Bachelor in Data and Business Analytics Course Descriptions

2018 — 2019

BACHELOR IN

Data and Business Analytics Course Descriptions

The Bachelor of Data and Business Analytics at IE University offers a unique opportunity for students to study the basics of data science (mathematics, statistics, and programming) as applied to business real-life situations. All courses are taught by a combination of theoretical and practical lectures, assignments, individual and group projects. Laboratory work consists of learning and applying statistical software & programming languages. From the very first course, the students will be given real-life problems (real-life big data) to analyze and solve. Depending on the nature of the course, field visits might be undertaken to specific industries/ organization to expose the students to current and emerging technological advances that might be related to generating new types of data.

It is worth noting that in all basic mathematical and statistical courses, the students will be equipped with online tools (like MyMathlab and Mystatlab) that help them design study plans, deepen their knowledge in specific topics, and monitor their progress throughout the course.

Important note: this information is subject to change.

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EVALUATION / ASSESSEMENT

we will adapt our curriculum to include emerging software and programming languages in the market.

In each course, students will be assessed through a combination of written examinations, laboratory and course work.

Most courses include a final project or a number of assignments that can be done either individually or in group. Throughout the semester, students will receive constant feedback on their progress.

YEAR ONE Data Insights & Visualization IE MODULE — 6 ECTS, 1, 1º DESCRIPTION This course provides a solid

In what follows is a detailed description of the basic, obligatory and elective courses planned for this program. These courses serve as a template and might be slightly adapted to emerging changes in the field.

BASIC COURSES These include a combination of mathematical, statistical, and computer science courses. In addition, students will be exposed to a number of introductory courses to management, behavioral sciences and business. Presentation and writing skills are also taught as part of developing the soft skills a data scientist needs to have.

It is our objectives to teach students as many programming languages and statistical software as possible. This include, but are not restricted to: Excel, SPSS, SAS, Hadoop, SQL as well as R, Python and Matlab. As this degree is highly linked to technological advances,

foundation and jumpstart students ability to work effectively in the exciting and fastgrowing new world of data analysis. Students will be taught how to import, search, and query data (data insights) to answer questions like: How did Google searches predict the spread of the H1N1 flu outbreak?

How can a retailer predict when one of its customers is pregnant before her family ever knows it? How can you predict someone’s political affiliation analyzing the songs he plays at home?

Learning to Observe, Experiment & Survey 6 ECTS: JESSICA DESCRIPTION Principles of statistical

investigation. Observational studies.

Controlled experiments. Surveys.

Randomization.

Fundamentals of Probability & Statistics 6 ECTS, 1, 1º DESCRIPTION In almost all data-

driven solutions, data scientists exercise

Bachelor of Data and Business Analytics Course Descriptions

statistical thinking in designing data collection strategies, deriving insights from data visualization methods, obtaining supporting evidence for decision-making, and constructing models for predicting future trends. This course starts with an introduction to data science and the central role that statistics plays in the data science approach. It uses mathematical tools to collect, organize, process, and summarize data; make estimates using probability rules; and draw inferences that will affect decision-making in uncertain environments.

Numerous real-world examples from the social sciences, business, health-care as well as sports will be discussed.

Presentation Skills 3 ECTS, 1, 2º DESCRIPTION In this course, students will

learn the basics of communication and presentation skills. Specific topics include: types of speech, presentation structure, elements of arguing, inventing, arranging and phrasing main points, supporting claims with evidence, dealing with fear of public speaking, delivery of presentations, etc.

Since presentation skills are learned only by practicing, students will learn by watching themselves and their colleagues present to the class, on tape. Participants will be required to prepare presentations on subjects of their choice, and on points submitted by the teacher.

Simulating and Modeling to Understand Change

Writing Skills

6 ECTS, 1, 2º

3 ECTS, 1, 2º

DESCRIPTION This course describes the

DESCRIPTION This course will teach

concepts and methods of simulation and model building for data science. Students will learn how to translate expert knowledge into a causal diagram and develop the appropriate model to test and draw inferences.

students about the basics of effective writing. It will expose students to the logic and the style of effective and convincing written work. It is a highly interactive course which will force students to design argument statements, introductory paragraphs, and paper outlines in class.

