Isilay Talay (sharing with PhD student Venu Bhaskar Puthineedi)
Businesses rely on insights of managers and managers gain their insights by carefully and scientifically analysing data. This fundamental quantitative course aims to provide you with the necessary skills and techniques to be able to analyse data, make inferences and make better decisions.
This is not intended to be a fundamental mathematical course, rather we would use mathematical and statistical techniques to understand how to make decisions. This being a 2-module course, we would achieve this learning over 2 semesters. As a fundamental statistics course, we would cover topics on probability, inferences, regression, forecasting among others.
This course would have a substantial hands-on nature i.e. we would be learning theoretical techniques as well as implementing them on real data. We would use Excel as a software of choice for this purpose. We would also, from time to time, discuss other relevant software and tools for such statistical analysis.
The module intends to prepare the students for a career in business and management by providing them with tools and techniques to understand and analyse data. After successfully completing this module, students would be able to:
- Appreciate the role of data driven insights generation in real world situations
- Understand and process unstructured data through descriptive and explorative mechanisms
- Be able to use excel for generating insights from real world datasets
- Critically analyse and use key statistical concepts in business settings
- Understand key challenges and limitations in data-driven decision making
Recommended Texts/ Required Readings
I would recommend the following core textbook for the course. We would broadly follow this textbook throughout the course.
- Statistics for Managers Using Microsoft Excel (global edition) – Levine, Stephan and Szabat, Pearson publishers
General Supplemental Readings
- There are many fundamental statistics books available in library and otherwise that you may refer to for additional reading/references for the material taught in class. Below are some suggestions. Supplemental material for the course may also be provided through the blackboard system for the module during the year.
- Lee and Peters. Business Statistics using Excel and SPSS, Sage Publishers, with STATLAB 1st Ed 2016.
- Veal. Business Research Methods – a Managerial Approach, Pearson, multiple copies are available in the TCD Library
1. In-class exercises (40%): There would be 4 in-class exercises in lectures mentioned below. These exercises would be based on group work and would each carry a 10% weight for a total of 40 % weightage.
2. Mid-term Assesment (20%): There would be a mid-term assessment that would be comprised of a take-home exam where students would have to analyse a dataset for a specific business purpose and use the techniques they learnt in class to suggest solutions for the firm. Students would also have to defend their choices of the methods based on the statistical theory taught.
3. End Term Assessment (40%): There would be a end-term assessment that would be comprised of a take-home exam where students would have to analyse a dataset for a specific business purpose and use the techniques they learnt in class to suggest solutions for the firm. End term assessment would expect students to use the techniques learnt throughout the year to be applied and insights derived from data. Students would also have to defend their choices of the methods based on the statistical theory taught.