Postgraduate Online in Data Science and Artificial Intelligence

Be the driving force behind future strategic decisions: master data and AI with La Salle-URL

Nid: 27794
Syllabus

The Postgraduate Online in Data Science and Artificial Intelligence (30 ECTS) consists of two University Expert certifications, designed to offer you a comprehensive and practical education in the most in-demand fields in today’s job market.

1. University Expert in Data Science (15 ECTS)

You will be trained to master data analysis and its application in strategic business decision-making through the following modules:

Course 1: Fundamentals of Data Science. 5 ECTS

This course provides a balanced introduction between theory and practice in data science. It covers statistical fundamentals for data science and the use of the R programming language, concluding with a practical case application. This approach ensures the acquisition of basic statistical foundations and the necessary tools for their use in real-world data science studies

Course Syllabus

1. Introduction to Data Science

  • Basic concepts and the role of data in today’s society
  • Life cycle of a data science project
  • Types of data: Structured, Unstructured, and Semi-structured
  • Ethics and privacy in data management

2. Statistical Fundamentals for Data Science

  • Basic concepts: population, sample, and statistical parameters
  • Variables and measurement scales
  • Sampling methods and bias
  • Descriptive statistics
  • Probability and distributions
  • Statistical inference
  • Correlation and regression

3. Introduction to the R Programming Language

  • Working environment and basic concepts
  • Data manipulation: Importing, exporting, cleaning, and transformation
  • Visualization and graph creation
  • Descriptive statistics in R

4. Practical case study

  • Definition of a problem and objectives
  • Data collection and preparation
  • Data analysis using R
  • Result visualization and report generation
  • Interpretation of results, limitations, and areas for improvement

Course 2: Data and business. 5 ECTS

In today’s data-rich environment, understanding how data generates knowledge and supports business growth is essential. Modern business management relies on the ability to exploit and analyze data for strategic decision-making.

Better responses to common questions and the ability to ask new ones are possible today thanks to data science applied to business management.

This course bridges the gap between data science and business leadership, enabling professionals to leverage data-driven insights for innovation and strategic planning. It is designed for professionals with or without direct experience in business management.

Course Syllabus

1. Introduction to Data Science in Business

  • Overview of data science for business
  • Good data, bad data and fake news
  • Big data applied to city management

2. Data analysis

  • Methods for analyzing data
  • Methods for solving forecasting problems
  • Business forecasting case studies

3. Business case study

  • Description of a business problem
  • Solution and analysis of results

Course 3: Business intelligence. 5 ECTS

In today's business world, organizations must learn how to generate actionable insights from data. Business Intelligence enables companies to leverage data effectively, turning it into valuable decision-making tools.

Course syllabus

1. Purpose and utility of business intelligence

  • Data driven decision-making
  • Business intelligence projects
  • Key business indicators

2. Data management

  • From data silos to productivity: Data Warehousing
  • Structured and organized data: The Multidimensional Model
  • Data governance and supporting platforms

3. Data analysis

  • Business intelligence platforms
  • Augmented analytics

2. University Expert in Artificial Intelligence (15 ECTS)

You will explore the most advanced AI techniques and applications to develop innovative solutions:

Course 1: Fundamentals of Artificial Intelligence. 5 ECTS

This course provides a solid foundation in AI, equipping students with the fundamental skills needed to develop AI-based systems.

Course syllabus

1. Introduction to Artificial Intelligence

  • History of AI and basic concepts
  • AI paradigms
  • Ethics and current AI challenges

2. Introduction to Python

  • Fundamentals of Python programming
  • Data manipulation and analysis
  • Libraries for AI development
  • Data visualization using Python
  • Data preparation techniques

3. Practical case study

  • Problem statement and objectives
  • Data collection and preprocessing
  • AI technique selection
  • Implementación de un modelo básico en Python
  • Basic model implementation in Python
  • Model evaluation and improvements

Course 2: Knowledge-Based Systems. 5 ECTS

This course introduces the fundamental concepts of artificial intelligence, starting with search algorithms to understand what type of problems they can solve and what characteristics they have. These algorithms help us design and understand the structure of any Knowledge-Based System.

Additionally, this foundation enables the introduction of Machine Learning concepts, covering supervised learning methods (k-NN, Decision Trees, etc.) and unsupervised learning methods (Clustering).

Finally, the course concludes with semantic web and linked data (graphs). The semantic web allows for the enrichment of data sets through formal knowledge representations: ontologies.

Course syllabus

1. Artificial Iintelligence and knowledge-based systems

  • Introduction to Artificial Intelligence and Knowledge-Based Systems
  • Blind Search Algorithms
  • Heuristic Search Algorithms

2. Machine Learning

  • Introduction to Machine Learning
  • Analogical Learning: KNN
  • Inductive Learning: decision trees, ID3, C4.5

3. Unsupervised learning and semantic web

  • Unsupervised learning: clustering
  • Semantic web and ontologies

Course 3: Artificial Intelligence for Data Science. 5 ECTS

In data science, applying artificial intelligence to a Knowledge-Based System is essential. These methods allow us to explicitly represent knowledge stored in a knowledge base.

This course covers methods and tools of applied artificial intelligence, useful for data analysis and model generation. It completes the Machine Learning concepts and introduces deep learning techniques and advanced artificial intelligence methods.

Course syllabus

1. Machine Learning Algorithms

  • Bagging & Boosting
  • Support Vector Machine
  • Association Rules

2. Deep Learning

  • Multi-Layer Perceptron
  • Convolutional Neural Network
  • Recurrent Neural Network

3. Evolutionary computation

  • Introduction to evolutionary computation
  • Genetic algorithms