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
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
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- Overview of data science processes
- Methods, tools and real-world applications
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- Python for Data Science
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- Python programming basics
- Data structures
- Packages for data analysis
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- Data Collection and APIs
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- APIs
- Web scraping
- Working with unstructured data sources
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- Databases and Data Warehousing
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- Relational databases
- SQL
- ETL processes
- Data warehousing principles
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- Data Pre-processing and Cleaning
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- Handling missing data
- Outliers
- Feature encoding
- Normalisation
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- Exploratory Data Analysis
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- Summary statistics
- Visualisations
- Identifying patterns
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- Statistical Analysis and Modelling
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- Regression
- Classification
- Forecasting methods
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- Machine Learning
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- Supervised learning models like classification and regression
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- Advanced Machine Learning Methods
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- Neural networks
- Deep learning
- Reinforcement learning
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- Business Intelligence and Analytics
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- BI process
- Dashboards
- Data storytelling
- Predictive analytics
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- Data Visualisation and Dashboards
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- Visual encodings
- Interactive reports
- Communicating insights
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- Ethics, Bias and Privacy in Data Science and Major Trends in ML and DS
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- Responsible AI
- Transparency
- Ethical use of data
- Major trends in ML and DS
Learning objectives
This module is integral to digital transformation, teaching students to harness data science concepts, theories, and practices to solve real-world business challenges. By the end of the course, you’ll not only grasp the essential concepts but also be ready to lead innovative changes in the digital era. Here’s what you’ll achieve:
- Evaluate and integrate data science principles to solve real-world business challenges, demonstrating creativity in data interpretation and insight extraction. (Transferable Skill: Critical Thinking)
- Apply advanced data science methods and algorithms to develop and optimise models that address complex business problems. (Transferable Skill: Problem Solving)
- Synthesise insights using statistical and machine learning techniques to make informed decisions, effectively communicating results to diverse audiences. (Transferable Skill: Communication)
- Design and assess advanced visualisations, dashboards, and BI tools to deliver actionable insights and enhance business decision-making. (Transferable Skill: Service Orientation)
- Collaborate within teams to design and implement data-driven solutions, fostering teamwork and adaptability. (Transferable Skill: Team Competence)
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.
Opening up new digital opportunities
This course is ideally suited to professionals seeking advanced data science skills. It opens up careers in data analysis, machine learning, business intelligence and AI. Graduates can become data scientists, analysts, AI specialists, or business intelligence developers.
Our master’s programme and micro-credentials provide a diverse array of modules that perfectly complement the Data Science for Business course. Enhance your expertise and advance your career by exploring the range of courses offered at Digital4Business.
Download the prospectus
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.
FAQ
Minimum B2 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. Alternatively, proficiency may be assessed via a test or interview
Relevant EQF Level 6 qualification required in a relevant field including but not limited to: computer science, IT, engineering, maths, business, or economics. Without this you will have an interview and assessment to evaluate certifications, qualifications or professional experience.
This EU co-funded programme is open to all EU27, EEA, UK and Ukrainian nationals with a passport or valid ID from one of these countries.