BSc Business Analytics

In today’s business landscape, the increasing complexity and proliferation of business cases-problems that managers and companies face have been more prominent than ever, and Data-Driven Decision Making, therefore, has been a central focus and priority for most organisations. Today, both managers and data scientists, need to work in a dynamic and complex environment where data is collected from multiple sources and platforms and eventually to be processed-analysed in an integrative and solution-oriented manner. Companies and managers need to have an in-depth understanding of the data-rich environments and they need to have a clear roadmap, by knowing the relevant methods, theories, and approaches that will guide them while harnessing and using data efficiently in finding solutions to business problems.  

The Business Analytics Bachelor Programme at the University of Amsterdam is designed as an integrative and multi-disciplinary program with the objective of training students as competent data analysts & data scientists who master the analytical-mathematical-programming skills-knowledge and who can bridge this with knowledge (theory ad practice) in the domain-related knowledge on economics, business administration and management sciences to design-develop, implement and interpret-communicate; relevant solutions to the business problems with an analytical, holistic and data driven perspective.  

The Business Analytics bachelor programme is a multidisciplinary program integrating three main pillars in today’s business analytics with an A-B-C approach (A= Analytics, B=Business, C=Computer Science). This program consists of courses from three main domains: A=Analytics (mathematics, statistics, and econometrics), B=Business (management, finance, human resources, marketing, and business law & ethics), and C=Computer Science (programming, data structures, and AI & machine-learning) and bridges these three domains with each other in a well-balanced and integrated manner. The students in this program are trained for using computers-programming combining this with mathematical-statistical knowledge and business-management theories to take better and data-driven decisions in today’s business landscape.  

Courses taught by our department:

Analytics for a Better World
Introduction to Programming
Algorithms and Data Structures in Python
Machine Learning
Operations Research - Deterministic Methods
Management Consulting - Operational Excellence
Operations Research - Stochastic Methods
 

Business Operations and Processes - BSc Business Administration

This course gives an introduction to the theories behind the design, control and improvement of the processes that create goods and services. Creating products and services is clearly the key for the existence of any organisation. An organisation's mastery of its business processes may entail substantial competitive advantage. The demands in terms of efficiency, quality, speed, flexibility and dependability are challenging. The strategic importance of effective and efficient operations and process management has resulted in a thriving scientific discipline and a flood of commercial offerings by consulting firms in business and industry.

The course offers a wide variety of fields, including manufacuring services, governmental and healthcare organisations. Operations are often complex and dynamic systems, decisions and problems are typically fuzzy and pluralistic. Part of the course is about learning to structure such decisions and problems to allow the derivation of a rational solution.

Management Research Methods 1 - pre-Master's for EPMS, Business Administration and schakelprogramma Accountancy and Control in deeltijd

Good strategic decision making requires first and foremost high quality information. For managers and other decision makers it is therefore crucial to understand the quantitative and statistical methods (and drawbacks) that are so often used to generate the information they are provided with. Moreover, in their theses the students often need to perform statistical analysis. This course reviews and explains the basic statistical concepts and techniques which are used in the area of Management and Business Administration and MSc theses and emphasises the practical application of the various techniques using SPSS software.

MSc Data Science and Business Analytics

There has been explosive growth in the amount of data available to organisations. This gives them the opportunity to quickly learn from these huge amounts of mostly interconnected data, but they need data specialists to do this. And these specialists may well be the most sought-after professionals on the job market today.

The MSc Data Science and Business Analytics combines techniques from statistics, econometrics, operational research, artificial intelligence, informatics, law, ethics and communication sciences. Students obtain a thorough knowledge of predictive and causal modelling and be able to solve business-related problems.

The curriculum consists of a balanced mix of Analytics (A), Business (B) and Computer Science (C) subjects, with major attention being paid to integrating the A, B and C components, where

• ‘A’ stands for Analytics and includes data science techniques, statistics and econometrics;
• ‘B’ stands for Business and covers areas such as finance, human resource management, marketing,
  and process mining;
• ‘C’ stands for Computer Science, which includes subject areas such as databases & infrastructures and
  machine learning (including deep learning).

