Data Science for Business

Unlock the power of data to transform business processes

Harness data science to drive innovation and success

Solve real-world business challenges by harnessing data science concepts, theories and practices

This module teaches innovative strategies for data interpretation and extracting insights. It covers advanced data science methods and algorithms, encouraging creative problem-solving and model optimisation crucial for digital transformation. Students will learn to analyse data comprehensively using statistical and machine learning techniques, gaining skills to synthesise insights for informed decision-making and clear communication.

The curriculum also focuses on designing and evaluating advanced visualisations and business intelligence tools, equipping students with the ability to convey complex data insights effectively. These skills are essential for enhancing model performance and driving business innovation and success.

Innovative online learning with hybrid methods and expert guidance

This module is delivered entirely online, using innovative hybrid learning methods that combine live (synchronous) and self-paced (asynchronous) activities. Expert tutors guide students through the material, ensuring a comprehensive learning experience. Activities include live lectures, individual study, and hands-on lab sessions.

Key teaching strategies include problem-based learning, gamification and flipped classroom techniques. By leveraging emerging technologies like artificial intelligence, the module aims to enhance the learning experience and keep pace with cutting-edge educational research and methods. The module uses ongoing and final assessments to measure progress, including exams, assignments, and projects. The project (50%) applies Data Science to business problems, and a final test (50%) checks overall learning.

Time commitment

  • Classroom and demonstrations: 36 hours
  • Practical work/tutorials: 36 hours
  • Independent learning: 178 hours
  • Total: 250 hours

Credit points

  • 10 ECTS

 

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Full course breakdown

Subjects covered:

The Digital Transformation module is a 10 ECTS course, conducted over 12 weeks with 3 hours of lectures per week. Here’s an overview of the topics to be covered:

Data Science for Business is a 10 ECTS module delivered over 5 hours per week for 12 weeks. An indicative schedule of topics to be addressed each week is outlined below:

  • Introduction to Data Science
      • Overview of data science processes
      • Methods, tools and real-world applications
  • Python for Data Science
      • Python programming basics
      • Data structures
      • Packages for data analysis
  • Data Collection and APIs
      • APIs
      • Web scraping
      • Working with unstructured data sources
  • Databases and Data Warehousing
      • Relational databases
      • SQL
      • ETL processes
      • Data warehousing principles
  • Data Pre-processing and Cleaning
      • Handling missing data
      • Outliers
      • Feature encoding
      • Normalisation
  • Exploratory Data Analysis
      • Summary statistics
      • Visualisations
      • Identifying patterns
  • Statistical Analysis and Modelling
      • Regression
      • Classification
      • Forecasting methods
  • Machine Learning
      • Supervised learning models like classification and regression
  • Advanced Machine Learning Methods
      • Neural networks
      • Deep learning
      • Reinforcement learning
  • Business Intelligence and Analytics
      • BI process
      • Dashboards
      • Data storytelling
      • Predictive analytics
  • Data Visualisation and Dashboards
      • Visual encodings
      • Interactive reports
      • Communicating insights
  • Ethics, Bias and Privacy in Data Science and Major Trends in ML and DS
    • Responsible AI
    • Transparency
    • Ethical use of data
    • Major trends in ML and DS
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Module leader

Roberto André Pereira Henriques

Roberto André Pereira Henriques is an Associate Professor at NOVA Information Management School (NOVA IMS), where he has been contributing to the academic community since September 2018. He earned his bachelor’s degree in biophysics engineering from Universidade de Évora in 2002, followed by a master’s degree in Geographic Information Science and Systems from NOVA IMS in 2006, graduating with unanimous distinction and high praise. In 2010, he completed his PhD in Information Management at the same institution.

Roberto has been involved in numerous research and development projects, including the CIBERSEGURANÇA project (2019-2022) and initiatives focused on higher education and sustainability. His industry collaborations and research have led to significant advancements, particularly in the fields of artificial intelligence and machine learning. Henriques is a prolific author, with recent publications exploring topics such as cancer detection, student attentiveness, and diabetes management using big data and machine learning.

Part of the Digital4Business ecosystem

This Data Science for Business module is part of the Digital4Business programme, an innovative new online master’s funded by the EU. Designed to develop the digital leaders and strategists of tomorrow, it explores how digital transformation drives business innovation and efficiency, providing the expertise needed to excel in the digital era.

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FAQ

Minimum C1 English proficiency, plus 2 years' work or education in an English-speaking environment. IELTS: 6.0; TOEFL PBT: 600; TOEFL CBT: 200; TOEFL iBT: 100

Applicants need a suitable cognate EQF Level 6 qualification (e.g. STEM, economics, etc.). Description of the eight EQF levels Those without such a qualification will undergo an interview and assessment to determine the suitability of their certifications, other qualifications, and/or professional experience.

This EU-funded programme is open to all EU nationals with a passport or valid ID from one of the 27 EU countries