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University of Beira Interior
DATA SCIENCE
- What is Data Science? Data infrastructure: challenges due to volume, heterogeneity and inconsistency/incompleteness;
- Data Science Fundamentals: Framing Problems, Data Wrangling, Exploratory Analysis, Feature Extraction and Modelling;
- Data Encoding and File Formats;
- Databases: Relational, Non-Structured Data;
- Data Visualization and Summarization;
- Pie, Bar Charts, Histograms, Boxplots, Scatterplots 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;
- Mining Web Data and Social Network Analysis;
- Software Engineering and Computational Performance
- CRAP Design;
- Key Data Structures;
- Amortized and Average Performance;
- 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.
- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and practical classes
- Practical Project (P) The practical projects of this course weights 50% (10/20) of the final mark
- To get approved at the course, a minimal mark of 5/20 should be obtained in the practical project part;
- The pratical project mark is conditioned to an individual presentation and discussion by each student;
- Written Test (F) Monday, June 6th, 2022, 14:00. Room 6.18
- Mark (M) M = (A >= 0.8) * (P * 10/20 + F * 10/20)
- Admission to Exams Students with M >= 6 are admitted to final exams
- The practical projects mark is considered in all exam epochs;
Theoretical slides: [pdf]
Theoretical slides: [pdf]
Theoretical slides: [pdf]
Theoretical slides: [pdf]
Theoretical slides (Clustering): [pdf]
Theoretical slides (Models Interpretability): [pdf]
Theoretical slides (Meta Learning): [pdf]
Theoretical slides (Semi-Supervised Learning): [pdf]
Theoretical slides: [pdf]
Theoretical slides: [pdf]