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Presentation
Presentation
The Fundamentals of Statistics for Data Science course aims to provide the student with fundamental concepts of of probability theory and techniques of descriptive statistics and statistical inference, essential for the study of Engineering.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6347-25229
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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
1. . Descriptive Statistics Types of data: integers, continuous, categorical, arrays, matrices Frequency tables Measures of central tendency and variability Visualization 2. Linear Regression Independent vs. dependent variables. Scatter plots Covariance and Pearson coefficient Regression line Residuals, least squares method Calculating the estimate for the response given a certain value for the independent variable 3. Probability Random experiment. Sample space. Event. Operations between events Properties of the probability function. Probability of the union of events Law of total probability Bayes' theorem Conditional probability. Independent events 4. Statistical Inference Sample and random sample. Estimator and estimate for a proportion. Confidance intervals and hypothesis tests for a proportion.
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Objectives
Objectives
This subject aims to show that LG1: probability is as an essential measure function in science. LG2: statistics enables us to collect data, analyse data, establish hypothesis on data and test these hypothesis. Hence, both probability and statistics lead us to knowledge in science and engineering
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Teaching methodologies and assessment
Teaching methodologies and assessment
For each topic in this course, a set of application exercises is presented. Students are encouraged to solve these exercises and to raise any doubts they may have. The assessment includes a continuous component, which involves completing three tests and a final exam (Make-up). The average of the three tests is denoted as A, and the Exam Grade is denoted as B. If A > 9.5, the student is approved for the course and can take the exam to improve the grade. In this case, the Final Grade = max(A, B). If A < 9.5, the student is not approved for the course and must take the exam to obtain approval. Students who achieve a final grade of at least 10 points are considered approved.
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References
References
Murteira, B. (2012), Probabilidades e Estatística, vols. I e II MacGraw-Hill. Murteira, B. (2007), Introdução à Estatística, MacGraw-Hill. Morais, M.C. (2020). Probabilidades e Estatística: Teoria, Exemplos & Exercícios. IST Press (Coleção Ensino da Ciência e da Tecnologia). Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists. 5th ed, Academic Press.
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Office Hours
Office Hours
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Mobility
Mobility
No