Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).

4 credits

15.0 h + 15.0 h

Q1

Teacher(s)

Bugli Céline;

Language

French

Main themes

Main themes: - Steps of a statistical data analysis with a statistical software - Classes of statistical software - Statistical graphics: main classes of graphics and efficient use - Basic statistical analysis with "point and click" statistical software. Data cleaning. - Programming in the R language. - Programming in SAS.

Aims

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At the end of this course, the students will have gain a critical view of the different classes of statistical software available on the market and basic culture on statistical algorithms and graphics. They will also be able to realise basic statistical analysis with different software (SAS, R, Excel, SPSS, JMP) and write programs in the R and SAS programming languages. |

Content

Lecture: Steps in statistical analysis of computer data. Introduction to the different classes of statistical software. Graphical presentation of data. Introduction to statistical software, Introduction to the use of the computer room. Case studies of data set analysis using basic statistical methods. Generation of random numbers. Numerical problems encountered in regression. Introduction to R and SAS. Communication between different software and languages (R, SAS, Python, etc...).

Exercises: SAS and R programming exercises. Case studies with SPSS or JMP software.

Exercises: SAS and R programming exercises. Case studies with SPSS or JMP software.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The course consists of lectures with demonstrations of statistical software and software use exercises sessions designed to give the student maximum autonomy: each student works at his own pace on the basis of evolving documents.The modalities foreseen will evolve according to the health situation.

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Two MANDATORY programming jobs in SAS and R.Computer-based examination (if the sanitary situation allows it). Solving basic statistical case studies with SAS Enterprise Guide and SPSS (or JMP) software, SAS programming and R.

Please note that the required work must be carried out during the first quarter of the year according to a schedule that will be communicated to you at the beginning of the course. In the event of failure to submit a work, the student will have 0 on his first pass of the exam. However, with the teacher's permission, he or she may be able to take an additional question to catch up on his or her score from the second time he or she passes the exam. His request to re-score the work should be made BEFORE the start of the examination session and will only be considered if the work has not been returned or is missed (less than 50%).

Other information

SCORES

Students enrolled in both parts of the course must pass both parts to pass the course. If the score of one of the 2 parties is less than 50%, this score will be used as the total score for the course.

The points awarded to projects depend on your success in the programming questions during the exam:

Project score on 1.25 if your project score > 2*scores programming questions of the exam

Project score on 2.5 if your project score ≤ 2*scores programming questions of the exam

Students enrolled in both parts of the course must pass both parts to pass the course. If the score of one of the 2 parties is less than 50%, this score will be used as the total score for the course.

The points awarded to projects depend on your success in the programming questions during the exam:

Project score on 1.25 if your project score > 2*scores programming questions of the exam

Project score on 2.5 if your project score ≤ 2*scores programming questions of the exam

Online resources

Faculty or entity

**LSBA**

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme

Sigle

Credits

Prerequisites

Aims

Master [120] in Data Science : Statistic

Master [120] in Mathematics

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Minor in Statistics, Actuarial Sciences and Data Sciences

Master [120] in Mathematical Engineering

Master [120] in Agricultural Bioengineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Environmental Bioengineering

Master [120] in Chemistry and Bioindustries

Approfondissement en statistique et sciences des données

Master [120] in Statistic: General

Master [120] in Statistic: Biostatistics

Master [120] in Biomedical Engineering