Data Science Bootcamp

During the Summer of 2019 I taught a bootcamp of 432 hours on Data Science at NEOLAND with the following contents:

1. Matematical foundations. Basic notions of Probability and Statistics. Elementary Linear Algebra. Multivariable Calculus.
2. Practical programming. Introduction to Python. Numpy. Pandas. Input/Output libraries. Introduction to R.
3. Data visualization. Matplotlib. Seaborn.
4. Data mining. Twitter API. Scrapping. Machine Learning. Scikit-Learn. Linear Regression. Logistic Regression. Naive Bayes. K Nearest Neighbours. Singular Value Decomposition and Principal Component Analysis. Support Vector Machines. K Means. Trees. Ensemble Learning. Natural Language Processing. Sentiment analysis. Deep Learning. Neural nets. TensorFlow. Keras.
5. Database systems. MySQL. SQLite3. MongoDB.
6. Final project.

Here are some useful references:

• Hands-On Machine Learning with Scikit-Learn and TensorFlow, by Aurélien Géron. Also check out the Machine Learning Notebooks associated to this fantastic book.
• Kaggle. The biggest Data Science platform. This is the best place to get datasets, take part in competitions and study online courses.
• William Chen's answer to "How can I become a data scientist?". In this Quora post, William Chen offers a great guide, full of references to online resources. Highly recommended reading.

Here is some material that I developed for the course (in Spanish):

• Introductory notes on Probability and Statistics.
• Notes on Multivariable Calculus.
• Exercises on Multivariable Calculus.
• Introductory notes on databases.
• Slides on relational and non-relational databases.
• SQL in Python Database mini-project.
• Notebook on SQL in Python (Jupyter File).
• Notebook on MongoDB in Python (Jupyter File).
• Introduction to Machine Learning notebook (Jupyter File).
• Notes about notation in modelization problems.
• Notes on Logistic Regression and classification problems.
• Notebook on Logistic Regression (Jupyter File).
• Notes on the Naive Bayes classifier.
• Notebook on the Naive Bayes classifier (Jupyter File).
• Notes on the K Nearest Neighbours algorithm.
• Notebook on the K Nearest Neighbours algorithm (Jupyter File).
• Notes on Support Vector Machines.
• Notebook on Support Vector Machines (Jupyter File).
• Notes on Ensemble Learning.
• Notebook on Ensemble Learning (Jupyter File).
• Notes on the K Means clustering algorithm.
• Notebook on the K Means clustering algorithm (Jupyter File).
• Notebook on Natural Language Processing and Sentiment Analysis (Jupyter File).