Publication date : 02/07/2024
Course : Data Analytics with Rdata modeling and representation
Practical course - 4d
- 28h00 - Ref. DTA
|
Download in PDF format
Share this course by email
 | Understanding the principle of statistical modeling |
| Choosing between regression and classification according to data type |
| Evaluating the predictive performance of an algorithm |
| Create selections and rankings from large volumes of data to identify trends |
Intended audience
Data center managers (Datamining, Marketing, Quality, etc.), database users and business managers.
Prerequisites
Basic knowledge of statistics and R, or have taken the courses "Statistics, mastering the fundamentals" (Ref. STA) and "R environment, data processing and analysis ... " (Ref. TDA).
Course schedule
1 A reminder of the R language
- Data types in R.
- Import-export data.
- Techniques for drawing curves and graphs.
Role-playing
Getting to grips with scripts and Notebooks.
2 Component analysis
- Principal Component Analysis.
- Correspondence Analysis.
- Multiple Correspondence Analysis.
- Factorial Analysis for Mixed Data.
- Hierarchical Principal Component Classification.
Hands-on work
Implement the reduction in the number of variables and identify the factors underlying the dimensions associated with significant variability.
3 Modeling
- Steps in building a model.
- Supervised and unsupervised algorithms.
- The choice between regression and classification.
Hands-on work
Set up dataset sampling. Carry out evaluation tests on several supplied models.
4 Model evaluation procedures
- Re-sampling techniques in training, validation and test games.
- Testing the representativeness of training data.
- Performance measurements for predictive models.
- Confusion and cost matrix, ROC and AUC curves.
Hands-on work
Set up dataset sampling. Perform evaluation tests on several supplied models.
5 Unsupervised algorithms
- Hierarchical clustering.
- Non-hierarchical clustering.
- Mixed approaches.
Hands-on work
Unsupervised clustering on multiple datasets.
6 Supervised algorithms
- The principle of univariate linear regression.
- Multivariate regression.
- Polynomial regression.
- Regularized regression.
- The Naive Bayes.
- Logistic regression.
Hands-on work
Implement regressions and classifications on several types of data.
7 Text data analysis
- Text data collection and pre-processing.
- Primary entity extraction, named entity extraction and referential resolution.
- Grammatical labeling, syntactic analysis, semantic analysis.
- Lemmatization. Vector representation of texts. TF-IDF weighting.
PARTICIPANTS
Data center managers (Datamining, Marketing, Quality, etc.), database users and business managers.
PREREQUISITES
Basic knowledge of statistics and R, or have taken the courses "Statistics, mastering the fundamentals" (Ref. STA) and "R environment, data processing and analysis ... " (Ref. TDA).
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.
Data center managers (Datamining, Marketing, Quality, etc.), database users and business managers.
PREREQUISITES
Basic knowledge of statistics and R, or have taken the courses "Statistics, mastering the fundamentals" (Ref. STA) and "R environment, data processing and analysis ... " (Ref. TDA).
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.
Dates and locations
Select your location or opt for the remote class then choose your date.
Remote class
Last places available
Guaranteed date, in person or remotely
Guaranteed session