1 ECTS

Introduction to training and explanation of how it works;

What is Analytics and why is it important?

Types of Analytics;

Success stories and applications.

Module 2. Simplifying Data

Data processing;

Descriptive and inferential statistics;

Exploratory graphics.

Integrate and clean databases;

Produce relevant visualisations of the data and interpret them in the context of the problem;

Discover key trends in a dataset;

Discover anomalous observations in a database;

Interpret the results of an exploratory data analysis in the context of the problem.

Predicting with Artificial Intelligence

Artificial Intelligence algorithms for classification, regression, and clustering problems;

Training and evaluation of Artificial Intelligence algorithms;

Modelling and forecasting time series.

Recognise classification, regression and clustering problems and select the appropriate Artificial Intelligence algorithms to solve them;

Train and evaluate Artificial Intelligence algorithms on realistic problems, such as price estimates, fraud risk assessment, content recommendation or demand forecasts over time.

Make better decisions with Optimisation and Simulation

Mathematical modelling of problems;

Search for solutions using optimisation and simulation methods.

Recognise problems that can be addressed using optimisation and simulation techniques, such as scheduling, route planning and stock provisioning;

Mathematically model a problem, taking into account decision alternatives, objectives and complex constraints;

Select the appropriate method for finding solutions and supporting decision-making.

Analytics and Artificial Intelligence in Practice

Resolution of various case studies;

Independently recognise the most appropriate analytical approach to a problem from among all those learned in the course;

Communicate the results of data analysis in language appropriate to decision-makers in the context of the problem.