Specialization Business Analytics and Operations Research
The new Specialization Business Analytics and Operations Research provides students with the theory,
the quantitative skills, and the soft skills that are needed to identify, evaluate and exploit opportunities
that can create value to businesses or society. Specifically students will acquire:
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Specialized knowledge of theory, methods and techniques needed to solve complex business
or economics problems, with focus on:
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The “hard” skills needed to be able to successfully implement prescriptive and predictive
analytics methods and techniques in practice. This involves:
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Methods and techniques for analyzing big data, and/or making predictions (predictive
analytics).
Optimization methods and techniques (prescriptive analytics).
Translating a practical problem into a mathematical or statistical model and choosing
the right method to solve the problem.
Being able to use state-of-the-art computer software.
The “soft” skills that are crucial for the successful implementation of business projects.
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The curriculum is as follows:
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Choose 5 core courses (cluster A):
 Optimization (Unit 1)
 Simulation (Semester 1)
 Decision Making with Business Analytics (Semester 1)
 Data Science Methods (Unit 3)
 Supply Chain Analytics (Unit 3)
 Professional Business Analytics Skills (Semester 2)
30 ECTS
6 ECTS
6 ECTS
6 ECTS
6 ECTS
6 ECTS
6 ECTS
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Choose 2 BAOR electives (cluster B)
12 ECTS
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Master thesis
18 ECTS
60 ECTS
The remainder of this document provides information regarding the contents of the core courses, and
the list of available electives.
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Optimization
Learning goals: Optimization plays a crucial role in Business Analytics. It enables decision makers to
optimize business processes or make optimal policy decisions. Optimization techniques lie in the core
of many prescriptive business analytics methods, as these methods typically require solving
optimization problems. Students will acquire specialized knowledge of theory, methods and techniques,
to solve complex nonlinear optimization problems and robust optimization problems. Students also
acquire the skills necessary to implement optimization methods and techniques in practical problems.
This includes translating the problem at hand in an optimization model, and being able to use state-ofthe-art software.
Teaching methods: lectures and computer labs. There will be 7 to 10 labs where students will get handson experience with several implementations of nonlinear optimization software. The use of these
implementations will include the use of MATLAB, as well as the YALMIP toolbox and several solvers.
Contents: The emphasis of the course is on continuous, non-linear optimization. The objective of
nonlinear optimization is to find the best possible solution in non-linear decision models. Nonlinear
optimization is an area of great theoretical challenges, as well as of recognized practical relevance. In a
series of lectures, first we will briefly review the most important model types and solution approaches.
This will be followed by a more detailed discussion of optimization algorithms. The last part of the course
will be devoted to robust optimization: a modern approach to solve optimization problems with
uncertain data.
Particular emphasis will be given to applications in various fields, including data science, finance,
marketing, supply chain optimization, etc... In each of these fields, non-linearity and uncertainty in
objectives occur in a natural way.
Topics include:
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Introduction to non-linear programming. Optimality conditions. Applications of nonlinear
programming.
Convex sets and functions. Convex optimization. Lagrangian duality and KKT conditions.
Examples of convex optimization; linear and quadratic programming; second order cone
programming.
Algorithms for unconstrained and constrained optimization: Line search, first order methods,
and Newton's method.
Robust optimization: Deterministic counterpart of linear constrains with uncertainty.
Polyhedral-uncertainty, Bertsimas-Sim uncertainty, ellipsoidal-uncertainty.
Applications of robust optimization.
Compulsory Reading:
1. Stephen Boyd and Lieven VanderBerghe, Convex Optimization, Cambridge University
Press 2004. Available at: http://www.ee.ucla.edu/~vandenbe/cvxbook.html
2. Aharon Ben-Tal, Laurent El Ghaoui and Arkadi Nemirovski, Robust Optimization,
Princeton University Press 2009. Available at:
http://www2.isye.gatech.edu/~nemirovs/FullBookDec11.pdf
Recommended Prerequisites: Linear Optimization, Linear Algebra.
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Simulation
Learning goals: Simulation is a key tool in predictive analytics. Students acquire specialized knowledge
of theory, methods and techniques for simulation. Students also acquire the skills necessary to
implement simulation methods and techniques in practical cases. This includes the use of state-of-theart simulation software.
Teaching methods: lectures and computer labs.
Contents: The course covers the following topics:
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Basic Simulation Modeling and Modeling Complex Systems
Simulation Software, in particular Arena
Verification and Validation
Selecting Input Probability Distributions
Generating random numbers and random variates
Output data analysis and comparing scenarios
Variance Reduction Techniques
Experimental Design, sensitivity analysis, and optimization
Agent-based simulation
Combined discrete-continuous simulation
Compulsory Reading:
1. A.M. Law, Simulation modeling and analysis, McGraw-Hill, New York, 2015, 5th edition. The whole
book is mandatory reading for the written exam.
2. W.D. Kelton, R.P. Sadowski and N.B. Zupick, Simulation with Arena, McGraw-Hill, New York, 2015,
6th edition. This book covers both the general principles of simulation and the Arena software. It
is NOT required reading for the written exam. It is useful for making the two required homework
assignments.
Recommended Prerequisites: Probability and statistics, Stochastic Operations Research Models
3
Decision making with Business Analytics
Learning goals: Business analytics (BA) is a broad field that includes both prescriptive analytics (e.g.,
optimization) and predictive analytics (e.g., simulation and data science methods). Broadly speaking,
the field covers quantitative methods and techniques that are essential to identify, evaluate and exploit
opportunities that can create value to businesses or society. Students who complete this course will:
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have insight in why and how BA plays such an important role in industry nowadays;
have broad knowledge of advanced BA models and methods;
be able to decide which model/method to use when;
be able to use advanced BA methods to solve decision problems in practice;
be able to use state-of-the art BA software.
