Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
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
At the end of this learning unit, the student is able to : | |
| 1 |
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. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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
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.
Evaluation methods
Two MANDATORY programming jobs in SAS and R.
Computer-based examination: 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%).
Computer-based examination: 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 Forests and Natural Areas Engineering
Approfondissement en statistique et sciences des données
Minor in Statistics, Actuarial Sciences and Data Sciences
Master [120] in Chemistry and Bioindustries
Master [120] in Biomedical Engineering
Master [120] in Agricultural Bioengineering
Master [120] in Data Science : Statistic
Master [120] in Mathematical Engineering
Master [120] in Statistic: Biostatistics
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Mathematics
Master [120] in Statistic: General
Master [120] in Environmental Bioengineering