Universitat Internacional de Catalunya

Technology, Innovation & Leadership

Technology, Innovation & Leadership
6
13365
1
First semester
OB
Main language of instruction: English

Teaching staff


Dr. YARDIMCI, Atilla - atilla@uic.es

Lecturer: Atilla YARDIMCI, PhD.

Phone: +34 655 243 594

E-mail: atilla@uic.es

Office: Inmaculada, 22. 08017 Barcelona. Spain.

Office hours: By appointment

 

Introduction

The course is based on learning by doing and places the students with the constraints of entrepreneurs: limited time, limited knowledge and limited resources. Giving a general knowledge of the digital transformation, no-coding and digital marketing tools. Introducing the culture and the methods of digital tools development, in order to prepare them to manage teams of developers and digital projects. Practising problem analysis, entrepreneurial thinking and customer-centricity.

Pre-course requirements

  • Proficiency in English
  • Basic knowledge of business concepts
  • Fundamentals of marketing
  • Basic computer literacy

Objectives

The objective of this course is to introduce the fundamental concepts and technical aspects of Artificial Intelligence (AI) and Generative AI (GenAI) within the business ecosystem.

The course aims to provide students with a clear understanding of how AI-driven technologies create opportunities and transform innovation processes, particularly in Venture Capital (VC) and Private Equity (PE).

In addition, the course emphasizes the integration of data analytics, technology leadership, and innovation management as key drivers of business transformation and competitive advantage.

By the end of the course, students will be able to:

  • Understand core concepts of AI, GenAI, data science, and analytics in business contexts
  • Identify AI-driven technology trends and evaluate their impact on innovation and investment strategies
  • Apply data analytics and statistical thinking to support business decision-making
  • Use analytical tools (e.g., Python, Power BI, SQL) to generate insights and support strategic decisions
  • Analyze opportunities and challenges in adopting AI-driven solutions within organizations
  • Understand how to lead and manage data-driven and AI-focused teams in organizations
  • Integrate AI, analytics, and innovation strategies into real-world business and investment scenarios
  • Evaluate the role of technology leadership in driving digital transformation and sustainable growth

Competences/Learning outcomes of the degree programme

  • CB10 - Possess the learning skills that enable continued study in a manner that will be largely self-directed or autonomous.
  • CB8 - To absorb knowledge and face the complexity of making judgements based on information that, although incomplete or limited, includes reflections on social and ethical responsibilities connected to the application of your knowledge and judgements.
  • CB9 - Communicate conclusions, and the knowledge and ultimate reasons that underpin them, to specialist and non-specialist audiences in a clear and unambiguous manner.
  • CE10 - Interact with the entrepreneurial ecosystem and establish relationships with its actors, institutions, incubators, accelerators and investor networks, among others.
  • CE3 - Interpret and master different startup financial cycle models and apply them by drawing up the most efficient financing, be it venture capital, private equity or other forms of financing.
  • CE8 - Identify human resources with sought-after talent in order to attract or train them in a new environment of innovation and technological transformation.
  • CG2 - Possess planning and organisational skills, whilst having the flexibility to adapt these plans to new situations.
  • CT3 - Show sensitivity for ethical, personal, social and environmental values when making decisions and building relationships with others.

Learning outcomes of the subject

By the end of this course, students will be able to apply fundamental concepts of Artificial Intelligence (AI), Generative AI (GenAI), data science, and analytics to analyse business problems and support decision-making in entrepreneurial and investment contexts. They will evaluate AI-driven technology trends, use analytical tools to generate insights, interpret key business metrics, and design data-driven strategies for innovation and growth, while demonstrating teamwork, communication skills, and an understanding of technology leadership in AI-driven environments.

Syllabus

Session 1: AI and Digital Transformation
Introduction to AI and GenAI concepts and their role in business transformation.
Discussion of key technology trends and their impact on innovation and entrepreneurial ventures.

Session 2: Data Science for Business
Overview of data science concepts and methodologies in business contexts.
Understanding how data supports decision-making in VC/PE environments.

Session 3: Data-Driven Management & KPIs
Introduction to key performance indicators such as CAC, LTV, churn, and MRR.
Application of metrics in business analysis and investment decisions.

Session 4: CRM & Growth Analytics
Analysis of customer behaviour, retention, and churn.
Design of growth strategies using CRM and data analytics.

Session 5: Growth Metrics in VC/PE
Use of growth analytics in startup valuation and fundraising processes.
Evaluation of financial performance and scalability of ventures.

Session 6: Predictive Analytics & Big Data
Introduction to predictive models, forecasting techniques, and big data concepts.
Application of analytics to support strategic decisions.

Session 7: Data Science Tools
Practical use of Excel, SQL, Python, and Power BI.
Hands-on analysis to generate business insights.

Session 8: NLP & Text Analytics
Introduction to Natural Language Processing and sentiment analysis.
Use of text data for market insights and investment evaluation.

Session 9: Leadership in AI
Understanding technology leadership and managing AI-driven teams.
Exploration of innovation culture in digital organizations.

Session 10: GenAI & Digital Maturity
Applications of GenAI tools in business processes.
Assessment of digital maturity and transformation strategies.

Session 11: Innovation & Pitch Development
Development of data-driven business ideas and pitch decks.
Use of storytelling and KPIs to communicate value to investors.

Session 12: AI in Financing & Strategy
Applications of AI and deep learning in finance and risk analysis.
Integration of AI into strategic decision-making processes.

Evaluation systems and criteria

In person



The final grade will be calculated based on the following components:

  • Participation (10%): Active engagement during class sessions, including discussions and optional homework activities.
  • Class Activities (30%): Performance in group-based sessions, practical exercises, and in-class problem-solving activities.
  • Assignments (30%): Completion of mandatory individual and group assignments (2 assignments, each weighted at 15%).
  • Final Project (30%): Individual project evaluating the application of AI, data analytics, and business strategy concepts.

Final Project Requirements:
The final project will consist of a structured presentation including:

  • Business problem definition
  • Data understanding
  • Data preparation
  • Model / AI solution
  • Evaluation (solution accuracy)
  • Strategy and insights
  • Presentation skills

Additional Notes:

  • Homework is optional but recommended as part of participation.
  • Assignments are mandatory.
  • The final grade corresponds to the weighted average of all components.
  • Active participation and attendance are expected throughout the course.

Bibliography and resources

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