Establishment
Teaching content
FINANCE
Training officer(s)
M.Phan
Stakeholder(s)
Minh PHAN
Présentation
Prerequisite
None
Goal
At the end of the course, the student should be able to:
- be knowledgeable about the open source analytical tools in business
- detect the advantages and disadvantages of using an open source analytical tool
- be proficient in one of the most important open source analytical tool of data science: Python
- extract, manipulate, analyze, visualize and report financial data using Python
- know how to process data and prepare basetable for machine learning project using Python
These competencies and/or skills contribute to the following learning objectives
- 1.C Communicate effectively in English
- 3.A Breakdown complex organizational problems using the appropriate methodology
- 5.C Employ state-of-the-art management techniques
- 7.A Demonstrate an expertise on key concepts, techniques and trends in their professional field
- 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field
- 7.D Be a reference point for expertise-related questions and ambiguities
.
- be knowledgeable about the open source analytical tools in business
- detect the advantages and disadvantages of using an open source analytical tool
- be proficient in one of the most important open source analytical tool of data science: Python
- extract, manipulate, analyze, visualize and report financial data using Python
- know how to process data and prepare basetable for machine learning project using Python
These competencies and/or skills contribute to the following learning objectives
- 1.C Communicate effectively in English
- 3.A Breakdown complex organizational problems using the appropriate methodology
- 5.C Employ state-of-the-art management techniques
- 7.A Demonstrate an expertise on key concepts, techniques and trends in their professional field
- 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field
- 7.D Be a reference point for expertise-related questions and ambiguities
.
Presentation
Run Python programs in a PC environment
• Read raw input files in various formats and create Python datasets
• Create new variables in the data step
• Use Python procedures to describe data numerically and graphically
• Annotate Python output with informative titles, labels, and formats
• Work with datasets: sort, subset, merge, and re-format datasets using Python
• Use Python scripts for basic statistical inference
• Export data and output to other computers and softwares
• Read raw input files in various formats and create Python datasets
• Create new variables in the data step
• Use Python procedures to describe data numerically and graphically
• Annotate Python output with informative titles, labels, and formats
• Work with datasets: sort, subset, merge, and re-format datasets using Python
• Use Python scripts for basic statistical inference
• Export data and output to other computers and softwares
Modalités
Organization
Type | Amount of time | Comment | |
---|---|---|---|
Présentiel | |||
Cours interactif | 24,00 | ||
Travail personnel | |||
Charge de travail personnel indicative | 11,00 | ||
Group Project | 16,00 | ||
Autoformation | |||
E-Learning | 24,00 | ||
Overall student workload | 75,00 |
Evaluation
Details will be given in the first class.
Control type | Duration | Amount | Weighting |
---|---|---|---|
Contrôle continu | |||
Contrôle continu | 4,00 | 6 | 20,00 |
Autres | |||
Projet Collectif | 16,00 | 1 | 30,00 |
Rapport écrit | 1,00 | 1 | 30,00 |
Projet Individuel | 10,00 | 1 | 20,00 |
TOTAL | 100,00 |