It will also provide practical information about the research process, rules of citation, and styles of presentation. Specific topics include: Balancing criteria in choosing a topic: past experience, professional goals, manageability, and passion; Descriptive versus analytic topics; The writing process, Combining a clear roadmap with a flexible

The Big History of Ideas & Innovation IE MODULE — 6 ECTS, 1, 2º DESCRIPTION This course aims at providing

students with a better understanding of the history and development of ideas. It reflects the cultural and social diversity of contemporary world.

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attitude; Creating an outline; Choosing Sources; The basics of argumentation, etc.

matching, file compression, cryptology, dynamics and linear programming and exhaustive search. Substantial use of programming mainly Python and SQL.

YEAR 2 Professional bootcamp: Teamwork Probability and Statistics for Data Scientist

3 ECTS, 2, 4º

6 ECTS, 2, 3

two courses series in Professional Bootcamp.

Emphasis is in the information, tools and techniques students need to effectively work in teams in any organization or project and will be introduced to three key aspects of professional development: product development, team work and project management. The course is designed to increase the student’s interpersonal and disciplinary skills by teaching them the basic concepts of team work namely initiation and adaptation. These soft skills are deemed crucial to data scientist professional as they have to deal with professionals from various disciplines and areas.

DESCRIPTION This course is the first of a

DESCRIPTION This course is the second

in the series probability and statistics.

It focuses on types of data analytics and discusses various kind of text data: clustering text, topic modeling and metrics for label description. Also it introduces the concept of Bayesian Modeling, probability calibration and Bayesian Inference.

Mathematics for Data Scientist 6 ECTS DESCRIPTION This course is essential for

Data Science students as it focuses on the basics of linear algebra, namely systems of linear equations and matrix algebra. These concepts are crucial for other courses such as algorithms, programing and machine learning.

Algorithms & Data Structures 6 ECTS, 2, 2º DESCRIPTION Fundamentals of Discrete

Mathematics: Sets, Series, Sums, etc. Data structures, linked lists, stacks, queues, hash tables, trees. Algorithms basics. Analysis, sorting and searching of algorithms.

Additional topics will be chosen from pattern

Bachelor of Data and Business Analytics Course Descriptions

OBLIGATORY COURSES

learn the relevance of social science for their professional development.

YEAR 1

Technology for Innovation IE MODULE — 3 ECTS, 1, 1º

Fundamentals of Human Behavior

DESCRIPTION Current and emerging

6 ECTS: JESSICA TOLLETTE

technologies. Application to innovation and problem-solving in a profession or career path. Introduction to IoT, Blockchain, Mobile, Wearables, Artificial Intelligence, VR and Big Data. Real-world examples of how these technologies are being used. Hands-on innovation methods.

DESCRIPTION This course provides a basic

knowledge in the fundamentals of the study of behavior. Students will learn about human behavior in four main domains: biological, psychological, cultural and social. They will also learn about the main theories in human behavior including cognitive theory, social learning theory, role theory and attachment theory. Finally, they will learn who and how the study of human behavior is relevant for a career in business.

Fundamentals of Social Sciences 6 ECTS DESCRIPTION This course is about the

fundamentals of social science and its relevance for the study of behavior looking specifically at the following disciplines: sociology, anthropology, economics, political science, business and law. It will introduce students to the primary methods of the social sciences including the scientific method, experiments, surveys, interviews and observation. Additionally, students will learn the most important theories, trends and most innovative studies from the relevant disciplines, for example: political populism, the impact of the use of technology on personal relationships, etc. Students will also

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Fundamentals of Data Analysis 6 ECTS, 1, 2 PRE-REQUISITE Fundamentals of

Probability & Statistics.

DESCRIPTION This course follows on Fundamentals of Probability and Statistics and focuses on inferential statistics namely estimation, significance tests, correlation and regression.

YEAR 2 Programming for Data Scientist 6 ECTS; 2, 3º DESCRIPTION Learn how to apply

fundamental programing concepts, computational thinking and data analyses techniques to solve real-world data science problems. This course will teach you the required general overview of the Python programming language coupled with specific

use cases for data analysis. Understand and apply introductory programming concepts such as sequencing, iteration, selection, creative codes, building blocks, repetition, etc.

Data Structures and Storage 6 ECTS; 2, 3º DESCRIPTION This course builds on

Algorithms and Data Structures course and seeks to provide students with a basic understanding of enterprise data structures, storage and management technologies.

Topics include: Types of data structures: List-structure (generalized lists and multidimensional arrays) and balanced binary trees (AVL, Red- Black trees, and Splay trees), storage systems, storage networking

technologies (Storage area network; SAN and network attached storage; NAS) as well as concepts related to business continuity, storage security, and storage management.

