Course : Python Data Science, manipulating and visualizing dataoptional TOSA® certification
Practical course - 4d
- 28h00 - Ref. IYT
|
Download in PDF format
Share this course by email
 | Get an overview of Python's scientific ecosystem |
| Learn about the essential scientific libraries for data analytics |
| Be able to manipulate large data sets with Python |
| Understanding the benefits of datavisualization |
| Visualizing data with Python |
Intended audience
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to learn about the scientific world of Python.
Prerequisites
Python language practice.
Practical details
Hands-on work
Numerous exercises are used to illustrate the topics.
Teaching methods
Active teaching, feedback and demonstrations are provided by the trainer to enable participants to put them into practice more quickly.
Course schedule
1 Introduction to the scientific Python ecosystem
- Overview of Python's scientific ecosystem: must-have libraries.
- Know where to find new bookstores and assess their sustainability.
- The main open source tools and software for data analysis.
- Why use scientific distribution, Anaconda.
- Understand the benefits of a virtual environment and know how to use it.
- IPython interpreter and Jupyter server.
- Best practices for getting your data analytics project off to a good start with Python.
- Scientific file formats and libraries for manipulating them.
Hands-on work
Setting up the development environment: installing Anaconda, creating a virtual environment, exporting and duplicating an environment, using Jupyter notebooks.
2 The SciPy Stack
- The SciPy Stack is the foundation of the essential scientific libraries on which all the others are based.
- NumPy: numerical calculation and linear algebra (vectors, matrices, images).
- SciPy, based on NumPy for statistics, functional analysis, geospatial analysis, signal processing, etc.
- Pandas: tabular data analysis (CSV, Excel, etc.), statistics, pivots, filters, search...
- Matplotlib: the essential data visualization library.
Hands-on work
Measure the performance of the NumPy installed by your Linux and that of Anaconda. Image processing with NumPy. First plots. Statistical analysis of CSV files. First mapping elements. Fourier transforms.
3 Visualization libraries
- Overview of Python visualization libraries: 2D/3D, desktop/web, statistics, cartography, big data...
- Desktop-oriented libraries: Matplotlib, Pandas, Seaborn.
- Web-oriented libraries: Bokeh, Altair, Plotly...
- 3D libraries: Plotly, pythreejs, ipyvolume...
- Cartographic libraries: Cartopy, folium, ipyleaflet, Bokeh, cesiumpy...
- Big data libraries: datashader, Vaex...
Hands-on work
Multiple exercises with some of the libraries presented. Big data, cartographic, 2D and 3D visualization.
4 Data visualization
- The benefits of datavisualization
- Using PyViz and the HoloViz ecosystem.
- Overview of SuperSet, Mayavi, Paraview and VisIt tools.
Hands-on work
Continue to use visualization libraries and manipulate tools.
5 Scientific file formats and the handling of voluminous data
- Overview of the main scientific file formats: NetCDF, HDF5, GRIB, JSON, PARQUET, MATLAB, CGNS...
- Handle big data with Dask, Vaex and Xarray.
Hands-on work
Handle data exceeding GB, read and write NetCDF/HDF5 files. Visualization of climate data, satellite images, creation of videos/graphic animations.
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.
BENOIT P.
31/03/26
5 / 5
Pour répondre aux besoins divergents des élèves, ce serait probablement utile de faire un point rapide en début de chaque journée pour identifier les questions/nouveaux besoins, et réserver une heure en fin de journée pour les traiter ... et ne traiter au fil de l’eau que les demandes/questions vraiment ponctuelles (sinon risque d’ennuyer les autres ... surtout quand certains -comme moi- posent beaucoup de questions).
PIERRE D.
31/03/26
2 / 5
Formation peu en relation avec le programme proposé. Animation inexistante.Les supports ont semble-t-il été rédigés à 100 % via chatGPT.Questionnaires quotidiens qui n’a rien à voir avec ce qu’on a "vu" la veille. Les exercices sont répétitifs et ne présentent pas beaucoup d’intérêt. Aucun retour d’expérience, aucune explication sur le subtilités, aucun "tips" montré.
ANNABELLE B.
31/03/26
4 / 5
J’ai trouvé que le format jupyter notebook mélangeant cours et exercice était bon et nous permettait d’avancer à notre rythme.
Publication date : 07/08/2025
PARTICIPANTS
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to learn about the scientific world of Python.
PREREQUISITES
Python language practice.
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.
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to learn about the scientific world of Python.
PREREQUISITES
Python language practice.
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