Teaching methods: lectures, assignments and labs.
Contents: The course will cover the following topics:
1. What is BA?
a. Predictive / prescriptive analytics
b. Role/impact in industry
2. BA models/methods:
a. Overview of models/methods from other BAOR courses
b. Other important advanced BAOR models/methods
c. When to use which model/method?
d. Important modelling issues / validation / verification
e. Impact of Big Data (Volume/Velocity/Variety/Veracity) on models/methods
3. BA software
4. Application areas:
a. Supply chain management/ Finance /Marketing
b. Medical field / Engineering field / …
Compulsary reading: Selected book chapters and papers will be handed out.
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Data Science Methods
Learning goals: Currently, datasets in finance, marketing, economics are becoming larger, both in terms
of number of observations and variables collected. Large datasets require new statistical and
computational methods for extracting the relevant information and making informed business
decisions. Students will acquire specialized knowledge of statistical and machine learning methods for
large data. Students will also acquire the skills necessary to implement methods and techniques in
practical cases in a variety of fields, including, e.g., finance, marketing and economics. For example, the
acquired skills will allow you to make predictions of asset returns, sales, crime rates, defaults, or car
accidents, using large datasets and machine learning techniques. You will (learn to) use the free
software package R.
Teaching methods: lectures and computer labs.
Contents: The course will cover the following topics:
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Clean, summarize and visualize large data using dimension reduction techniques and visualization
(principal components, clustering, pattern recognition). For example, you want to summarize your
customer base into representative groups.
How to select a good model for prediction (among many models), using statistical model selection
(forward model selection, lasso, shrinkage, regression trees). For example, you want to predict
sales, or returns, or wage, but you have a lot of predictors and only some matter.
How to select a good model for classification, using computational algorithms (classification trees,
bagging, boosting, support vector machines). For example, you want to predict default based on
some “diagnostics of credit health”, which you organize into a decision tree.
Causal inference and big data techniques (time permitting).
In choosing models for big data, special attention will be paid to economic and business intuition.
Grading: 80% exam, 20% empirical (computer) assignments. The assignments count for the final grade
of the initial exam and also of the first resit.
Compulsory Reading:
Gareth, Witten, Hastie, Tibshirani- An Introduction to Statistical Learning with Applications in R,
(available for free at http://www-bcf.usc.edu/~gareth/ISL/).
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Supply Chain Analytics
Learning goals: Within the broad field of Business Analytics, supply chain management plays a dominant
role. Students will acquire specialized knowledge of quantitative models and techniques for supply chain
management.
Teaching methods: lectures, assignments and/or labs.
Contents: The course will cover the following topics:
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Demand & operation planning
o Demand management (forecasting)
o Production planning
o Scheduling
Multi-echelon Inventory
o Deterministic and the stochastic nature
Distribution & transportation
o Extensions of the VRP
o Time definite service networks
o Zone clustering
Performance modeling of storage and material handling systems
o Warehouse design modeling
o Automated handling design optimization
Service optimization
o Yield and revenue management
o Contracting optimization
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Professional Business Analytics skills
Learning goals: in addition to strong quantitative skills taught in the other BAOR courses (algorithms,
models, data analytics and IT-skills), business analytics professionals also need strong “softer” skills. The
course focuses on those softer skills that are essential for the successful implementation of business
analytics in practice. The course is strongly recommended for those who want to work in the practice
as an internal or external consultant in Operations Research or Finance.
Teaching methods: lectures, case solving, presentations and role play. The course is taught by (3)
teachers who all work both at the University and in practice, and have a lot of experience.
Contents: You will work around a case in a group of at most four students. In addition to the modelling
part of the case, focus will be on consultancy skills such as acquisition interviews, scope interviews,
understanding how managers decide, presenting a project plan, presenting the results, and dealing with
resistance to change. The lectures will cover theory on the following topics:
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Steps in a business analytics project; the decision quality methodology from Stanford
Acquisition interviews and scope interviews
Law and ethics
Data techniques
Modeling and validation techniques
Change management & leadership
Presentation skills
Grading: The final grade is based on two interviews (together 20% of the grade), one project description
presentation (20%), one presentation on the analysis of the findings (20%) and one final elevator pitch
including implementation aspects (20%).
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BAOR Electives
You choose two electives of 6 ECTS each. Through your choice of electives, you can acquire specialized
skills of business analytics methods and techniques in a specific field of your interest, e.g., finance,
marketing or economics, or you can extend your technical skills in data science or optimization methods
and techniques.
Suggested electives are:
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The core course that you did not choose under Cluster A
Finance Analytics :
o Asset Liability Management
o Valuation and Risk Management (formerly Financial Models)
Marketing Analytics:
o Customer Analytics
o Market assessment
Econometrics techniques:
o Core courses of the MSc Econometrics and Mathematical Economics
Information Management:
o Business Analytics and Emerging Trends
Optimization theory and methods (courses offered by LNMB):
o Scheduling
o Advanced Linear Programming
o Queueing Theory
o Discrete Optimization
For the course contents, please see the Electronic Study Guide. Students who wish to choose an elective
not mentioned in this list need to ask for approval by the Examination Committee.
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