DataRoot University by DRL
Data Science Fundamentals
Learn the mathematics, Python and machine learning needed to build an ML application. Put the theory into practice with coding assignments, Kaggle problems and a final project.
- 14 weeks
- Online · Slack
- Free
Before you start
Basic programming and university-level mathematics. The opening modules refresh the Python and maths you’ll use throughout the course.
What you’ll build
A machine learning application with data preprocessing, model training, a prediction API and a Docker container.
Course outline
8 modulesMath overview
Refresh calculus, linear algebra and probability, then check your understanding in a module test.
Python
Work with data types, functions, decorators, modules and object-oriented programming. Build a Snake game to put Python into practice.
Python libraries for data science
Use NumPy, pandas, SciPy and Matplotlib to manipulate arrays, process tabular data and visualise results.
Supervised learning
Implement and compare regression, Naive Bayes, nearest neighbours, neural networks and support vector machines through coding assignments.
Unsupervised learning
Find structure in unlabelled data with K-means clustering and principal component analysis.
Practical machine learning
Prepare real datasets, use scikit-learn and XGBoost, and evaluate your models in Kaggle competitions.
REST APIs & containerisation
Build a REST API with Flask and package it with Docker so the application can run outside your development environment.
Final project
Choose an ML problem and dataset. Develop preprocessing and training, expose predictions through an API, and containerise the application.