Artificial Intelligence la salle campus barcelona

Postgraduate in Artificial Intelligence and Data Science Management in Business

Grow your business by using data combined with Artificial Intelligence

Nid: 27008
Syllabus
Module 1: Data Science and Big Data in Business

1. Introduction to Data Science, Big Data, Deep Learning, and Machine Learning

  • Definition of various concepts related to data science
  • Theory of different methods such as Machine Learning (supervised, unsupervised, and semi-supervised)
  • Illustration with real-life cases (Morale Machine)

2. Data-Driven Management

  • Data-driven business models
  • Legal challenges of Big Data – Big (Brother) Data
  • Data-Driven organization: new corporate strategy
  • AI Canvas Model
  • Testimony of a Data Scientist on the impact of Big Data in their company

3. Exploratory Data Analysis

  • Practice with real Big Data cases
  • Use of open data
  • Illustration with issues in HR, Marketing, Health, etc.

4. Supervised and Unsupervised Machine Learning Models

  • Python practice with supervised Machine Learning algorithms (Knn, RF, SVM)
  • Python practice with unsupervised Machine Learning algorithms (PCA, AGNES, DIANA)
  • Application to real business cases

5. Data Storytelling and Data Visualization

  • Data Storytelling: methodology
  • Design principles: color, shape, and visual thinking
  • Learning interaction techniques with data
  • Exploring common tools for creating charts: Tableau or Power BI
  • Data visualization tools: Reporting and Dashboarding

6. Project and Real Business Case

  • Application to a real case: your business
Module 2: Artificial Intelligence in Business

1. Introduction and Context

  • Introduction and evolution of artificial intelligence
  • AI applications in business
    • When to apply Ai and when not
    • When does AI add value?
    • Traditional business and AI, and AI-based business models
  • Workshop: design thinking applied to AI
    • Tools for defining AI solutions in the company

2. AI in Practice

  • AI and decision-making
  • AI and process automation
    • Examples and hands-on exercises of application in companies
  • Generative AI and AI tools
    • How do we identify the ideal tool for our task? (Audio/video/image/text/tables/etc)
  • Workshop: defining challenges
    • Selection and design of AI solution applied to a chosen challenge (Udio/Perplexity/ElevenLabs/NoteGPT/EverART/LivePortrait/etc)

3. Business Applications of AI

  • AI and data analysis
    • Machine Learning models: predictive analysis, customer segmentation, anomaly detection, etc.
  • Application of AI in business
    • Use of AI in price optimization, sentiment analysis, demand forecasting
  • Workshop: developing a project

4. AI Strategy

  • AI strategies in business
    • How to include AI in the company’s strategic plans? The importance of aligning AI with business strategy
  • AI value chain and AI project management
  • Workshop: strategy and leadership with AI

5. Implications of AI for Proper Management

  • AI and digital transformation
  • AI risks and security
  • AI ethics
  • AI regulation and policy
  • Workshop: ethical assessment of AI models

6. AI Management and Governance

  • Implementation of an AI strategy in an organization
    • Managing the risks of an AI solution
  • Data analytics and decision-making tools supported by AI models
    • Fraud detection using Amazon Fraud Detection

7. AI Project

  • Planning the execution of an AI project
    • Own development vs outsourcing
  • Implementing an AI project in a business scenario
  • Workshop: applying AI
    • Development a chatbot for customer service, creating a machine learning model to predict customer churn

8. Final Project Presentation and Debate