tutorial

Awesome tutorials for beginners, intermediates and experts in data science :) !

A. Getting started with Python programming

The basics as a good starting point !

1. Hello world
2. Data types
3. Data structures
4. Strings formatting and operations
5. Numerical operations
6. Logical operations
7. Conditions
8. Loops
9. Functions

Exercices :

B. Becoming expert with Python programming

More advanced tutorials ...

1. Modules and packages()
2. Lambda functions
3. Comprehension lists
4. POO
5. The .apply() method from Pandas()
6. Debug your code

C. Data science tutorials

1. Data wrangling

• Getting started with Pandas - Part 1
• Getting started with Pandas - Part 2
• Exercice - Data exploration on Spotify playlist

2. Web scraping

• Getting started with Beautiful Soup - Part 1
• Getting started with Beautiful Soup - Part 2
• Atelier Dev IA - Ville de Tours

3. Graphics & Dataviz

• Getting started with Seaborn - Part 1
• [Getting started with Seaborn - Part 2]

4. Cartography :

• Cartography with Leaflet & Folium - Part 1
• [Cartography with Leaflet & Folium - Part 2]

5. Text mining & NLP :

• Text mining & NLP - Part 1
• Text mining & NLP - Part 2 : Sentiment Analysis with VADER (french module)

D. Data science use cases

Data Analysis

1. Data analysis on diamonds dataset
2. [Data analysis on FAO STAT data[FR]]

Machine Learning

1. Supervised ML - Classification of Iris
2. Supervised ML - Classification of Penguins - FR
3. Supervised ML - Classification of Airline Passenger Satisfaction - Sentiment analysis

E. Machine learning tutorials and exercices

1. Data Preprocessing for machine learning && Exercice - Preprocessing for machine learning
2. Pipeline for machine learning && Exercice - Pipeline for machine learning
3. Cross Validation & Grid Search for machine learning
4. [Hyper-parameters tuning]
5. Metrics for classification - Part 1 2-class && [Metrics for classification - Part 2 n-class]
6. [Metrics for regression]
7. [Feature importance]
8. Supervised learning

8.1 Models for Regression :

• Simple & Multiple Linear Regression
• [Ridge, Lasso & Elastic-Net Regression]
• KNN regression

8.2 Models for Classification :

9. Unsupervised learning :

• Clustering Part 1 - K-means on Iris dataset
• Clustering Part 1 - K-means and PCA on simulated data
• Clustering Part 2 - Agglomerative Clustering on Fromages dataset
• Clustering Part 3 - Clustering on USA Arrests dataset
• PCA Part 1 - USA Arrests dataset

10. [Comparison of ML models]

F. Exercices

G. Teaching - Evaluation