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Presentation
Presentation
This module offers an in-depth view of the field of Data Science, teaching fundamental principles and methods of data analysis and machine learning. The unit is aimed at data analysts and data scientists and emphasizes the importance of using descriptive andpredictive analytical models obtained from data. By the end of the module, students will have acquired the necessary knowledge and developed a comprehensive set of skills to represent and interpret reality through data and to use artificial intelligence methods to identify patterns and make predictions.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6606-23869
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Introduction to Data Science What data represents Structuring data Data processing and cleaning Structuring databases Business intelligence Descriptive statistics Visualisation Python CRISP-DM methodology Classification Prediction Cluster analysis Neural networks
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Objectives
Objectives
Current times are characterized by an exponential growth in the volume, variety, and velocity of data production. The curricular unit of Data Science aims to teach students, with the support of case studies and software, main methodologies, methods, techniques and tools that underpin the preparation, structuring, description, analysis, inference and visualization of data to extract knowledge, detect patterns and support decision-making. It is intended that students develop skills in identifying problems that can be solved using data science, in describing data, in structuring problems through descriptive and predictive models, in the use of data analysis tools, and in the interpretation of the results obtained after applying business intelligence, statistical and artificial intelligence methods to support decision-making and understanding.
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Teaching methodologies and assessment
Teaching methodologies and assessment
The teaching methods for this data science module aim to create an engaging, effective, and hands-on learning experience. Lectures provide foundational knowledge, in-class exercised and debates encourage active participation and critical thinking, state-of-the-art software on business intelligence and artificial intelligence provide the actual tools used by current data analysts and data scientists, and the hands-on application of these tools and syllabus to solve a real-world problem using Big Data empowers the students to learn and acquire the skills needed to develop and apply Data Science in their future professional environments.
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References
References
Békés, G, Kézdi, G (2021). Data Analysis for Business, Economics, and Policy. Cambridge Books, Cambridge University Press Igual, L, Seguí, S (2017). Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications. Springer
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Office Hours
Office Hours
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Mobility
Mobility
No
