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Data Analytics

Data Analytics

Code	16682
Year	3
Semester	S1
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Informatics
Entry requirements	N/A
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.
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.
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.
Language	Portuguese. Tutorial support is available in English.