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# MBA Big Data & Business Analytics Course Outlines

#### Analytics courses

• Primer Statistics (online)

A good understanding of statistics will be essential in the MBA Big Data & Business Analytics programme. Students who need to brush up their skills prior to this MBA, we recommend to join the (online) introduction to basic statistics course taught by the University of Amsterdam.

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course students will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare the student for the next course in the MBA Big Data - the course Statistics.

• Statistics

Lecturer: dr. N.P.A. van Giersbergen

#### After this course, the student should be able to:

• Identify Big Data problems that require statistical techniques;
• Apply the statistical techniques correctly on Big Data problems;
• Understand the properties of these techniques, and the role of assumptions;
• Interpret the conclusions properly;
• Program in “R”.
• Econometrics

Lecturer: Prof. dr. H.P. Boswijk

#### After this course, the student should be able to:

• Translate economics and business questions into econometric models and hypotheses;
• Analyze discrete choice data, panel data, time series;
• Interpret the conclusions properly, and understand the role of assumptions;
• Use the statistical programming language R for econometric model building.
• System optimization

Lecturer: Prof. dr. G.M. Koole (VU - Mathematics & Computer Science)

#### After this course, the student should be able to:

Optimize deterministic systems

• Being able to model business problems as optimization problems;
• Recognize (Mixed-Integer) Linear Programs (MILP's);
• Use Excel and AIMMS to program and solve MILP’s;
• Interpret the results.

Optimize stochastic systems

• Understand the role of uncertainty in business problems;
• Understand basic models for capacity planning and the role of uncertainty;
• Develop simulation models using simulation software;
• Interpret the results.
• Data Stewardship

Lecturer: dr. N.P.A. van Giersbergen

### After this course, the student should be able to:

• Have good insight in the opportunities and challenges posed by the collection of data in an internet-based, dynamic and constantly evolving context;
• Have good understanding of the various steps (such as searching, capturing, storage and pre-processing) that need to be taken to make this type of data suitable for subsequent advanced statistical and machine learning techniques;
• Be able to apply fundamental techniques of data cleaning (like finding inconsistencies, duplicates, replacement using regular expressions);
• Know the principles of web-specific data-collection techniques such as APIs and scrapers;
• Have knowledge of statistical strategies to deal with missing observations and outliers;
• Have a basic understanding of the Python programming language.
• Quantitative Marketing

Lecturers: dr. M. Soomer (ORTEC & UvA)

#### After this course, the student should be able to:

• Study and analyse relevant marketing questions in today’s (online) world;
• Apply quantitative techniques for making data-driven marketing decisions.

• Big Data Strategy & Implementation

Lecturer: M. Heijnsbroek MBA (MIcompany)

#### After this course, the student should be able to:

• Drive focus on the critical Big Data opportunities (goal);
• Assess readiness on opportunity capture, metrics and models, technology, and people (situation);
• Develop a coherent vision and road-map to capture (direction);
• Lead a Big Data initiative to success (execute).

#### After this course, the student should be able to:

• Develop insights into personal strengths, weaknesses, core values and development priorities;
• Develop the ability to inquire and advocate in an effective way;
• Understand and apply different styles of influencing with integrity;
• Reflect on the effectiveness of one's leadership behaviours by applying practical concepts;
• Develop insights into importance of diversity and inclusiveness in leadership;
• Create a culture of learning and giving/receiving high quality feedback;
• Generate an effective team charter in order to maximise the impact of team work.
• Consumer Behaviour

Lecturers: J. Demmers MSc, R.E.W. Pruppers MSc & dr. M. Vock

#### After this course, the student should be able to:

• Understand core theories from related fields (e.g. psychology, behavioural economics) that are central to comprehending consumer behaviour;
• Analyse how these theories are applied and adapted to fit the marketing/consumption context;
• Evaluate academic research on consumer behaviour topics;
• Apply consumer behavior concepts to real-life business cases related to organizations’ marketing strategies;
• Present and discuss their analyses (formally and informally) in a manner that benefits fellow students' understanding and learning experience.
• Law & Ethics for Big Data

Lecturers: E. Visser LLM (Project Moore) & O. van Daalen LLM

#### After this course, the student should be able to:

• Understand principles of privacy and data protection;
• Identify the possible risks of Big Data for privacy;
• Perform a law/ethics compliance scan;
• Understand technologies to minimize privacy risks;
• Design privacy-friendly systems and services;
• Understand that proper communication and transparency is key.
• Operations & Supply Chain Management

Lecturer: Prof. dr. J.A.A. Veen

#### After this course, the student should be able to:

• To understand O&SCM issues in general business context;
• To understand the importance of O&SCM, as well as the need for an integrated vision on O&SCM in any organisation;
• To understand the linkages of Operations and Supply Chain to other business areas;
• To be able to use tools and techniques in O&SCM environments;
• To identify, analyse and resolve typical problems that arise in managing Operations and Supply Chain;
• To be able to understand and resolve O&SCM implementation issues.
• Financial Accounting