This course will also cover key concepts related to cloud computing, and some of the new trends in the storage industry. The basic concepts covered in this course will enable learners to later explore each concept in greater detail.

AI – Machine Learning Foundations 6 ECTS, 2, 4º DESCRIPTION Machine Learning is a

growing field that is used when searching the web, placing ads, credit scoring, stock trading, and many other applications. This course

Bachelor of Data and Business Analytics Course Descriptions

is an introduction to machine learning and algorithms that will help students develop a basic understanding of basic principles and derive practical solutions using predictive analytics.

Forecasting and Time-Series Analysis

Nonstationarity and unit roots. Forecast uncertainty and model evaluation: Sources of uncertainty; Statistics for forecast assessment; Theil’s U Statistics. Forecasting strategies.

Structural breaks. Fan charts. Vector AutoRegressions (VARs). Cointegration and Vector Error Correction Models (VECMs).

Evaluating regression models.

6 ECTS, 2, 4º DESCRIPTION Introduction to forecasting

using a programming language (R, STATA, or Eviews: if Eviews is selected, basics of Eviews will be taught). Basics of Time-Series data: Identification of stationary processes, estimation with stationary time-series and model selection, working with data.

Statistical properties of times-series data:

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Introduction to Business and Social Analytics 6 ECTS, 2, 4º PRE-REQUISITE Fundamentals of

Probability and Statistics and Linear Algebra.

Working knowledge with a programming language such as Python or R. DESCRIPTION This course will teach

the fundamental techniques towards a principled approach for data-driven decision making. Quantitative modeling of the dynamic nature of decision problems using historical data. Various approaches for decision making in the face of uncertainty.

These topics will be presented in the context of practical business and social applications.

Operating Systems and Parallel Computing 6 ECTS, 2, 4ยบ DESCRIPTION The course discusses the

design and implementation of operating systems: Multi-tasking, concurrency and synchronization. Inter processes communication, scheduling, memory and storage management, input-output, file systems, protection and security. In addition the development of programs for parallel computing will be discuss. Topics include: Basic concepts such as speed, load balancing, latency, system taxonomies. Programming on parallel systems such as shared or distributed memory machines, networks, grid computing, etc. Substantial use of Hadoop: MapReduce and PageRank.

Global Issues and Debate 3 ECTS, 2, 4ยบ DESCRIPTION This course will cover all

current issues and debates in data science and technology. Students will be trained how to debate and argue efficiently new and emerging topics.

YEAR 3 Analyzing Social Media 3 ECTS, 3, 6ยบ DESCRIPTION Understand the

characteristics of the web and social networks: how they are connected, how they form and how processes and transactions occur on them. Using mathematical tools from graph theory, linear algebra, probability and game theory. Model social networks and apply algorithms for community detection in networks. Clustering social networks, apply both hierarchical and k-means clustering.

Implement the basic PageRank algorithm for strongly connected graphs.

Recommendation Engines 6 ECTS, 3, 6ยบ DESCRIPTION This course introduces

students to the concept of recommender systems, reviews several examples in detail, and leads students through nonpersonalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. In addition, students will learn how to evaluate recommender systems using several families of metrics, including ones to measure prediction accuracy, rank accuracy, decisionsupport, and other factors such as diversity, product coverage, and serendipity. Specific topics include how different metrics relate to different user goals and business goals,

Bachelor of Data and Business Analytics Course Descriptions

how to conduct offline (for example, how to prepare and sample data, and how to aggregate results) and online (experimental) evaluation.

AI – Machine Learning & Analytics 6 ECTS, 3, 6º PRE-REQUISITE AI – Machine Learning

Foundation, Calculus, Linear Algebra, Probability and Statistical Concepts, and Programming Skills.

DESCRIPTION This course builds on the first Al – Machine learning Foundation.

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection. Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others. In the first half of the course we will cover supervised learning techniques for regression and classification. In the second half of the course we shift to unsupervised learning techniques, namely data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

to use the Hadoop technologies in Microsoft Azure HDInsight to build batch processing solutions that cleanse and reshape data for analysis. Students will learn technologies like Hive, Pig, Oozie, and Sqoop with Hadoop in HDInsight; and how to work with HDInsight clusters from Windows, Linux, and Mac OSX client computers. In addition, emphasis is placed on teaching how to implement lowlatency and streaming Big Data solutions using additional Hadoop technologies like HBase, Storm, and Spark on Microsoft Azure HDInsight. Additional topics include stream processing concepts and frameworks.