Courses taught by our department:

Advanced Analytics for a Better World
Machine Learning and Optimisation
Privacy, AI, Law & Ethics
Applied AI Research Seminar

Quantitative Marketing - MSc Econometrics and MBA Big Data & Business Analytics

Description: Large amounts of consumer data are available in today’s (online) world. This creates many opportunities for smarter and data driven marketing. Some of the topics the students become familiar with during the course are:

• Measuring marketing effectiveness (Conversion Attribution & Media Mix modeling)
• Online advertising with Google AdWords & Realtime bidding
• Recommendation engines
• Dynamic pricing
• Online reinforcement learning 

Data Wrangling - Minor Data Science and Artificial Intelligence

Big Data refers to data that are more voluminous, but often also more unstructured than traditional data. This in particular concerns data-collection that draws on internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy data. The first step in this process is called data wrangling, which is the main subject of this course. Once the data is parsed and cleaned, it is usually analysed in an exploratory way before more advanced statistical or machine learning techniques are applied. The focus lies on:

• acquiring and storing data
• data preparation: cleaning, selecting, transforming, merging and reshaping the data
• computer aided exploratory analysis
• comparison of data mining techniques
   

Text Retrieval and Mining - Minor Data Science and Artificial Intelligence

Description: The underlying question behind this course is: how does a machine collect, represent and process textual data to algorithmically extract valuable information, identify consistent patterns and learn systematic relationships between pieces of text? The technological topics which will be covered in this course are:

• textual data collection and indexing
• text representation
• text pre-processing
• machine learning for text classification and ranking
• evaluation
   

Databases and Data Visualisation - Minor Data Science and Artificial Intelligence

Data and databases play a central role in any information system from transaction processing to enterprise systems and, of course, data science applications. The purpose of this course is to offer a solid understanding of the core concepts in this area as well as an opportunity to apply these concepts hands-on and in a ‘living case’ business setting. These core concepts are based on the relational data model and SQL - as the de facto standard database language - combined with data visualisation and the design of metrics and dashboards. The course includes a significant practical part with a focus on data modeling, on SQL and on data visualisation to solve business issues.
 

Machine learning for Data Science - Minor Data Science and Artificial Intelligence

This course provides an introduction to machine learning for the students with little to no knowledge of the subject. The topics that will be covered in the course include:

• K-nearest neighbors algorithm
• Model selection
• Linear models for regression and classification
• Decision trees and random forests
• Preprocessing and feature engineering
• Imputation and feature selection
• Support vector machines for classification and regression
• Evaluation metrics
• Working with imbalanced datasets
• Clustering and cluster evaluation
• Non-negative matrix factorization and outlier detection
   

Algorithms and Data Structures in Python - Minor Data Science and Artificial Intelligence

This course provides an introduction to mathematical modelling of computational problems. It covers the common algorithms, algorithmic paradigms and data structures used to solve these problems. It also uses the Python programming language to implement and test algorithms and data structures on realistic datasets. The technological topics which will be covered in this course are:

• Python Programming Basics
• Introduction to Object-Oriented Programming in Python
• Algorithm Analysis
• Basic Data Structures
• Recursion
• Sorting and Searching
• Trees and Tree Algorithms
• Graphs and Graph Algorithms

Operational Excellence - Executive Programme in Management Studies, Amsterdam MBA and Executive Master in Finance and Control

Managing a business involves identifying, analysing and developing improvement opportunities in processes and in the organisation. Managers need professional skills in problem analysis, problem solving and decision taking. As a generic structure for the process of problem-analysis, we take Lean Six Sigma's DMAIC (Define/Measure/Analyse/Improve/Control) model which has become the world standard in business and industry. The DMAIC approach helps in structuring messy problems and translates them info a quantitative decision problem. For a selection of common business problems we learn standard quantitative analysis approaches:

• measurement system validation: techniques for assessing the reliability of measurement procedures
• process diagnostics: statistical techniques for quantifying the quality performance of a process
• process analysis: analysing problems related to throughput times and capacities in business processes
• optimisation of processes: statistical techniques for experimentation and Lean best practices
• process control: statistical techniques for monitoring and controlling business processes
   

Process Improvement in Healthcare - MBA in Healthcare Management

Managing a business involves identifying, analysing and developing improvement opportunities in processes and the organisation. Managers need professional skills in problem analysis, problem solving and decision-making. A generic structure for improvement projects in Lean Six Sigma.