Lecturer: dr. G. Georgakopoulos

#### After this course, the student should be able to:

• Understand the ‘language’ of business, its uses and limitations;
• Interpret and understand the impact economic events have on the Balance Sheet, Income Statement and Statement of Cash Flows;
• Understand and describe the measurement theories used in financial accounting;
• Recognize how financial statements communicate economic events to third parties (i.e. owners, investors, creditors) and the impact this information has on them.
• Valuation

Lecturer: dr. T. Yorulmazer

#### After this course, the student should be able to:

• To understand concepts of time value of money, arbitrage, CAPM  and (N)PV and to be able to apply this to the evaluation of capital budgeting decisions and company valuation;
• To be able to reflect on the limitations of the NPV approach;
• To analyse, report and present business cases on valuation, capital budgeting and capital structure;
• To calculate the appropriate WACC for capital budgeting decisions.
• Corporate Strategy

#### After this course, the student should be able to:

• Knowledge. To encourage the understanding of the many, often conflicting, schools of thought and to facilitate the gaining of insight into the assumptions, possibilities and limitations of each set of theories and issues relevant to the topic of corporate strategy;
•  Skills. To develop the course participant's ability to define strategic issues, to critically reflect on existing theories, to creatively combine or develop theories where necessary and to flexibly employ theories where useful;
• Attitude. To instill a critical, analytical, flexible and creative mindset challenging organizational, industry and national paradigms and problem-solving recipes.
• Entrepreneurship

Lecturer: P.M. van der Fluit MBA MSc

#### After this course, the student should be able to:

• To understand the core concepts and models of entrepreneurship in both new ventures and large existing companies (intrapreneurship);
• To analyse and understand key challenges of innovation and launching new digital products and services including innovations organize to execute issues within larger organizations;
• To analyse how companies execute techniques from the start-up and venture world;
• To collaborate in a team and create and present a new offering that solves a real business need in a complex organisation, including a business model;
• Via a case study of GE industrial internet, learn how the largest industrial company in the world is turning themselves into becoming the Digital Industrial Company;
• About the ’transition gap’ - the phase between ’lean start-up’ and ’crossing the chasm’, a critical phase which prevents some start-ups from growing to their full potential.
• International Study Trip: Entrepreneurship and Innovation in Silicon Valley

Lecturers: Prof. dr. M. Salomon

#### The Silicon Valley Study Trip is meant for students to:

• Learn how the Silicon Valley ecosystem stimulates innovation and entrepreneurship;
• Understand the role of the following in the Silicon Valley ecosystem:
• universities such as Stanford;
• accelerators, incubators, venture studios;
• venture capital;
• Understand what it takes to start a business in the United States of America.
• Corporate Finance

Lecturer: dr. S. Terovitis

#### After this course, the student should be able to:

• Have achieved a rigorous working knowledge of key issues in finance;
• To understand the accepted theoretical foundations and conceptual underpinnings of finance and financial economics;
• To apply concepts and ideas in practical business situations.

Lecturers: dr. C.T. Boon & dr. R.D. Ronay​

#### After this course, the student should be able to:

• Describe, reproduce and critically evaluate the theoretical arguments underpinning
• the importance of leading and managing people;
• how to lead and manage people effectively;
• managing teams and team diversity effectively;
• managing culture and change;
• Apply these theories to firms by analyzing people-related business problems in cases and exercises.
• Fintech: Blockchain & Cryptocurrencies

Lecturer: R. Schwentker

#### After this course, the student should be able to:

• Acquire an overview of digital currencies, blockchains, and distributed ledger technology;
• Learn about potential applications of distributed ledger technology to new products and services;
• Explore blockchain technology and its potential to provide faster, cheaper, and more secure financial transactions;
• Understand the opportunities and risks from smart contracts and other emerging technologies.

#### Computer Science Courses

• Data Visualisation

Lecturers: Dr. W. van Hage (Synerscope) & Prof. dr. M. Worring

#### After this course, the student should be able to:

• Understand the purpose of various types of data visualisation, ranging from info-graphics to visual analytics
• Understand the applicability of various visualisation techniques
• Use visualization tools to perform visual analysis
• Big Data Infrastructures & Technology

Lecturers: Y. Demchenko

#### After this course, the student should be able to:

• Understand the functionality and limitations of different databases;
• Modern analytical relational databases (“column-stores”, “NewSQL”);
• Modern non-relational databases (“key-value stores”, “NoSQL”);
• Databases designed for big data analytics;
• Understand data management tools;
• Employ databases in big data analytics
• Machine Learning

Lecturers: Prof. dr. M. Worring & dr. S. Rudinac

#### After this course, the student should be able to:

• Understand methods from machine learning, in particular; (decision trees and decision forests, clustering and topic modeling, logistic regression and deep learning, matrix factorization and times series analysis & spatio-temporal event modeling);
• Apply the methods in advanced techniques (text analytics, image and video analytics and recommendation);
• Apply the techniques in large scale use-cases.
• Language Technology

#### Lecturer: dr. E. Kanoulas After this course, the student should be able to:

• Collect, represent, and algorithmically process textual data;
• Describe and explain text classification and ranking algorithms, relate them to each other, identify differences and similarities;
• Describe and compare different evaluation methods, and use them to perform experiments and measure the effectiveness of algorithms;
• Analyse the experimental results, perform failure analysis and draw conclusion.