Big Data Technology 6 ECTS, 3, 6º DESCRIPTION In this course, students will

gain essential skills in today’s digital age to store, process and analyze data to inform business decisions. They will develop their knowledge of big data analytics and enhance their programming and mathematical skills. Substantial use of Apache Spark and R programming languages. Specific topics include: cloud-based big data analysis; predictive analytics, including probabilistic and statistical models; application of largescale data analysis; analysis of problem space and data needs.

Project Management 3 ECTS, 3, 6º

Stream Analytics

DESCRIPTION This course will provide

6 ECTS, 3, 6º

students with a general introduction to project management knowledge, tools and

DESCRIPTION This course focuses on how

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techniques. It explores what it means to work by project and how to manage the main areas proposed by the PMI (Project Management Institute) in the Project Management Body of Knowledge (PMBOK), such as scope, time, cost, human resources, stakeholders, risks and communication.

Emphasis is placed on Agile Methodology as applied to Project Management and on the specific skills that a project manager needs to acquire or develop to run a successful project, such as structured thinking, analysis, problem solving, communication, leadership or conflict resolution. Special attention is given to the critical success factors required to overcome resistance to change. In this course, students will explore project management with a practical, hands-on approach through case studies and class exercises.

NLP, Text Mining, and Semantic Analysis 6 ECTS, 3, 6º DESCRIPTION This course teaches the

linguistic fundamentals of natural language processing, part of speech tagging, hidden Markov models, syntax and parsing, lexical semantics, compositional semantics, word sense disambiguation, machine translation, text generation, and deep learning for NLP. Programming language is mostly Python.

During the course, students will work on a project that they propose on groups composed of 4-5 members. There will be two options for projects: either identifying a real existing application that uses NLP

and analyzing the techniques that it uses, or designing and developing your own NLP based applications.

Designing Artificial Intelligence and Implementing Small Technologies 6 ECTS, 3, 6º DESCRIPTION In this course, students will

be introduced to the concept, background and terminology of of Intelligent Machining: machine tools that are self-aware – they perceive their own states and the state of the surrounding environment – and are able to make decisions related to machine activity processes. They will learn how the integration of smart sensors and controls are helping to improve productivity and will be exposed to various sensors and sensing techniques, process control strategies, and open architecture systems that can be leveraged to enable intelligent machining.

At the end of the course, students will be prepared to contribute to the implementation of intelligent machining projects.

Advanced Databases 6 ECTS, 3, 6º DESCRIPTION Basic principles of database

management systems (DBMS) and DBMS application development. Database design, architecture, models specifically relational models, application development tools, structured queries and languages (SQL) and manipulation languages. In addition, student will learn some NoSQL technologies. The

Bachelor of Data and Business Analytics Course Descriptions

student will learn the difference between Relational Model (SQL based) versus other non-relational databases as document-based or Key/Value-based databases. To better understand this technologies the students will practice with some of these databases.

Data Visualization, Dashboards and Storytelling 6 ECTS, 3, 6º DESCRIPTION This course is an advanced

collect it, clean it, visualize it, analyze it and interpret it using a particular programming language, appropriate to their specific type of data. The objective of this course is to validate some of the skills acquired in big data and data science, to challenge a realworld case by combining data analysis, machine learning and some coding abilities.

Professional Bootcamp – Self Management

course on data visualization, dashboard creation and storytelling. In this course students will learn the principles, tools, techniques and best practices required to extract value from data through effective visualizations. In addition, students will be introduced to the concept of ̈data product ̈ which is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. The following software will be used in this course: Tableau Public, R, Carto, Python, and Gephi.

3 ECTS, 3, 6º

Datathon for Social Impact

Emerging Topics in Data Science and Management

3 ECTS, 3, 6º

6 ECTS, 4, 8º

DESCRIPTION This course is an advanced

DESCRIPTION This course will address the

course on data management, analysis and interpretation. It is a project-based course during which the students will be exposed to various types of data, by providing them with different sensors. Each group of student will learn about specific kind of data, how to

latest topics in technology and their impact on data science and management. Specific topics include latest programming software, emerging statistical theories, new sensors and data types, emerging tendencies in data analysis and management, etc.

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DESCRIPTION This course is the second

in the Series Professional Bootcamp.

