Publication date : 09/30/2025
Course : Descriptive statistics, introduction
Practical course - 2d
- 14h00 - Ref. UES
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 | Understanding the benefits of descriptive statistics |
| Understand how to process raw data |
| Understanding basic statistical tools and calculations |
| Pose a statistical problem and find the appropriate method |
Intended audience
Professionals who need to perform statistical calculations on a daily basis to process their data. Data analysts, decision support project managers, future data scientists.
Prerequisites
No special knowledge required.
Practical details
Hands-on work
A full afternoon is devoted to practicing descriptive statistics on data chosen by the participants.
Teaching methods
Each participant will bring a data file that they use professionally to calculate basic statistics.
Course schedule
1 Definition
- Definition of descriptive statistics. The study of uncertainty.
- Comparison of calibrated products with random data.
- Introduction to the randomness of statistical data.
- Conclusion: the statistician's question.
Exercise
Studying the statistician's problem: identifying the differences between standardized products and others with a hazard.
2 Mathematical formalization
- Indexing from 1 to n. Absolute value.
- The Sigma symbol for writing sums.
- The square and the square root.
- Numbers, frequencies, quartiles, percentiles: explanations and graphical representations.
- Interval calculation: processing continuous data.
Exercise
Application of each concept presented on exercises.
3 Statistical processing of one-dimensional data
- Data type: qualitative or quantitative.
- Data with numbers: frequency calculation and interpretation.
- Data sorting and processing: statistical formatting of various examples of raw data.
- Graphical representation.
- Position parameters: mean, mode, median.
- Dispersion parameters: range, quantiles, decile, variance.
- Variance: an average "of deviations".
Exercise
Data transformation, sorting and representation. Dispersion measurement.
4 Random variables
- Definition. Category of variables.
- Examples and examination of random variables.
- Distribution curves.
- Explanations of confidence intervals.
- The best-known law: the normal law.
Exercise
Use a normal distribution table.
5 Two-dimensional descriptive statistics: contingency tables
- The data.
- Graphical representation.
- Covariance.
- The linear correlation coefficient.
Exercise
Calculation of covariances and correlation coefficients. Analysis.
6 Case study: using participant data
- Highlighting the statistical problem.
- Data formatting.
- Calculation of basic statistics and graphical representation.
- Finding the right method for the problem.
Customer reviews
4,5 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
ANNE D.
20/10/25
5 / 5
Complete in a short space of timeGood balance between theory and practice
JEROME C.
20/10/25
5 / 5
Personally, I'd have liked a bit more style or colour to help me find the information quickly.
RAMIREZ ERIC T.
13/10/25
5 / 5
I really appreciated the exercises because they gave me a better understanding of the conceptsI had difficulty accessing the document platform at the beginningI would have liked to do more exercises, perhaps even to extend the course over a 3rd dayThe trainer explained things well and the pace of the course was very well adapted to the content and topics covered.
PARTICIPANTS
Professionals who need to perform statistical calculations on a daily basis to process their data. Data analysts, decision support project managers, future data scientists.
PREREQUISITES
No special knowledge required.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Professionals who need to perform statistical calculations on a daily basis to process their data. Data analysts, decision support project managers, future data scientists.
PREREQUISITES
No special knowledge required.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
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