discussion and Hands on practice | |
| Simulation techniques | Weblink#9 and 10 | Hands on practice | ||
| Week-13 | Data collection | Weblink#9 and 10 | Lab-based Quiz#2 | |
| Gap assessment: Current state, Desired state, Gap | Weblink#11 and 12 (HBS Online) | General discussion and Hands on practice | ||
| Week-14 | Action plan; Risk mitigation etc. | Weblink#11 and 12 (HBS Online) | Hands on practice | |
| Key performance indicators (KPIs): Specific, Measurable | Weblink#13 and 14 (qlik.com) | Hands on practice | ||
| Week-15 | KPIs Continued: Relevant, Time Bound, Actionable etc. | Weblink#13 and 14 (qlik.com) | General discussion and Hands on practice | |
| Continued… | Weblink#13 and 14 (qlik.com) | General discussion and Hands on practice; Quiz#3 | ||
| Week-16 | Concluding Discussion | Revision and concluding group discussion; Behavioral assessment | General discussion | |
| Final Exam | ||||
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