Visão Geral

Este Curso Data Analytics Basic Methods é uma introdução ao R, analisando e explorando dados com R e usando R com um banco de dados. Ele se concentra em estatísticas para construção e avaliação de modelos. Os tópicos abrangem pesquisa experimental, análise de correlação, regressão, intervalos de confiança e comparações de grupo e modelos paramétricos e não paramétricos.

 

Objetivo

Este Curso Data Analytics Basic Methods fornece habilidades técnicas de análise de dados para realizar análises quantitativas de dados e big data.

• Utilize conjuntos de dados reais com gráficos e medidas numéricas.
• Conduza uma análise univariada e bivariada em conjuntos de dados em R.
• Sinalize possíveis discrepâncias para uma investigação mais aprofundada.
• Ilustre graficamente a distribuição de probabilidade das variáveis.
• Diferencie distribuições de probabilidade discretas e contínuas.
• Calcule probabilidades binomiais usando aproximações normais.
• Investigue se o tamanho da amostra é grande o suficiente para aplicar o teorema do limite central.
• Aplicar técnicas de teste e estimativa para análises de regressão linear simples em R.

Materiais

Português/Inglês + Exercícios + Lab Pratico

Conteúdo Programatico

Module 1

Introduction to the R Environment

Topics

• Introduction to statistics
• The R environment

Learning Objectives

By the end of this module, you should be able to:

• Successfully navigate through all areas of the course site and get to know your instructor and classmates.
• Distinguish the difference between descriptive and inferential statistics.
• Manage the R environment.
• Successfully install the required software on your personal computer.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Introduction chapter: What is Statistics?

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer. 

• Chapter 1: Basics, Section 1.1 First Steps

Assessments

• Quiz (cumulative 10% of the final grade)

Module 2

Basics of R-language

Topics

• Variables in R
• Vectors and matrices in R

Learning Objectives

By the end of this module, you should be able to:

• Categorize the type of variables in R. 
• Interpret simple R outputs regarding vectors and matrices in R.

Readings

Required Readings

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 1: Basics, Section 1.2 Language Essentials

Recommended Readings

Lander, J. (2013). R for everyone: Advanced analytics and graphics, illustrated edition. Boston MA: Addison-Wesley.

• Chapter 4: Basics of R

Assessments

• Quiz (cumulative 10% of the final grade)
• Assignment 1 (10% of the final grade) – Due end of Module 4

Module 3

Describing Data with Graphs

Topics

• Graphs for categorical data
• Graphs for quantitative data
• Lists and arrays in R
• Data frames in R variables in R

Learning Objectives

By the end of this module, you should be able to:

• Describe data with graphs.
• Interpret and write R codes regarding vectors, matrices, lists, arrays and data frames in R.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 1: Describing Data with Graphs

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 1: Basics, Section 1.2 Language Essentials

Recommended Readings

Lander, J. (2013). R for everyone: Advanced analytics and graphics, illustrated edition. Boston MA: Addison-Wesley.

• Chapter 5: Advanced Data Structures

Assessments

• Quiz (cumulative 10% of the final grade)

Module 4

Describing Data with Numerical Measures

Topics 

• Measures of center
• Measures of variability
• Measures of relative standing
• Describing bivariate data
• Functions in R

Learning Objectives

By the end of this module, you should be able to:

• Describe univariate and bivariate data with numerical measures. 
• Interpret and write R codes regarding functions in R.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 2: Describing Data with Numerical Measures
• Chapter 3: Describing Bivariate Data

Recommended Readings

Lander, J. (2013). R for everyone: Advanced analytics and graphics, illustrated edition. Boston MA: Addison-Wesley.

• Chapter 8: Writing R Functions

Assessments

• Quiz (cumulative 10% of the final grade)

There are no learning sessions this week. You may use this time to review course materials.

Module 5

Probability Distributions, Control Statements, and Loops in R

Topics

• Probability and probability distributions
• Control statements and loops in R

Learning Objectives

By the end of this module, you should be able to:

• Compute probability distributions.
• Write R codes including control statements and loops.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 4: Probability and Probability Distributions

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 2: The R Environment
• Chapter 3: Probability and Distributions, Sections 3.1; 3.2

Recommended Readings

Lander, J. (2013). R for everyone: Advanced analytics and graphics, illustrated edition. Boston MA: Addison-Wesley.

• Chapter 9: Control Statements
• Chapter 10: Loops, the Un-R Way to Iterate

Assessments

• Quiz (cumulative 10% of the final grade)
• Assignment 2 (10% of the final grade) – Due end of Module 8

Module 6

Discrete Distributions

Topics

• Discrete distributions
• Discrete distributions in R
• Plotting tools in R

Learning Objectives

By the end of this module, you should be able to:

• Distinguish the difference of Bernoulli, Binomial, Poisson and Hypergeometric probability distributions.
• Formulate discrete distributions in R.
• Write R codes using plotting tools.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 5: Several Useful Discrete Distributions

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 3: Probability and Distributions, Sections 3.3; 3.5

Assessments

• Quiz (cumulative 10% of the final grade)

Module 7

Midterm Exam

Topics

• Core concepts from modules 1 to 5
• Preparing for the midterm exam

Learning Objectives

By the end of this module, you should be able to:

• Review core concepts from modules 1 to 5.
• Complete the practice questions in preparation for the midterm exam.

Readings

N/A

Assessments

• Midterm Exam (25% of the final grade)

Module 8

The Normal Probability Distribution

Topics

• The Normal Probability Distribution
• Applications in R

Learning Objectives

By the end of this module, you should be able to:

• Compute normal probability distribution.
• Formulate normal distribution in R.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 6: The Normal Probability Distribution

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 3: Probability and Distributions, Sections 3.4; 3.5

Assessments

• Quiz (cumulative 10% of the final grade)

Module 9

Sampling Distribution

Topics

• Sampling distributions
• Central Limit Theorem

Learning Objectives

By the end of this module, you should be able to:

• Build sampling plans and experimental designs.
• Apply central limit theorem to random samples of a population.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 7: Sampling Distributions

Assessments

• Quiz (cumulative 10% of the final grade)
• Assignment 3 (10% of the final grade) – Due end of Module 11

Module 10

Linear Regression 

Topics

• Linear regression
• Linear models in R

Learning Objectives

By the end of this module, you should be able to:

• Model the relationship of two data with the method of least squares.
• Interpret R codes for prediction of linear models.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 12: Linear Regression and Correlation

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 6: Regression and Correlation

Assessments

• Quiz (cumulative 10% of the final grade)

Module 11

Correlation

Topics

• Pearson correlation
• Visualizing correlation in R

Learning Objectives

By the end of this module, you should be able to:

• Analyze the Pearson correlation, a parametric test in statistics.
• Visualize correlated variables in R.

Readings

Required Readings

Mendenhall, W., Beaver, R.J., Beaver, B.M., & Ahmed, S.E. (2018). Introduction to probability & statistics, 4th Canadian Edition. Toronto, Ontario: Nelson Education.

• Chapter 12: Linear Regression and Correlation

Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York, NY: Springer.

• Chapter 6: Regression and Correlation

Assessments

• Quiz (cumulative 10% of the final grade)

Module 12

Final Exam Review

Topics

• Core concepts in modules 6, and 8 to 11
• Preparing for the final exam

Learning Objectives

By the end of this module, you should be able to:

• Review core concepts in modules 6, and 8 to 11.
• Complete the practice questions in preparation for the final exam.