Course : Big Data Analytics with Python
Practical course - 4d
- 28h00 - Ref. BDA
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 | Understanding the principle of statistical modeling |
| Choosing regression and classification depending on data type |
| Evaluating an algorithm’s predictive performance |
| Creating selections and classifications in large volumes of data to reveal trends |
Practical details
Hands-on work
Developing/conducting analysis in Python, with the modules pandas, NumPy, SciPy, MatPlotLib, seaborn, scikit-learn, and statsmodels.
Course schedule
1 Introduction to modeling
- Introduction to the Python language.
- Introduction to the Jupiter Notebook software.
- Steps for building a model.
- Supervised and unsupervised algorithms.
- Choosing between regression and classification.
Hands-on work
Installing Python 3, Anaconda, and Jupiter Notebook.
2 Model evaluation procedures
- Techniques for resampling in training, validation and testing sets.
- Learning data representativeness test.
- Predictive model performance measurements.
- Confusion and cost matrix and AUC-ROC curve.
Hands-on work
Setting up data set sampling. Conducting evaluation tests on multiple provided models.
3 Supervised algorithms.
- The principle of univariate linear regression.
- Multivariate regression.
- Polynomial regression.
- Regularized regression.
- Naive Bayes.
- Logistic regression.
Hands-on work
Implementing regressions and classifications on multiple data types.
4 Unsupervised algorithms
- Hierarchical clustering.
- Non-hierarchical clustering.
- Mixed approaches.
Hands-on work
Handling unsupervised clusters in multiple datasets.
5 Component analysis
- Principal component analysis.
- Correspondence analysis.
- Multiple correspondence analysis.
- Factor analysis for mixed data.
- Hierarchical classification of principal components.
Hands-on work
Reducing the number of variables and identifying underlying factors of dimensions associated with significant variability.
6 Text data analysis
- Collecting and preprocessing text data.
- Extracting primary entities, named entities, and reference resolution.
- Grammatical tagging, syntactical analysis, semantic analysis.
- Lemmatization.
- Text vectorization.
- TF-IDF weighting.
- Word2Vec.
Hands-on work
Explore the contents of a text base using latent semantic analysis.
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.
CANÉLIA P.
24/02/26
4 / 5
Le contenu de la formation était très bon. Le seul bémol était l’équilibre entre le temps de cours et le temps d’exercice qui étaient tous les 2 trop longs. La formation aurait été plus dynamique si les parties cours/exercices avaient été plus alternés.
LUDIVINE S.
24/02/26
4 / 5
Le contenu est très complet et rentre vraiment dans les détails. L’approche mathématique est importante pour comprendre le principe des algorithmes mais peut etre ces aspects ont pu être trop approfondis.Le contenu est très dense pour des personnes qui n’y sont pas initiés.Le fait de connaitre les principes de base du machine learning m’a permis d’apprécier les subtilités.Les exercices parcourus sont très classiques et souvent déjà vus. Des cas d’usages plus business se
LE STRAT CHRISTELLE D.
16/12/25
4 / 5
Very good training
Publication date : 07/15/2024
PARTICIPANTS
PREREQUISITES
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.
PREREQUISITES
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
Last places available
Guaranteed date, in person or remotely
Guaranteed session