Training offer
My training offer is dedicated to students, enthusiasts and professionals with any background and training.
The courses will focus mainly on the practical application of concepts and tools in business and professional contexts. My goal is not only to give knowledge, but to teach the profession of Data Scientist.
Machine Learning
The set of techniques, algorithms and methodologies that allow machines to learn.
The topics are:
- Feature selection
- Hyperparameters optimization
- Overview of supervised and unsupervised models
- Training, validation and testing of supervised models
- Dimensional reduction (PCA)
- Practical examples
These topics will be covered using Python code.
Python language
One of the most studied programming languages in the world, which has now become a must for anyone who wants to deal with machine learning and artificial intelligence.
The topics are:
- Syntax
- Variables, functions
- The most used libraries
- Elements of object-oriented programming
- Practical examples of use
R language
The programming language for statistical analysis, data visualization and training of forecast statistical models.
The topics are:
- Syntax
- Variables, functions
- The most used libraries
- Practical examples of use
Statistics and data analysis
The basic tools of the Data Scientist, essential for understanding the data you work with and the results of the models.
The topics are:
- Basic statistics (probability, distributions, histograms)
- Advanced statistics (correlation, hypothesis tests, bootstrap, simulations)
- Descriptive analysis (mean, variance, median, percentiles)
- Practical examples
E/R model and SQL language
Although we live in the era of Big Data, the relational data model is still an important tool for today’s Data Scientist.
The topics are:
- Entity-relationship data model
- Design and implementation of a relational database
- SQL language
- Writing queries for simple and complex analysis
- Practical examples