The microcredential in Essential Machine Learning for Data-Driven Decisions offers a practical and accessible introduction to Machine Learning, with a strong emphasis on its application in data-informed decision making. This course is aimed at professionals who want to understand how machine learning models function and how to apply them effectively in real-world scenarios.
During the program, participants will develop skills to explore and analyze data, identify patterns, and create models using key machine learning methods such as classification, regression, and clustering. The course blends theoretical understanding with hands-on practice, making use of widely adopted tools like Python, scikit-learn, and environments such as Jupyter Notebook.
A key focus of the course is on choosing the right models, interpreting their outputs, and leveraging them for both strategic and operational decisions. These skills are highly valuable across industries including healthcare, finance, marketing, logistics, and manufacturing, where data-driven insights play a crucial role.
In about one month, participants will gain a strong base in data analysis and machine learning techniques, preparing them to advance into more complex areas like deep learning and artificial intelligence. This program is designed to strengthen your professional profile within the field of data science and advanced analytics.
Degree in Applied Data Science from the UOC. Data Scientist proficient in R and Python, Computer Programming, and specialized applications. Full-stack developer and Big Data environment administrator. He is the Director of Software Development, Data Science, and AI at CD4Business. He has extensive teaching experience since 2021 across various business schools, educational centers, and universities. Member of the 2024 “Generació Propòsit” (Purpose Generation) by the Princess of Girona Foundation.
Learning methodology
The teaching methodology of the programme supports student learning and the development of the required competencies.
The microcredential is delivered in a hybrid format, combining four live online sessions with additional materials such as exercises, practical examples, and applied activities available through a virtual campus. Learning is based on real-world case studies and the resolution of professional challenges.
Faculty members provide continuous support and personalized feedback on assigned tasks and exercises.
Training Content
- Unit 1. Introduction to Machine Learning. Origins, architectural taxonomy, and practical use cases to identify when and how to apply automated learning.
- Unit 2. Supervised Learning. Regression, classification, and self-supervised techniques to model labeled data and solve predictive challenges.
- Unit 3. Unsupervised Learning. Clustering, dimensionality reduction, and anomaly detection methods to uncover hidden patterns in unlabeled datasets.
- Unit 4. Reinforcement Learning. Development of autonomous decision-making systems through model-based strategies and reward-penalty policies.
It is recommended to have some familiarity with the Python environment for data handling and analysis, as well as a basic knowledge of core statistical concepts. This course is intended for individuals who already have some experience working in data analysis settings.
Certificate/badge details
Microcredential. Europass digital credential in Essential Machine Learning for Data-Driven Decisions issued by the Universitat Politècnica de Catalunya.
