Core Data Science using Python

Exploratory data analysis and interactive visualisation, unsupervised learning, 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

Preprocessing (scaling, log transformations, imputation, one hot coding)
Exploratory data analysis and interactive visualisation
Unsupervised learning (k-means clustering, DBSCAN)
Supervised learning (KNN, decision trees, random forests)
Model Evaluation and Tuning

Languages and libraries:

Python 3
Numpy and Pandas for data manipulation
Matplotlib for visualisation


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.

Get in touch with us to learn more about the course! 


Exploratory Data Analysis


Unsupervised Learning and Supervised Learning


Ensemble Methods

Case Studies

Learning  Outcomes:

Get familiar with what data science, machine learning, supervised/unsupervised learning are
Use Pandas to import, summarise, filter and modify datasets
Use Pandas for cleaning, sorting, joining and aggregating datasets
Make charts and interactive dashboards using Pandas, Matplotlib, Seaborn, Bokeh and Panel
Learn about the fundamentals of supervised learning and how to train and evaluate a model
Learn the theory behind decision trees and how to use them in practice
Understand what is unsupervised learning and how it differs from other machine learning techniques
Learn the theory behind the KMeans algorithm and how to use it in practice
Learn the theory behind DBSCAN and how to apply it in practice
Get familiar with what ensembles are the intuition behind them
Learn the theory behind bagging and the random forest algorithm as well as how to use it in practice
Learn the theory behind boosting, gradient boosting and how to use it in practice
Learn what stacking is and how to apply it on your models

KATE Projects:

  • FTSE Market Summary Report: Data analysis of an FTSE dataset using pandas
  • Customer Banking Complaints EDA: EDA of Customer Banking Complaints using pan- das and bokeh

  • Classification and Model Selection: Build a supervised learning model to predict the success of Kickstarter campaigns.

Get in Touch 

Interested in learning more?

If you’re interested in what the ‘Core Data Science Using Python’ could do for your team or department, please complete the form to the right of this text and we’ll get back to you within two working days with more information.

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