Statistical softwares and basic statistical programming

Language

French

Prerequisites

Basic training in probability and statistics: descriptive statistics (including location measures, dispersion measures, graphs), statistical distributions, inference (principle of hypothesis testing, H0 and H1 hypotheses, test statistics, p-values and their interpretation), simple and multiple linear regression, single-criterion analysis of variance. Ability to use a personal computer: file handling, (basic) knowledge of Windows, Word, Excel, Internet Explorer and the Moodle platform. Students are not required to have any prior knowledge of R or Python or other programming languages. The course will start with the basics and lead students throughout the semester to more complex statistical problem-solving activities using both programming languages.

Main themes

The aim of the course is to introduce students to programming and algorithms using two programming languages: R and Python. This course provides an introduction to statistical programming using R and Python. Topics covered include Notions of programming and algorithms, programming with R and programming with Python.

Learning outcomes

At the end of this learning unit, the student is able to :
1	A. In accordance with the AA framework of the Master's program in Statistics, General Orientation, this activity contributes to the development and acquisition of the following AAs: primary: 1.3, 2.2, 2.5, 3.2, 3.3, 3.4, 4.1, 4.2, 4.4, 5.2, 5.3, 5.5 secondarily: 2.3 With regard to the AA framework of the Master's program in Statistics, Biostatistics specialization, this activity contributes to the development and acquisition of the following AA: primary: 1.3, 2.2, 2.5, 3.2, 3.3, 3.4, 3.5, 4.1, 4.2, 5.2, 5.4, 5.6 secondarily: 2.3 B. At the end of the course, students will have acquired a critical view of the different classes of statistical software available on the market and knowledge of statistical graphics. They will also be able to carry out basic statistical analyses on the computer using various software packages (SAS, R, Excel, SPSS, JMP) and to program algorithms and manipulate complex data files with more advanced software (SAS and R).

Content

• Programming and algorithms
• Notions of debugging
• R programming basics: use of RStudio, creation of variables, different object types (vector, matrix, factor, dataframe, list), data import, loops, conditions, functions, scripts, working directory, graphing, statistics with R, use and installation of packages
• Python programming basics: the different tools and working environments for programming in Python, variable creation, object types (integers and real numbers, Booleans, lists, dictionaries, tuples), conditions, loops, functions and packages, reading and saving files, NumPy, dictionaries, Pandas, data visualization with seaborn and matplotlib.

Teaching methods

The course is made up of lectures supplemented by demonstrations of statistical programming in R and Python, and exercise sessions designed to give students maximum autonomy: each student works at his or her own pace on the basis of evolving documents.

Evaluation methods

In this course, students are evaluated in two ways:

• Activity A: continuous evaluation consisting of 2 compulsory works to be handed in during the quadrimester, according to a timetable set at the beginning of the quadrimester (10% of the final grade).
• Activity B: an in-session computer-based written examination (90% of the final grade).

The final grade is the weighted average of grades A and B. In the final grade, part A is worth 2/20 and part B is worth 18/20.
Details of activity A :
The two MANDATORY assignments are programming projects in R and Python. Please note that compulsory projects must be completed during the first four months of the course, according to a timetable that will be sent to you at the beginning of the course. Failure to complete at least one of the assignments, or late submission, will result in an “A” absence grade for the final course grade.
Details of Activity B :
Written exam, open-book and computer-based
The final grade for the written exam will consist of 2 integrated parts. Successful completion of both parts is essential to demonstrate the skills and knowledge defined in the learning outcomes for the teaching unit. Passing the written exam is contingent on passing each of the assessment parts.

Online resources

Various documents (videos, lecture slides, etc.) available via Moodle.

Bibliography

Livre open-source : Learning with Python 3 écrit par Peter Wentworth, Jeffrey Elkner, Allen B. Downey, et Chris Meyers disponible sur la page https://openbookproject.net/thinkcs/python/english3e/

Teaching materials

• Transparents du cours et exercices disponibles sur Moodle. Accès à la documentation SAS sur le site de SAS.

Faculty or entity

> LSBA