Predictive Data Analytics
Mälardalen University
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Predictive Data Analytics
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Utbildningsfakta
Kod: DVA478
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling About this course The course aims to give insights in fundamental concepts of machine learning for predictive analytics to provide actionable, i.e., better and more informed decisions in, forecasting. It covers the key concepts to extract useful information and knowledge from data sets to construct predictive modeling. Introduction: overview of Predictive data analytics and Machine learning for predictive analytics. Data exploration and visualization: presents case studies from industrial application domains and discusses key technical issues related to how we can gain insights enabling to see trends and patterns in industrial data. Predictive modeling: consists of issues in construction of predictive modeling, i.e., model data and determine Machine learning algorithms for predicative analytics and techniques for model evaluation. You will learn * Select suitable machine learning algorithms to solve a given problem for predictive data analytics. * Explore data and produce datasets suitable for analytical modeling. * Basics of machine learning for predictive analytics
Behörighet
90 credits of which at least 60 credits in Computer Science or equivalent, including 15 credits in programming as well as 2,5 credits in basic probability theory and 2,5 credits in linear algebra, or equivalent. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.
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Publiceringsversion: 8e217193-f5fa-4778-b085-a4521fd03e8d
Kontrollsumma: 1b0dc54c0fc8a359f83ba9dc8f9d468479ce432de4c03bce3b8b33dd67fe3f6c
Senast ändrad enligt källan: 2025-03-14T10:56:36