Introduction to Data Science – Python

Data science is an interdisciplinary field grounded in mathematics, statistics and computing aimed at extracting knowledge and insights from both structured and unstructured data. While this expanding domain allows organizations to address numerous business use cases, many technologies and solutions are currently available on the market and companies are struggling to navigate through this massive ecosystem.

Python, as the leading data science programming language opens countless possibilities to data scientists and mastering its wide-ranging capabilities is key. This course acts as a comprehensive introduction to this world and will help you harness Python’s full power through machine learning algorithms.

Trainers

Mélanie
Mélanie
Data Scientist
Mélanie
Data Scientist
Romain
Romain
Head of Research
Romain
Head of Research
Sébastien
Sébastien
Data Scientist
Sébastien
Data Scientist

Training plan

1 – Presentation of data science tools

Jupyter
Python
Numpy
Matplotlib
Pandas
Sklearn environment

2 – Description and implementation of common Machine Learning algorithms

Supervised learning:

Classification

Knn
Bayes
SVM
Decision trees
Ensemble classifiers

Regression

Linear
Nonlinear

Unsupervised learning:

Clustering

K-means
CHA
SVDD

3 – Discovering and application of the main notions of Machine Learning

Over and underfitting
Splitting datasets
Cross validation
The curse of dimensionality
Model selection
Unbalanced datasets
Regularization, etc.

4 – Summing-up exercise

5 – A few good practices in data science projects


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Training goals
  • Understanding the fundamental principles of data science
  • Discovering the biggest groups of algorithms
  • Applying these algorithms with Python libraries
Duration
4 days
Needed skills
  • Prior knowledge in mathematics
  • Prior knowledge in programming
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