1 🫶 About
This is the official course website for Statistics & Big data2 2025 - 2026 for laboratories. This website augments lecture topics and provides exercises for home and class assignments. Additional theory wrt slides and textbook will not be part of the exam, indeed they are for your growth and hopefully in the future as quicksilver resource to recover R proficiency from lethargy.
1.1 🔧 Logistics
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Lectures:
- Mondays: 14:00 - 17:00 CET
- Tuesdays: 10:00 - 13:00 CET
- Location: Campus Gemelli, Room 20 (and optionally remote)
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Office hours:
- Dr. Niccolò Salvini: Available via email and for questions before/after lectures
- Prof. Sophie Dabo-Niang: Available during intensive session (week of November 17th)
- here’s the shared drive (slides notebooks extra in class)
1.3 🗒 labs’ contents
1.3.1 Part 1: The Foundations (Dr. Niccolò Salvini)
- Introduction to the R ecosystem
- Install R and RStudio
- R tricks for research and professional life
- Data wrangling with R
- Hypothesis Testing Fundamentals
- Alternative hypothesis testing
- How to calculate p-values
- Hypothesis testing with null hypothesis
- Hypothesis testing on averages
- Analysis of Variance (ANOVA)
- Testing more than 2 means
- Chi-Square Tests
- Testing more than 2 proportions
- Linear Regression Analysis
- Simple linear regression
- Multiple linear regression
- Nonlinear regression
- Regression with dummy variables
- Logistic Regression
- Introduction to logistic regression
1.4 Exam 📝
The exam is going to be open and closed questions on theory and practice (coding part). You will be asked to provide results and sometimes code leading to these results. You can also be asked to directly provide code to solve for that exercise. The exam is going to take place in labs classroom, this means you are not going to have your laptop during the exam. We generally don’t provide assignment neither group works. Indeed we provide intermediate exams for those who want to try them.
we are going to have 2 intermediate sessions exams on half of the whole content of the course. This means: - first intermediate: will happen typically on November and will be on Part 1 content (Dr. Salvini’s part) - second intermediate: will happen in January/February on Part 2 content (Prof. Dabo-Niang’s part)
you can take first intermediate and take the second on each exam date within the winter session, meaning you take part 1 in Nov and part 2 in either Jan and Feb. You can not reject intermediates, that means if you take first part, try the second and did not perform well, you need to take the full. Grades may undergo to a review process before being official if they are particularly low. This has happened quite often, but it does not happen every time.
1.5 📚 Suggested reading list
I am going to split resources by the expected level of their audience:
1.5.1 Minimal or 0 knowledge of R
- Everitt, B., Hothorn, T. (2011) An Introduction to Applied Multivariate Analysis with R, Springer-Verlag
- James, G, Witten, D, Hastie, T and Tibshirani, R, (2015) An Introduction to Statistical Learning, with Applications in R
- T. Timbers, T. Campbell, M. Lee Data Science: A First Introduction, Jul 2022 online version
- Wickham, H., Grolemund G. (2018) R for Data Science, O’Reilly. Freely available on-line at https://r4ds.had.co.nz/index.html
- R for non-programmers, Daniel Dauber 2022, free book
1.6 📜 Honor Code
Permissive but strict. If unsure, please ask the course staff!
- NOT OKAY Pleeease, I am using ChatGPT and its derivatives on a daily basis. I understand, it’s awesome and we are not enforcing any rule against it a home. Don’t do that during the exam.
- OK to search, ask in public about the systems we’re studying. Cite all the resources you reference. E.g. if you read it in a paper, cite it. If you ask on Quora, include the link.
- NOT OKAY to ask someone to do assignments/projects for you, we are monitoring freelancing websites, we have a plethora of bots doing this job daily.
- OK to discuss questions with classmates. Disclose your discussion partners.
- NOT OKAY to blindly copy solutions from classmates.
- OK to use existing solutions as part of your projects/assignments. Clarify your contributions.
- NOT OKAY to pretend that someone’s solution is yours.
- OK to publish your final project after the course is over (we encourage that and if you need it I would love to help you!)
- NOT OKAY to post your assignment solutions online.
1.8 Colophon
This book was authored using bookdown inside RStudio with bs4 theme The website is hosted with Netlify, and automatically updated after Netlify CI. The complete source is available from GitHub.
This version of the book was built with:
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