Business Strategy and Analytics Syllabus
Teaching Team
Instructor: Anil Doshi
Teaching Assistants
- Pavlo Ulianiuk
- Jingze Wang
- Joe Myers
Contact information for Module Leaders (and any Teaching Assistants if applicable) should be provided on the module’s Moodle page. Module Leaders and Teaching Assistants may also provide details of any Office Hours they may offer or their other preferred methods of contact.
When contacting members of staff by email, students should always
- Ensure that they use their UCL email account,
- Include their full name and student number in any correspondence
Module Details
- Module ID: MSIN0093
- Offered: Term 1
- Meeting Times: see module timetable
- Links to module resources:
Module Description
Strategy is making interdependent decisions that result in better outcomes. Organizations are increasingly considering how data can be used to inform strategic thinking and decisions.
In this module, students will investigate data and the organisation from three paths. First, students will grapple with what data represents and how it is structured to build a functional understanding on its uses and limitations. Second, students will cover analytical methods that are useful in studying the impact of strategic decisions. Third, students will look at data and the organization, to understand the strategic value of data itself and wider ethical implications.
The goal is for students not only to learn practical skills and data analytical methods, but also to learn how to critically understand an uncertain problem, provide a framing for it, and arrive at viable solutions.
Aims
The core aim of the module is to use data and analytics in the service of forming and testing a theory of the organization. You will learn to form your theory of the organization by integrating the relevant strategy frameworks or perspectives and considering how they evolve over time. You will learn to test your theory of the organization using a diverse set of empirical methods.
A secondary aim of the module is to prepare you to take on leadership roles in your organization by being able to assume a "big picture" and long-term view on your team, organization, and markets.
Learning Outcomes
By the end of this module, you are expected to be able to:
- Define the components of a business model and identify several different types of business models
- Evaluate the position of an organization with respect to its external environment
- Evaluate the strategic implications of an organization's resources, culture, and design
- Understand how performance is reflected in a company's financial statements
- Apply economic principles to incorporate data and data analytics into a company's value proposition
- Consider the ethical implications of data applications
- Understand the basic structure of a relational database
- Use data summarization and visualization to produce first order insights
- Design surveys and experiments and analyze the resulting data
- Understand the foundations of network analysis
- Apply methods on observational data to arrive at causal inferences
Policies
Attendance
Students are reminded that attendance at all sessions is compulsory and is constantly monitored. Students who fall beneath the minimum attendance requirement may be subject to barring, suspension or termination of study. Students should refer to the online timetable for details of venues for teaching, and for the timing of lectures and/or seminar groups, labs or other additional sessions which are integral to the module delivery.
Connected Learning
All students are expected to play an active role in their own learning. The more you put into your studies by engaging with the readings and preparatory materials, the more you will take away from the module. Individual contributions to the classroom environment will form an important part of your programme experience. In recognition of this, you are encouraged to play an active role in class, by making appropriate contributions, answering questions and asking questions yourself. There are two important principles behind this:
- To facilitate an environment whereby students can share relevant thoughts, insights and experiences which advance discussions and the general learning in class.
- To assist in the development of your skills in being able to ‘think on your feet,’ develop a succinct argument as well as learning how to challenge peers in a constructive way. Such skills will serve you well throughout your career.
High-quality contributions are meaningful, thoughtful, relevant remarks or questions that enhance everyone’s understanding of the case or concepts discussed, and do not have to be “correct.” In fact, seemingly erroneous comments can be highly valuable as well. If by nature you are a quiet person and would like to be called upon in class, please let us know and we will help draw you into the class discussions.
Student Code of Conduct
Students are also expected to follow UCL’s Code of Conduct for Students, particularly;
- Being punctual for classes, labs, seminars and other appointments
- Informing the relevant person if you are going to be absent or delayed for an activity where you are expected to attend
- Being aware of the advice and assistance available on academic and other matters from sources such as personal, programme and Departmental Tutors
- Seeking help for yourself when you need it
Students should also complete any required exercises, pre-reading or other additional tasks prior to each session, so that you arrive ready to engage with the lesson objectives and learning outcomes, and your peers.
Academic Integrity
In line with UCL regulations, you are expected to maintain the highest levels of academic integrity in all the work you produce in this module. To esnure you maintain these standards in your work, make sure you are fully informed on all types of academic misconduct. Ignorance of misconduct is not an excuse.
Technology
- Phones. Mute/off and placed in bags or packs i.e. far away from your hands
- Laptops
- During discussions/lectures: no laptops (if you have circumstances requiring an exception, please see me)
- During lab sessions: OK
- Tablets. Flat on desk with wifi off i.e. use like a notebook
Guests
Guests are welcome to attend class. Friends, colleagues who are interested in the program, and family are all invited. Please email me the name of your guest at least one day before class (to ensure they have access to the building). At the start of class I will ask you to introduce the guest to your peers and I encourage the class to make our guests feel welcome. I request that you inform your guests to remain as observers during the class.
