Introduction to Data Science using Python
Exploratory data analysis and interactive visualisation, unsupervised learning, dimensionality reduction and feature extraction, supervised learning and more.
What you will learn
The course is extremely interactive and hands-on. You will learn by working through concrete problems with a real dataset. You will be taught by academic and industry experts in the field, who have a wealth of experience and knowledge to share
Languages and libraries :
Elementary Python programming and use of the command line. You can acquire these skills at our Python bootcamp.
Basic probability and linear algebra.
Individuals who want to master new technical skills and learn the latest techniques and industry best practices to work effectively with Data Science teams.
DATA SCIENCE ESSENTIALS
Introduction to Data Science
- Overview of Data Science and Machine Learning
- Supervised vs. Unsupervised Learning
- Working with the Jupyter notebook
- The Numpy library for array manipulation
Working with real-world data
- The Pandas library for data manipulation
- Data cleaning and pre-processing
- Data visualisation with Matplotlib and Seaborn
Principal Component Analysis (PCA)
- What is PCA and why you need it
- Applying PCA in Python with SKLearn
UNSUPERVISED LEARNING AND SUPERVISED LEARNING
- The scikit-learn library for Machine Learning and scikit-learn pipelines
- k-means clustering
- Hierarchical cluster analysis
- Density-based clustering (DBScan)
- The k-Nearest Neighbour algorithm
- Overfitting, underfitting, bias-variance tradeoff
- Cross-Validation and hyperparameter tuning
Get in Touch
We will email you within the next 24 hours to arrange a quick call to help with any questions about the programme and recommend pre-course materials.