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Data Science

Code 14479
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements N/A
Learning outcomes The goal of this course is to improve decision making processes through the analysis of data. How data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data modelling as deep learning. Hence, this course transmits a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. In this sense, it can be seen as closely related to the fields of data mining and machine learning, but broader in scope.
Syllabus - What is data science? Data infrastructure: challenges due to volume, heterogeneity and inconsistency/incompleteness;
- Fundamentals of Data Science: framing, data transformation, exploratory analysis, feature extraction and modeling;
- Encoding and Data Formats
- Databases: relational and unstructured;
- Data visualization and summarization
- Bar graphs, lines, "Pie", Histograms, "Boxplots", scatter plots and heat maps;
- Dimensionality reduction
- Axis Rotation (PCA);
- Type Transformation (Wavelets, Spectral Analysis)
- Probability distributions;
- Anscombe's Quartet;
- Big data
- Hadoop, HDFS, PySpark;
- "MapReduce" paradigm;
- Frequent pattern mining model;
- Outlier analysis;
- Meta-Algorithms;
- Web Mining and Social Network Analysis;
- Software Engineering and Computational Performance
- CRAP Design and Modeling;
- Fundamental Data Structures;
Main Bibliography - C. Aggarwal. Data Mining: the textbook. Springer, ISBN: 9783319141411, 2015.
- John Kelleger. Data Science. MIT Press Essential Knowledge Series, ISBN: 0262535432,
2018.
- Field Cady. The Data Science Handbook. Wiley, ISBN: 1119092949, 2017.
Teaching Methodologies and Assessment Criteria Attendance (A)
Approval of the subject is subject to minimum attendance levels of 80% in classes.
Practical Work (P)
The practical work of the discipline contributes 10 values ??to the final classification.
Approval to the subject requires a minimum grade of 5 values ??in the practical work.
Part 1: Data Science Use Case: In-depth Report and Presentation (3 values): March 26, 2021, 23:59, via email. (Groups of up to 4 elements)
Part 2: Data Science Hands-On Project: Supermarket Analysis (7 values): May 28, 2021, 23:59, via email. (Individual)
Obtaining a classification in the practical works is conditioned to the individual presentation/discussion of each student with the teacher.
Frequency
Exam (F1) - Friday, June 4, 2021, 14:00-16:00, Room 6.17
Classification Teaching/Learning (C)
C=P1*3/20+P2*7/20+F*10/20
Admission to Exam
Students who obtain a minimum classification of 6 values ??in the Teaching-Learning component are considered admitted to the Exam.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2022-03-22

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