| Code |
16682
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| Year |
3
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
N/A
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Learning outcomes |
At the end of this course unit, the student is expected to be able to:
- Understand what data analysis is and how it fits into the data science workflow;
- Understand the nature of different types of data and the need to process them;
- Knowing how to apply the different types of data analysis (descriptive, predictive, prescriptive and diagnostic);
- Organize and synthesize data to obtain the necessary information to answer the questions being studied;
- Apply tools for objective and effective data visualizations that result in concrete actions;
- Understand what streaming data is and in what context it applies;
- Know how to analyze data in streaming data and time series;
- Use the Python language and its libraries for data analysis.
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Syllabus |
1. Introduction to Data Analysis and understanding of its importance in Data Science;
2. Characterization of different types of data and their nature;
3. Characterization of different types of data analysis;
4. Reduction of size/number of variables;
5. Visualization of data for analysis of data;
6. Statistical summary of data;
7. Selection of predictive variables;
8. Streaming Data;
9. Python for data analysis.
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Main Bibliography |
Reis, Joe, and Matt Housley. Fundamentals of Data Engineering. " O'Reilly Media, Inc.", 2022.
McKinney, Wes. Python para análise de dados: Tratamento de dados com Pandas, NumPy e IPython. Novatec Editora, 2018.
Rogel-Salazar, Jesus. Data science and analytics with python. CRC Press, 2018.
Nelli, Fabio. Python data analytics: Data analysis and science using PANDAs, Matplotlib and the Python Programming Language. Apress, 2015.
Mukhopadhyay, Sayan. Advanced data analytics using Python: with machine learning, deep learning and nlp examples. Apress, 2018.
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Teaching Methodologies and Assessment Criteria |
The contents of this course unit are presented in theoretical lectures (expository and interactive method), and its practical aspect is explored in practical laboratory classes. Each type of class has two hours of contact per week.
The practical classes are guided by laboratory manuals that students execute on laboratory computers. The practical and the proposed individual and group assignments are designed so that students develop the technical abilities described in the objectives by designing and implementing labs. The theoretical and practical components are assessed using two main elements:
- Labs 50% - 10 values;
- Project 50% - 10 values.
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Language |
Portuguese. Tutorial support is available in English.
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