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PIDE School of Economics

PIDE School of Policy, Development and Governance

Economics

  • Phd Economics
  • Mphil Economics

Econometrics

  • Phd Econometrics
  • Mphil Econometrics

Environmental Economics

  • Mphil Environmental Economics

Economics and Finance

  • Mphil Economics and Finance

Business Economics

  • Mphil Business Economics

PIDE School of Policy, Development and Governance

Public Policy

  • Phd Public Policy and Governance
  • Mphil Public Policy and Governance

Development Studies

  • Phd Development Studies
  • Mphil Development Studies

Management Sciences

  • MS Management Sciences
  • MBA (2 Years)

Big Data Analysis (E-778)

  • Instructor Name: Dr. Saud Ahmed Khan
  • Credit Hours: 03
  • PIDE School of Economics
  • E-mail: [email protected]
  • Office Hours: 08:30 AM -04:30 PM

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:

  1. https://www.researchgate.net/publication/227389400_Theory_of_Decision_under_Uncertainty
  2. https://asana.com/resources/decision-making-process
  3. https://www.orientalsolutions.com/complete-guide-on-what-is-data-curation-and-its-life-cycle/
  4. https://asana.com/resources/decision-making-process
  5. https://www.orientalsolutions.com/complete-guide-on-what-is-data-curation-and-its-life-cycle/
  6. https://online.hbs.edu/blog/post/data-driven-decision-making#:~:text=What%20Is%20Data%2DDriven%20Decision,determine%20business%20opportunities%20or%20threats
  7. https://www.linkedin.com/pulse/from-data-decisions-impact-ai-strategic-planning-underhill-ppm–shbvf/
  8. https://bmsce.ac.in/Content/IS/Big_Data_Analytics_-_Unit_1.pdf
  9. https://www.coursera.org/learn/simulation-models-for-decision-making
  10. https://hyperproof.io/resource/how-to-conduct-a-gap-assessment/
  11. https://online.hbs.edu/blog/post/gap-analysis
  12. https://www.qlik.com/us/kpi
  13. 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

Lecture Plan

Session Topic Readings

(Book Chapters/pages/Research papers/Handouts/Web links including video links

 Activities

Quizzes/Assignments/Term papers

 Relevant CLO
Module # 1: Title: 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 i. Pedagogical technique

ii. Class assessment at the end of the class – Potential of the batch

 TB/RB

Handouts/ppt and link#1

 General and Group  discussion  
Types of Data (Primary. Secondary, Qualitative, Quantitative)

Continuous, Discrete, Nominal, Ordinal, Ratio, Interval, Financial Data

 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)

Related Discussion

https://www.scilab.org/

https://libgen.ac/

  
Data Curation Life Cycle TB/RB

Handouts/ppt and link#2

 Access to Financial Data [brecorder, yahoo-finance, SBP, PBS.

Skill: Intro data handling software

Assignment#1

  
Module # 2, Title: Essential tools – Mathematics and Statistics
Week-4 Introduction to Core concepts of Mathematics

 

 TB/RB and Handouts/ppt i.                    Subject specific Discussion

ii. Hands-on practice

  
i. Non-Conventional approach to Calculus.

ii. Getting refreshed on Theory of Numbers, Intervals (types).

iii. Relations and Functions, Type of Functions, Inverse Function.

 TB/RB and Handouts/ppt i. General Discussion, Subject Related Discussion

ii. Hands-on practice

  
Week-5 iv. Straight Line, Polynomials and Algebraic Functions.

Exponential and Logarithmic functions and graphs relevant in Decision Making

 TB/RB and Handouts/ppt i. General Discussion, Subject Related Discussion

ii. Hands-on practice

  
Essential Statistical tools –

i. MCTs

ii. MDs and relative MDs

iii. Data Visualization

iv. Sampling distributions

v. Making Inference

 TB/RB and ppt i. General Discussion, Subject Related Discussion

ii. Hands-on practice

  
Week-6 vi. Hypotheses testing (mean and proportion)

vii. NAOVA

viii. Bivariate Analysis

ix. Simple Linear regression

x. Basic probability

xi. Sampling techniques

xii. Density plots and moments

xiii. Factor Analysis

 TB/RB and ppt i. General Discussion, Subject Related Discussion

ii. Hands-on practice

Lab-based Assingmnet#2

  
Data-driven decision making

Case Studies

i. Monitoring “Hand wash practices” in south Punjab districts

ii. “Open defecation” causes in selected districts in Sindh province

 Handouts/ppt Group discussion on the core material  
Week-7 iii. Stipend for female students at primary level

iv. Bonus or percentage in recovery

 Handouts/ppt Group discussion on the core material

Quiz#1

  
Introduction to Big data

i. Sources

ii. Types

 Ppt/WebLink#8

https://www.coursera.org/articles/big-data-analytics

 

 General discussion  
Week-8 iii. Usability in decision making Ppt/WebLink#8

https://www.coursera.org/articles/big-data-analytics

 

 General discussion  
MID TERM EXAM  
Module # 3: Data to decisions and AI
Week-9 Machine Learning techniques

R-studio

 Weblinks#6and7

https://youtu.be/aCUM4r1ONhg?si=kl9yLwqk-lASczMn

 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

https://online.hbs.edu/blog/post/gap-analysis

 General discussion and Hands on practice  
Week-14 –          Action plan

–          Risk mitigation etc.

 Weblink#11 and 12

https://online.hbs.edu/blog/post/gap-analysis

 Hands on practice  
Key performance indicators

–          Specific

–          Measurable

 Weblink#13 and 14

https://www.qlik.com/us/kpi

 

 Hands on practice  
Week-15 –          Relevant

–          Time Bound

–          Actionable etc.

 Weblink#13 and 14

https://www.qlik.com/us/kpi

 General discussion and Hands on practice  
Continued… Weblink#13 and 14

https://www.qlik.com/us/kpi

 General discussion and Hands on practice

Quiz#3

  
Week-16 Concluding Discussion i. Revision and concluding group discussion

ii. Behavioral assessment

 General discussion  
Final Exam

 

 

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