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MBA Big Data & Business Analytics

Course Outlines

Our curriculum is a combination of analytics, business and computer science. Offering you all the right skills to make you a multidisciplinary expert in the big data field. Click on a course to find out what knowledge and skills you will acquire.

Analytics courses

  • Primer Statistics (online)

    A good understanding of statistics is essential in the MBA Big Data & Business Analytics programme. It is important to understand research in the social and behavioural sciences. If you need to brush up your skills prior to this MBA, we recommend to join this course taught by the University of Amsterdam.

    After this course, you should be able to understand the basics of statistics: how to calculate and evaluate results.

  • Statistics

    After this course, you 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;
    • implement techniques in the programming language R.

    Lecturer: dr. N.P.A. van Giersbergen

  • Econometrics

    After this course, you should be able to:

    • translate economics and business questions into econometric models and hypotheses;
    • analyse discrete choice data, panel data, time series;
    • interpret the conclusions properly, and understand the role of making assumptions;
    • Use the statistical programming language R for econometric model building.

    Lecturer: prof. H.P. Boswijk

  • Optimisation for Business & Society

    After this course, you should be able to: 

    - Optimise deterministic systems:

    • Being able to model business problems as optimisation problems;
    • Recognise (Mixed-Integer) Linear Programmes (MILPs);
    • Use Excel and AIMMS to programme and solve MILPs;
    • Interpret the results.

    - Optimise 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.

    Lecturers:  dr. J. Berkhout MSc, prof. dr. G.M. Koole

  • Data Stewardship

    After this course, you should be able to:

    • have 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 needed to make this type of data suitable for 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.

    Lecturer: dr. Y. He

  • Data Science for Online Marketing

    After this course, you should be able to:

    • study and analyse relevant marketing questions in today’s (online) world;
    • apply quantitative techniques for making data-driven marketing decisions;
    • know what kind of consumer data are available within an (online) company.

    Lecturer: dr. M. J.  Soomer (ORTEC & UvA)

Business courses

  • Big Data Strategy & Implementation

    After this course, you 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).

    Lecturer: M. Heijnsbroek (MIcompany)

  • Amsterdam Leadership Programme

    After this programme, you should be able to:

    • develop insights into your 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 personal 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.

    Lecturer: J. Nuijten, MSc. MBA

  • Consumer Behaviour

    After this course, you 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 behaviour concepts to real-life business cases related to organisations’ marketing strategies;
    • present and discuss your analyses (formally and informally) in a manner that benefits fellow students' understanding and learning experience.

    Lecturer:  Mw. dr. A. Weihrauch

  • Law & Ethics for Big Data

    After this course, you should be able to:

    • understand the international principles and values concerning privacy and data protection;
    • understand the basics of international privacy laws, including the Privacy Regulation;
    • be aware of the possible threats and risks of big data for privacy;
    • be aware of the available methods and standards to design privacy-friendly systems and services;
    • understand that technologies can help to avoid or minimise privacy risks and threats;
    • perform a big data maturity scan to assess the level of compliance within the organisation;
    • understand that proper communication and transparency towards data subjects is key.

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

  • Operations & Supply Chain Management

    After this course, you should be able to:

    • understand O&SCM issues in general business context;
    • understand the importance of O&SCM and the need for an integrated vision;
    • understand the linkages of  O&SCM to other business areas;
    • use tools and techniques in O&SCM environments;
    • identify, analyse and resolve typical problems that arise in managing the operations and supply chain;
    • understand and resolve O&SCM implementation issues.

    Lecturers: dr. R. Goedhart, dr. A. Kuiper

  • Financial Accounting

    After this course, you should be able to:

    • understand the ‘language’ of business, its uses and limitations.
    • interpret and understand the impact of economic events on the balance sheet, income statement and statement of cash flows.
    • understand and describe the measurement theories used in financial accounting.
    • recognise how financial statements communicate economic events to third parties (i.e. owners, investors, creditors) and the impact this information has on them.

    Lecturer: D. Jullens, MSc

  • Fintech: Blockchain, Cryptocurrencies & Smart Contracts

    After this course, you should be able to:

    • Acquire an overview of the history, growth, strengths, and weaknesses 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.
    • Utilize existing financial benchmarks to evaluate the success of Bitcoin and other digital currencies as forms of money and as investments.
    • Understand the opportunities and risks from smart contracts and other emerging technologies.

    Lecturer: D.L. Yermack

  • Leading People Strategically

    After this course, you 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 to analyse people-related business; problems in casess and exercises, as well as in your own organisation in the big data context.

