Use Python to analyze, model, and visualize data effectively
Data is the lifeblood of any business. But you need to know how to make sense of it so that it brings value to the organization.
This training on data science with Python will equip you to produce automated methods for sorting and analyzing data in order to extract useful information.
You'll be able to get the most out of your data, and communicate it to influence decision making.
You will also be able to develop your skills in computer science, mathematics and management.
Is it for you ?
Any professional in a company (managers or non-managers) whose function requires the realization of data analysis, financial or not.
Prerequisites
None
What You'll Walk Away With
- ✓ Master Python fundamentals and Jupyter environment for efficient data handling
- ✓ Execute a full ETL process to extract, clean, and transform data from multiple sources
- ✓ Use key libraries (Pandas, NumPy) to analyze, aggregate, and simulate data
- ✓ Model data and perform predictive analysis with Statsmodels and Scipy
- ✓ Create clear, actionable visualizations with Matplotlib and Seaborn to support decisions
Training content
The more data you have, the more reliable information you can derive. This information can be used to anticipate asset or user/customer behavior and gain a competitive advantage.
1 What is Python, Jupyter Notebook and Anaconda
- 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
2 What libraries are essential for data science
3 ETL (extract/transform/load) process
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.
4 Modeling
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
- How to optimize and forecast data with the Statsmodels.api and Scpipy.stats libraries.
5 Visualization
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.
How to use the Matplotlib and Seaborn libraries to :
- Visualize, combine and customize data: line graphs, scatterplots, boxplots, heat maps, etc.
- Save one or more graphs (pdf, jpeg, etc.)
📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
