Syllabus for Business Strategy and Analytics in Masters of Business Analytics
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Business Strategy and Analytics Syllabus

Instructor: Anil Doshi

  • Office: Canary Wharf (Office SW6)
  • Email: anil[dot]doshi[at]ucl[dot]ac[dot]uk
  • Office hours: Wednesdays, 16:00 to 17:30, and by appointment.

Module Details

  • Module ID: MSIN0093 (UCL Moodle)
  • Module description
  • Offered: Fall 2018, Term 1
  • TAs (Office hours by appointment. Please email to schedule.)
    • Andrew Montandon (a[dot]montandon[at]ucl[dot]ac[dot]uk)
    • Sayan Sarkar (ssarkar[at]london[dot]edu)
  • Sections:
    • Section 1: Bloomsbury Gordon Street (25) Maths 505, Tuesdays, 8:30 to 11:30
    • Section 2: Canary Wharf Northeast Lecture Theater, Tuesdays, 13:00 to 16:00


  1. Attendance. Attendance is critical. If you have extenuating circumstances that are going to keep you out of class, please inform me in advanced as early as possible.

  2. Class decorum. Treat our time in the classroom as you would meeting with the executive team or board of directors of your company. This means listening to and respecting the perspectives of your peers. It also means speaking up and disagreeing when you have a different opinion.

  3. Class participation. This module depends on the participation of all the students. Active engagement (i.e. actively discussing and listening to your classmates' points) promotes a more lively discussion and debate. It is in the exchange of ideas that new and interesting ways of thinking about the concepts in the module emerge.

With that in mind, I may cold or "warm" (where students whose names are on the board in the beginning of class are on notice and will be asked to participate in the discussion at certain points) call in class. I understand that some people may find public speaking challenging for a variety of reasons. If this is the case, please see me.

  1. 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
  2. Class Norms. I would like to make explicit some norms that I hope are embraced in the classroom.

    • Arrive to class on time and prepared to contribute
    • Take risks with your ideas
    • Speak in class if you have a question
    • Embrace failure and the iterative nature of learning and discovery
    • And finally, do not wait until you have the perfect comment to raise your hand and speak in class
  3. 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 the day before class. 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.

  4. Food and Drink. In line with the UCL School of Management norms, please refrain from bringing food or drinks (water bottles are OK) into the class.


Assessment is based on an individual assignment and a team project. The deadlines and weights of each assignment are summarized in the schedule.

Individual assignment (60%)

The individual assignment is a term-long assignment.

Team project (40%)

Over the term, students will produce a team project. Teams will consist of four to five students.

The project will consist of three milestones:

  1. Idea generation phase. On week 3, submit a short one page document that includes names of each team member and a brief description (approx. one to two paragraphs each) of the team's three most exciting project proposals. I will hold optional meetings to discuss your ideas and provide any guidance you might request.

  2. Initial Draft. On week 6, submit a preliminary draft of the writeup. This does not have to be a complete draft. It is meant to be a forcing mechanism for you to make progress during the term and it provides you with an opportunity to assess the prospects of the project. Again, I will hold optional meetings to discuss progress, questions, problems, etc.

Note: although milestones 1 and 2 are not formally assessed, failure to submit will heighten the risk of the assessment of the final submission, as they are an integral part of completing the group project over the course of the term.

  1. Final project presentation/slides (15%) and writeup (25%). In week 10, each team will have the opportunity to present their work to the class and receive feedback. Presentations will run ten minutes each. The team can then incorporate comments into their final writeup (approximately 5 pages, excluding cover page, references, works cited, etc.), which will be due at the end of the week. At the end of the writeup, there should be an explicit attribution of labor among the team members.

Your team may choose one of the following types of projects. The project scope is flexible. If your team find that what you would like to do does not easily fit into either project type, I invite you to discuss your idea with me.

  1. Data project. You are the data team of your firm and you are presenting a research project to executive management as a means for better understanding a strategic aspect of the company, transactions, relationships between various activities of users, etc. Identify a dataset of interest to your team. Frame your study question and then conduct the appropriate analysis, provide a result and next steps (e.g. policy recommendation, follow on agenda).

  2. Data-based business proposal. You are a startup team and you are pitching a new business idea to an audience of startup mentors and investors. Your startup involves some data-based element as part of its services, business model, or strategy formation. Craft a business plan and pay particular attention to how the concepts in this module relate to the value proposition of your startup.

Honor Code

By taking Business Strategy and Analytics at the UCL School of Management, you are agreeing to maintain a high level of professional integrity. For your work, this means citing sources wherever appropriate and producing work that is your own. Cheating, plagiarizing, or misrepresenting one's work are a violation of our classroom's aspirational norms and the university's code of conduct.


Because we focus on underlying principles, I do not require the use of any specific software or tools. For required work, it is the student's responsibility to understand the problem, identify a path towards solution, and implement that path how he or she sees fit.

If you intend on using a programming language to produce your work, I highly suggest R and/or Python. Each has its comparative strengths and those most immersed in analytics in industry typically work with both languages. Here are a few resources for both languages:

Also, here are a few applications and environments that are typically used in industry.

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.

Recommended Books

Here is a selection of books to complement what you will see during the term.

  1. Strategy and Economics
  • Grant, R. M. (2016). Contemporary Strategy Analysis. UK: John Wiley & Sons Ltd.
  • Harris, J. and Lenox, M. (2013). The Strategist's Toolkit. Virginia, USA: Darden Business Publishing.
  • Zenger, T. (2016). Beyond Competitive Advantage. Boston, MA: Harvard Business Review Press.
  1. Data Summarization, Graphing, and Visualization
  • Berinato, S. (2016). Good Charts. Boston: Harvard Business Review Press.
  • Few, S. (2012). Show Me The Numbers: Designing Tables and Graphs to Enlighten. Burlingame, CA: Analytics Press.
  1. Probability, Statistics and Econometrics
  • Angrist, J.D. and J. Pischke. (2014). Mastering 'Metrics: The Path from Cause to Effect. Princeton: Princeton University Press.
  • Angrist, J.D. and J. Pischke. (2009). Mostly Harmless Econometrics. Princeton: Princeton University Press. Note: More advanced treatment of material from Angrist and Pisckhke (2014).
  • Cunningham, S. (2018) Causal Inference: The Mixtape.
  • Diez, D. M., C. D. Barr, and M. Cetinkaya-Rundel (2015). OpenIntro Statistics. OpenIntro.
  • Morgan, S.L. and C. Winship (2007). Counterfactuals and Causal Inference. Cambridge: Cambridge University Press.


A summary of the schedule and assignment deadlines is posted below. Note all assignments are due at 10:00am.

Week Date Topic Assignment Weight
1 2 Oct Module Introduction
Problem Framing
2 9 Oct Foundations of Strategy
Data Basics and Summarization
3 16 Oct Entrepreneurial Strategy
Data and Platform Strategy
Team Ideas Milestone
Optional Team Meetings (17 Oct - 19 Oct)

4 23 Oct Experiments
5 30 Oct Regression
Causal Graphs I
Reading Week
6 13 Nov Causal Graphs II
Causal Analysis I
Team Draft Milestone
Optional Team Meetings (14 Nov - 16 Nov)

7 20 Nov Causal Analysis II
8 27 Nov Causal Analysis III
9 4 Dec Data Ethics Individual Assignment (due 7 Dec 10:00) 60%
10 11 Dec Group Presentations
Module Wrap Up and Evaluations
Team Project (Slides and Writeup) (Due 14 Dec 10:00) 40%

Reading List

Please visit the UCL Reading List to see the readings for this module.