    Lecturer: dr. R.D. Ronay​

  • Finance

    After this course, you should:

    • Understand such concepts of time value of money, arbitrage, CAPM and (N)PV and the ability to apply this knowledge to the evaluation of capital budgeting decisions and company valuation.
    • Be able to reflect on the limitations of the valuation approaches.
    • Analyze, report and present business cases on valuation, capital budgeting, and data science in Finance.
    • Be able to calculate the appropriate WACC for capital budgeting decisions.
    • Understand theories of corporate financing decisions and able to apply this in real business cases.

    Lecturer: dr. J. Ligterink

  • Entrepreneurship Hackathon

    After this course, you should be able to:

    • Understand the core concepts and models of entrepreneurship in both new ventures and large existing companies (intrapreneurship).
    • Analyse and understand key challenges of innovation and launching new digital products and services. Including innovations organised to execute issues within larger organisations.
    • Analyse how companies execute techniques from the start-up and venture world.
    • Deal with the ’transition gap’ - the phase between ’lean start-up’ and ’crossing the chasm’. This is a critical phase which prevents some start-ups from growing to their full potential.
    • 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.

    Lecturer: prof. dr. M. Salomon

  • International Study Trip

    The trip is meant for students to learn how the hosting country stimulates innovation and entrepreneurship. By learning to understand the role of:

    • Universities;
    • Accelerators, incubators, venture studios;
    • Venture capital;
    • Large companies​.

    Another important goal is to see and experience what it takes to start a business in the hosting country.

  • Digital Transformations

    After this course, you should be able to:

    • Understand that digital transformations cover a broad array of technology-domains, organization-wide issues and managerial disciplines.
    • Get a holistic C-level understanding of conducting digital transformations which will serve you as a leader for the years to come.
    • Understand which elements of a digital transformation need change, and, particularly, how to make sure that changes will be effectuated.

    Lecturer: prof. dr. H. Borgman

  • MBA Big Data Thesis Project

    The student is to carry out a research project involving the application of big data in a business environment.

    Apart from a written report, the resulting thesis could be (the start of) a 'product’, e.g. a business concept, a plan on how to bring this concept to the market, and a descriptive, predictive or prescriptive model, algorithm and/or software implementation.

    This project encompasses all key learning elements of the curriculum:

    • Business relevance: Start a big data project that really makes sense in practice and has a clear business case;.
    • Academic rigor: Understand the differences between and limitations of academic theories, frameworks and concepts, and know how to apply them in your research project.
    • Multidisciplinarity: The ability and creativity to combine knowledge of the three main fields: Analytics, Business and Computer Science.
    • Pragmatism: Develop a solution that really works in practice, given the context in which it has to work.
    • Project management: Consider your thesis as a project and practice your project management skills to deliver on time and against specifications.
    • Communication skills: Communicate in a way that is clear, easy to understand and appealing to the different stakeholders (academic supervisors, stakeholders in the company, potential clients, etc.).

    Lecturer: prof. dr. M. Salomon

Computer Science Courses

  • Coding Lab

    After this course, you should be able to:

    •  understand how a computation is executed.
    • understand how a programme is executed.
    •  use instructions to control the programme flow.
    • create a programme to perform a repetitive task.

    Lecturer: J. Rossi MSc

  • Big Data Infrastructures & Technology

    After this course, you should be able to:

    • Understand the technical aspects of Big Data analysis
    • Know when and when not to use technology designed for Big Data processing.
    • Know how to extract, clean and transform large datasets in pre-processing.
    • Have a working understanding of SQL.
    • Know how to perform reshaping, data enrichment and aggregation on Big Data using relational operators.
    • Construct predictive models from large datasets through feature engineering and sampling.
    • Understand how to analyse large connected datasets, such as social graphs.

    Lecturer: dr. H.F. Mühleisen

  • Machine Learning

    After this course, you should be able to:

    - Understand methods from machine learning:

    • Supervised machine learning, including, for example:
      • Linear models for classification and regression
      • Support vector machines
      • Decision trees and random forests
      • Neural networks and deep learning
    • Feature selection
    • Model evaluation
    • Dealing with imbalanced datasets
    • Unsupervised machine learning, including, for example:
      • Clustering and cluster evaluation
      • Non-negative matrix factorization and outlier detection

    - Apply these techniques in realistic use-cases.

    Lecturers: dr. S. Rudinac, prof. dr.M. Worring 

  • Language Technology

    After this course, you should be able to:

    - Understand some of the most prominent language technologies:

    • Text representation for machine learning
    • Neural methods for machine learning
    • Information retrieval
    • Questions-answering
    • Information extraction and knowledge graphs.

    - Understand the possibilities language technology offers
    - Envision application of language technology
    - Apply the techniques in use-cases

    Lecturer: Dr E. Kanoulas

  • Deep Learning

    After this course, you should be able to:

    • Discriminate between different deep learning methods and explain their main characteristics, advantages and limitations.
    • Choose and apply appropriate methods for the given data and use cases.
    • Evaluate model performance using different metrics.

    Lecturer: dr. S. Rudinac