Course Description

The data analytics course is comprised of several analytical approaches that will meet the market needs. The course covers probability theory, predictive models, trend analysis, demand forecasting, and survey methods in detail. It’s a high-intensity course concerning the content and therefore it greatly relies on the self-learning/reading of the participants.

Course Outline

Title Details
Course Code & Title Data Analytics and Decision Making
Program(s) MS (Management) / MBA
Instructor Prof. Attaullah Shah
Website (if any): https://opendoors.pk/courses/data-sciences-for-finance/
Email For assignments: ims.assign@gmail.com

For Other communications: attaullah.shah@imsciences.edu.pk

attashah15@hotmail.com
Office Location Basement, Academic Bloc.
Office Contact Hours: From 1 PM to 5 PM on Saturday
Course Description: The data analytics course is comprised of several analytical approaches that will meet the market needs. The course covers probability theory, predictive models, trend analysis, demand forecasting, and survey methods in detail. It’s a high-intensity course concerning the content and therefore it greatly relies on the self-learning/reading of the participants.
Course Resources: For learning programming, I recommend the following books by
Jake Vanderplas, both of which are freely available:1. A Whirlwind Tour of Python
(link https://jakevdp.github.io/WhirlwindTourOfPython )2. Python Data Science Handbook
(link https://jakevdp.github.io/PythonDataScienceHandbook/ )Lectures and exercise posted on https://opendoors.pk/courses/data-sciences-for-financeQuantitative Economics and Finance with Python https://python.quantecon.org/intro.html
Course Assessment(s) 50% marks are based on weekly assignments that involve solving Python / Stata / Excel problems. The rest are based on mid term (20%) and comprehensive (30%).
Course Methodology The course is conducted in a computer lab. Each lecture starts with discussion of a model / issue related to financial data. The discussion is followed by a practical demonstration of how to solve the model / issue. Finally, students are given a chance to solve  the problems or similar problems on their own.
Course Objectives
  1. Data Management: Enable students to efficiently collect, organize, and prepare data for analysis.
  2. Statistical Analysis Proficiency: Develop students’ proficiency in performing statistical analysis using Stata and Microsoft Excel.
  3. Data Visualization: Teach students how to create meaningful visualizations to aid decision-making.
  4. Effective Reporting: Instruct students in the art of writing clear and concise reports based on statistical outputs.
Learning Outcomes
  1. Data Analysis: By the end of the course, students will be able to proficiently analyze diverse datasets to inform decision-making processes.
  2. Statistical Software Proficiency: Students will acquire the skills needed to perform statistical analysis using Stata and Microsoft Excel, including data entry, manipulation, and interpretation of results.
  3. Data Visualization Mastery: Students will be capable of creating informative graphs and charts to visually represent data patterns and insights.
  4. Effective Reporting: Upon completing the course, students will demonstrate the ability to prepare comprehensive reports summarizing statistical findings and their implications for decision makers.
Behavioral Expectations/ Class Policies (if any): 1. Students are expected to reach class within 2 minutes of the start of the class
2. Students are expected to submit their assignments within one-week time
3. The preferred learning style for the course is participative. Therefore, students are encouraged to raise questions, comment on solutions, suggest alternative solutions to problems, and answer questions raised the instructor.

Course Schedule

Week No. Description     Resources
Week 1 Introduction to data analytics
Week 1 Introduction
Week 1 Importance
Week 1 Challenges
Week 1 Data analytics types
Week 1 Variables and their types + Practical sessions
Week 2 A review of descriptive and inferential techniques
Week 2 Measures of central tendencies
Week 2 Dispersion
Week 2 Hypothesis testing
Week 2 Testing averages
Week 2 Testing proportions
Week 3
Week 3
Week 3
Week 3
Week 3
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 5
Week 5
Week 5
Week 5
Week 5
Week 5
Week 6 Quantitative Economics with python
Week 6
Week 6
Week 6
Week 6
Week 7
Week 7
Week 7
Week 7
Week 8
Week 8
Week 8
Week 8
Week 8

Mid-Term Exam
Week 9-10
Week 9-10
Week 9-10
Week 9-10
Week 9-10
Week 9-10
Week 11
Week 11
Week 11
Week 11
Week 12 Getting Started with Stata
Week 12
Week 12
Week 12
Week 12
Week 12
Week 13-14 Panel Data regression and univariate analysis
Week 13-14
Week 13-14
Week 13-14
Week 13-14
Week 13-14
Week 13-14
Week 15 Advanced Topics in Data Management using MS Excel
Week 15 Filtering techniques
Week 15 Conditional formatting
Week 15 Vlookup / index / match functions
Week 15 Pivot Tables
Week 16 Advanced Topics in Data Management using MS Excel – II
Week 16 Data Validation
Week 16 Data Protection
Week 16 Macros / VBA

Lecture Notes and Files

`

Week No. Description
Week 1 Introduction to data analytics [Download]
Week 2 Descriptive statistics [Download]
Week 3 Tabular and graphical representation of a single variable Download
Week 4 Tabular and graphical presentation of two variables Download
Week 4.1 Exercises Download
Week 5 Numerical Measures of descriptive Statistics Download
Week 5.1 Exercise on measures of locations – Download
Week 5.2 Exercise on measures of variability – Download
Week 5.3 Cases and Exercises Download
Week 6 Inferential Statistics Download
Week 7  Data Quality Control Download
Week 7 Covid Dataset Download Codebook of the covid data download
Week 7 Google Apps Data      Download

Assignments

Assignment 1: Commentary on a real word report – 5 marks

Upload your assignment 1 to Turnitin:

  1. Create a Turnitin account if you do not have one already. https://www.turnitin.com/newuser_type.asp?lang=en_us
  2. Use class id=41267642 or 41351071 and enrollment key = dadm to join the class
  3. Upload your assignment,  do not use contents from ChatGPT.
  4. Write a commentary of one or a half page yourself, do not copy that from internet or ChatGPT.
  5. You may copy parts of the report to your assignment file where descriptive statistics or graphs are given.
  6. The assignment submission date is November 3, 2023.

Class files

Results

Exams