Publications by Luz Melo
Ethical Model Evaluation in Data Science Projects: A Step-by-Step Guide
Ethical Measurement Ethical Interpretation of the Results Ethical Reporting Summary Ethical Measurement Correct Evaluation: Doing the Data Science Right Evaluating FAT Evaluating Other Ethical Requirements Ethical Interpretation of the Results p-Hacking Multiple Comparisons Ethical Reporting Reporting Transparently Ethical Academic Rep...
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Ethical Model Deployment in Data Science Projects: A Step-by-Step Guide
Access to the System Different Treatments for Different Predictions Cautionary Tales: Censoring Search and Face Recognition Governance Unintended Consequences Summary Access to the System Limited Access Different Versions for Different Persons Different Treatments for Different Predictions Data-driven Price Differentiation Behavior Modific...
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Data Science Ethics: Introduction
1 Introduction to Data Science Ethics 1.1 Variables 1.2 Data Cleaning 1.3 Exploratory Data Analysis Data Science Ethics: Introduction 1 Introduction to Data Science Ethics The sample data includes information on The Current Population Survey (CPS) for the 2010 - 2022 year sample. These surveys gather information on education, labor force statu...
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A Step-by-Step Guide to Building Ethical Data Science Projects
1 Introduction to Data Science Ethics 1.1 Variables 1.2 Data Cleaning 1.3 Exploratory Data Analysis 2 Ethical Data Gathering 2.1 k-anonymality 2.1.1 Supressing 2.1.2 Grouping 2.1.3 Perturbing 2.1.4 Challenges & Limitations 2.2 l-diversity 2.2.1 Challenges & Limitation 2.3 t-closeness 2.3.1 Step-by-Step Example to Implementing t-Closeness 2...
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A Step-by-Step Guide to Building Ethical Data Science Projects
1 Introduction to Data Science Ethics 1.1 Variables 1.2 Data Cleaning 1.3 Exploratory Data Analysis 2 Ethical Data Gathering 2.1 k-anonymality 2.1.1 Supressing 2.1.2 Grouping 2.1.3 Perturbing 2.1.4 Challenges & Limitations 2.2 l-diversity 2.2.1 Challenges & Limitation 2.3 t-closeness 2.3.1 Step-by-Step Example to Implementing t-Closeness 2...
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Data Science Ethics Project
1 Introduction to Data Science Ethics 1.1 Variables 1.2 Data Cleaning 1.3 Exploratory Data Analysis 2 Ethical Data Gathering 2.1 k-anonymality 2.1.1 Supressing 2.1.2 Grouping 2.1.3 Perturbing 2.1.4 Challenges & Limitations 2.2 l-diversity 2.2.1 Challenges & Limitation 2.3 t-closeness 2.3.1 Step-by-Step Example to Implementing t-Closeness 2...
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Step-by-Step Guide: Implementing Ethical Data Preprocessing in a Data Science Project
Introduction k-anonymality Fit the Linear Regression Model l-diversity t-closeness Things to Consider when Defining and Selecting Variables Fair Relabelling Summary References: Introduction The sample data includes information on The Current Population Survey (CPS) for the 2010 - 2022 year sample. These surveys gather information on education...
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Step by Step
Introduction k-anonymality Fit the Linear Regression Model l-diversity t-closeness Defining and Selecting Variables Fair Relabelling Summary References: Introduction The sample data includes information on The Current Population Survey (CPS) for the 2010 - 2022 year sample. These surveys gather information on education, labor force status, de...
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Reddit PCA
Read this question from the datascience subreddit. In this assignment, you craft a response to the question. You should help the poster understand: 1. When is PCA helpful? 2. What does PCA in a regression setting achieves? (model complexity vs data input complexity, comparison to LASSO, RIDGE) 3. Example of how it might be accomplished + code ...
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Analysis of the Framingham Heart Study
# Load the dataset and preprocess d1 <- read.csv("http://hbiostat.org/data/repo/2.20.Framingham.csv") d1 <- d1 %>% mutate(sex = factor(sex, 1:2, c("male", "female"))) # Note for the sex variable: 1 = Male, 2 = Female Introduction The Framingham Heart Study has been instrumental in identifying key risk factors for cardiovascular disease (CVD). ...
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