Lean Six Sigma is built on principles and methods that have proven themselves over the 20th century. It has incorporated the most effective approaches and integrated them into a full programme. It offers a management structure for organising continuous improvement of routine tasks, such as manufacturing, service delivery, healthcare, sales, nursing and other work that is done according to a routine. Furthermore, if offers a method and tools for carrying out improvement projects effectively.
 

Quantitative Methods - MBA in Healthcare Management

Good decision-making requires, first and foremost, high quality information. For managers and other decision makers it is therefore crucial to understand the quantitative and statistical methods (and drawbacks) that are so often used to generate the information with which they are provided.

This course reviews and explains the statistical concepts and techniques that are most commonly used in the area of management, business administration and healthcare. The course emphasises the practical application of various statistical techniques using Minitab. In the lectures, the various statistical techniques will each be carefully explained and illustrated with examples of how and why they may be used in management research.
 

Language technology - MBA Big Data & Business Analytics

The proliferation of data in all its forms has opened up the way to a new class of intelligent methods which are able to learn from this data. A large amount of this data is in human language form, either written or spoken.
This course will focus on technologies that allow machines to read and comprehend human language and generate human language themselves, so that human language can be transformed to information and communicated back to humans. A variety of applications will be presented aligned with tasks such as text clustering, classification, retrieval, question-answering, summarisation, together with the underlying language technology which includes language representation, knowledge extraction and information retrieval.
 

Digital Business Innovation - Amsterdam MBA and Executive Programme in Management Studies

Building on the theories, concepts, models and research introduced in Theories of Digital Business, we now look in more detail at how to model, innovate and transform customer interactions, business processes as well as business models with digital technologies and big data. Through lectures and case studies we explore theories and their application using a variety of tools for modelling and visualisation, including customer journey mapping, process modelling, business model mapping, big data visualisation and analytics dashboards.
 

Theories of Digital Business - Amsterdam MBA and Executive Programme in Management Studies

Rapid changes in digital technologies and their application are causing major changes for individuals, organisations and industries. Most recently, the internet and the availability of big data are radically impacting our personal and professional lives and challenge our thinking on how we live, work, learn, communicate, compete, collaborate, and socialise. As part of this digital transformation, new business models are emerging, as are new types of entrepreneurship and new forms of leadership.

The new elements include management practices and views on value creation, globalisation and entrepreneurship. This transforms both established industries such as manufacturing, transport and hospitality (think of globalisation, outsourcing, open innovation and disruptors such as Uber and Airbnb) as well as newer and converged industries such as telecommunications and media (think of platforms and hubs such as those offered by Alibaba, Amazon, Apple and Google).

This course aims at providing a deeper understanding of the issues, challenges and opportunities in this area, with a specific focus on creating business value with IT and big data. Understanding the developments and the underlying principles is crucial for all aspects of business administration, from marketing to logistics and from strategy to HR. The course emphasises a global organisational and managerial approach to digital business, covering strategic issues as well as implementation and change.
 

Machine Learning - MBA Big Data & Business Analytics

The proliferation of data in all its forms, be it symbolic, numeric, textual or visual, has opened up the way to a new class of intelligent methods which are able to learn from this data. Applications of machine learning are broad and diverse and range from prediction of health parameters, understanding the content of social media, to the recommendation of products. In this course we teach the theoretical foundations of machine learning and how to apply these methods in practical analytic tasks.
 

Data Visualisation - MBA Big Data & Business Analytics

The course will be comprised of the following:

• Design spaces
• Advanced visualisations for numeric, categorical, temporal, and geographical data
• Advanced visualisations for tree and network structures
• The role of perception and cognition in visualisation
• Visual analytics models
• Multimedia analytics