It is designed to increase the student’s interpersonal and disciplinary skills by teaching them the basic concepts of selfmanagement within a team. These soft skills are deemed crucial to data scientist professional as they have to deal with professionals from various disciplines and areas.

YEAR 4

Advanced Topic: Connected Industries, Smart Cities & e-Governments

evolution of these technologies on existing processes.

3 ECTS, 4, 8ยบ

Advanced Topics: Sales & Marketing Analytics

DESCRIPTION This course provides an

overview of smart cities, connected cities and e- governments along with emerging perspectives and trends. Specifically, students will study smart cities from the perspective of innovation and processes impacted due to deployment of technology such as such as smart parking, transport etc. Other topics include: IoT: underlying technologies, data, implications of 5G, IoT analytics, sensors deployed in cities etc.; Big Data: the impact of Big Data technologies and their role for IoT and AI; governance and policy; impact and

3 ECTS, 4, 8ยบ DESCRIPTION In a world where

organizations are inundated with data about consumer choices, the ability to translate this information into a good decision is critical.

This course enables students to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Specific topics include how to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and

Bachelor of Data and Business Analytics Course Descriptions

optimize marketing campaigns. Students will gain a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources for better business decisions.

skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners. The project consists of the following parts:

Advanced Topic – Health & Genetics Analytics

1. Problem identification and data collection, cleaning and preparation.

2. Exploratory data analysis and modelling.

3. Prediction model.

4. Data Product.

5. Data storytelling.

6. Final project submission and evaluation.

3 ECTS, 4, 8º DESCRIPTION In this course, the focus

is on understanding the data science behind genomic technologies, their current and future applications in research, pharmaceutical and clinical environments.

The main objective is to introduce the students to the above topics, and explore their current and future applications in the healthcare industry. Specific topics include: How to acquire, transform, classify, mine and visualize health data and how to identify data analytics based on entrepreneurial opportunities in healthcare and quantify its economic value.

Career Preparation & Design 3 ECTS, 4, 8º DESCRIPTION This course is project-based

in which students will be assigned various hypothetical jobs and asked to design and prepare them.

Capstone Project 12 ECTS, 4, 8º DESCRIPTION The capstone project will

allow students to create a usable and public data product that can be used to show their

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ELECTIVES: General Electives Customers and Markets DESCRIPTION In order to compete in an

ever-changing market, business leaders must assess and respond to consumer needs and wants. Marketing is a means of creating value for consumers and engendering loyalty and enthusiasm for your product or service.

Students enrolled in this elective will learn the following topics: the marketing mix; segmentation; targeting and positioning; marketing strategy and consumer behavior.

In addition, students will learn how to apply advanced concepts such as conjoint analysis and decision tree methodologies to product decisions, as well as learn the best ways to distribute and sell your offerings to consumers.

Healthcare Delivery – Analytics and Financial Services DESCRIPTION This course is an introduction

to the characteristics of healthcare and medical da ta along with associated data mining challenges in dealing with such data.

It focuses on studying big data techniques in the context of concrete healthcare analytic applications such as predictive modeling, computational phenotyping and patient similarity.

environment: water and waste; Energy, air, food and mobility; as well as green spaces and food. Specific case studies will be discussed such as the use of sensors in smarthouses and their impact on the environment.

Citizens, Society & Policy DESCRIPTION In today’s world, citizens

Hospitality, Travel and Tourism DESCRIPTION Students enrolled in this

course will learn the various aspects of tourism planning, management of services and experiences. Emphasis is placed on the changing role of technology in hospitality, travel and tourism particularly in the areas of decision-making, management of workforce and risk. Specific topics include the role data science plays in the tourism industry and how big data is used to creating resolutions and strategies that address tourism issues and challenges.

play an important role amidst governments, companies, NGOs and researchers in creating social, technological and political innovations for achieving sustainable societies. Citizen influence and are influenced by the technologies and systems that they use every day. In this project-based elective course, students will gain a deep understanding of the role citizens play as cocreators of sustainable societies when they engage in city politics or in the design of the urban environment and its technologies and infrastructure.

Environment and Sustainability DESCRIPTION Around the world, major

challenges of our time such as population growth and climate change are being addressed. This course introduces the origin and key concepts of sustainability and how to apply those to sustainable development practice. Emphasis is placed on the effect of emerging technologies on three basic concepts that have vast impact on our

Bachelor of Data and Business Analytics Course Descriptions

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The information in this brochure is subject to revisions or changes. You will find the most up-to-date information on the Universityʼs website.

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