Food and Drink
In line with the UCL School of Management norms, please refrain from bringing food or hot drinks (water bottles are OK) into the class.
Assessment
Assessment is based on two individual assignments (25%+40%) and a team project (35%). Deadlines and details of the assignments are provided in Moodle. A numeric mark scheme is used for this module.
Assessment is integral to learning and teaching; it must be used as a method of developing students’ knowledge and understanding, as well as measuring attainment. Assessment tasks must enable students to demonstrate the extent to which they have attained or exceeded the intended learning outcomes.
All modules have an approved format, duration, and/or length of their assessment(s). Modules may also sometimes be provided at different FHEQ levels, and as such should have differentiated assessment patterns for each FHEQ level they offer (even if all levels are taught together).
Weekly Requirements
In the class Moodle page, you will find the following resources for each week:
- This Week's Resources. Contains slides for the online video lectures, weekly readings, and other information that is relevant to the week.
- Video Lectures. These are two "books" that contain a series of video lectures for (A) strategy and (B) analytics. Each "book" is a series of videos (organized as "chapters"). Use the right hand side navigation (or the arrows) to navigate through all the videos.
- Class Preparation. This section contains materials you will need to have ready when you walk into class. Typically, this will include the mini-case and any software or web applications you will need to set up.
Each week, you must complete the following before class:
- Watch the lecture videos. These videos are your required "reading" each week.
- In the Class Participation section, read the mini-case and make sure you have set up any software or web applications we need for that week.
- For most weeks, you will be asked to read and prepare a mini-case. Each week, the mini-cases will serve as the basis of our in-class discussion and exercise. Reading the mini-case in advance and considering the exercise before you come to class are a critical part of your preparation.
- Note: the weekly readings in the reading list are not mandatory. They are there for your reference if you need an additional resource or you want to take a deeper dive into the material.
After each lecture, I will post:
- The Lecturecast of the class session.
- Whiteboard notes and wrap-up slides.
Schedule
A summary of the weekly topics and mini-cases is posted below.
| Week | Strategy | Analytics | Mini-Case |
|---|---|---|---|
| 1 | Business models | Data wrangling | Homegrown—The (Data) Structure of a Business Model |
| 2 | External environment | Descriptive analytics | Home Depot's Changing Environment |
| 3 | Inside the firm | Surveys | School of Management Cohort Survey |
| 4 | Organizational design | Networks | IdeaWeb—Understanding Workplace Networks |
| 5 | Financial performance | Regression | Airbus A380—Data-Driven Decision Making... Under Pressure |
| 6 | Entrepreneurial strategy | Experiments | Opinionistas—One Idea, Multiple Strategies |
| 7 | Platforms | Causal modelling | LoveMyPet—Causal Modelling of Platform Strategies |
| 8 | Data-based businesses | Observational data I | ProLinked—Estimating the Effect of Recommendations |
| 9 | Diversification | Observational data II | RevCo's Shopper's Club—Acquisition Synergies |
| 10 | The strategy leader | Data ethics |
Datasets and Data Repositories
If you want to look for an existing dataset for various assignments, I have assembled an list of data sources you can use as an initial point for your search.
I encourage you to explore these data sources. There are many interesting datasets here that will pull your project ideas in different directions (and move you away from the standard Kaggle dataset).
Recommended Books
There are no required books assigned for class. Here is a selection of books to complement what you will see during the term.
- Strategy
- Grant, R. M. (2016). Contemporary Strategy Analysis. UK: John Wiley & Sons Ltd. (UCL access)
- Puranam, P. and B. Vanneste (2016). Corporate Strategy. Cambridge, UK: Cambridge University Press. (UCL access)
- Zenger, T. (2016). Beyond Competitive Advantage. Boston, MA: Harvard Business Review Press. (UCL access)
- Analytics
- Cunningham, S. (2018) Causal Inference: The Mixtape. Python code for textbook examples can be found on Github.
- Diez, D. M., C. D. Barr, and M. Cetinkaya-Rundel (2015). OpenIntro Statistics. OpenIntro.
- Few, S. (2012). Show Me The Numbers: Designing Tables and Graphs to Enlighten. Burlingame, CA: Analytics Press.
- Gábor, B. and G. Kézdi (2021). Data Analysis for Business Economics and Policy. Cambridge, UK: Cambridge University Press.
- Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch (2016). Impact Evaluation in Practice. World Bank Group.
- Glennerster, Rachel and K. Takavarasha (2013). Running Randomized Evaluations: A Practical Guide. Princeton: Princeton University Press. (UCL access)
Time Commitment
This module is worth 15 credits. For this module, the expected time commitment is as follows:
| Activity | Workload (in hours) |
|---|---|
| Lectures | 30 |
| Preparation | 40 |
| Assignments | 60 |
| Revision | 20 |
| Total | 150 |
Note: Module credit correlates to the total number of learning hours e.g., a 15-credit module will have 150 hours, or and 30-credit module 300 hours. Each module will break this student learning time down into various activities/content over its duration (which may take the form of ‘contact time’ and ‘independent study’ time).