Objectives of the trainingAt the end of this training, the participant will be able to value his data with Python.
Targeted audienceAny professional in a company (managers or non-managers) whose function requires the realization of data analysis, financial or not.
In this module the participant will become familiar with the Python environment
- What is Jupyter Notebook
- What are the basics of programming with Python :
- What are the variables in data science
- What data processing can be done: Indexing, extraction, replacement, modification, addition, conversion, cleaning, membership test, sorting, structures (sets, dictionaries, etc.), mathematical operators, comparison operators, logical operators, etc.
- How to use the debugger
- What are the rules for using if, if-else and if-elif conditions for flow control
- When to use while and for loops
- How to create your own functions (def syntax, inputs or parameters, function body and outputs: return)
- When and how to use lambda functions
- How to load or install libraries and modules or python packages
Segment 2: What libraries are essential for data science
In this module the participant will become familiar with the different libraries specific to data science, and their respective use.
How to conduct a process that will extract, transform and load data, from a raw data source, for business needs.
- Taking advantage of the Pandas library (Panel Data or Python Data Analysis):
- How to extract data from various sources (Excel, CSV, HTML, JSON, etc.) and manipulate it (clean, filter and transform)
- How to identify, remove and replace missing data
- How to deal with duplicate data
- How to manage data aggregations (groupby)
- Using the Numpy library to create or generate data (simulations). Introduction to Monte-Carlo simulation.
How to model data to conceptualize the relationships between different types of information, with the Pandas library:
- How to combine data tables (add and merge tables)
- How to transform data and how to create data tables
Once the data has been extracted and modeled, it remains to see how to visualize them in a graphic form (diagram, graph, map, animation...), more easily interpretable and exploitable.
- Visualize, combine and customize data: line graphs, scatterplots, boxplots, heat maps, etc.
- Save one or more graphs (pdf, jpeg, etc.)
Skill building with real-life examples of increasing complexity, use of the application, explanations, questions and discussion.