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Advance your data skills at

OXFORD DATA ACADEMY

a 5-day, transformative, career-boosting data science bootcamp in the heart of Oxford

Oxford Data Academy is designed to give you the skills to readily apply to your workflows and elevate your impact right after the bootcamp. You will learn the basics of data handling in Python (pandas, numpy), data visualisation (matplotlib, seaborn, plotly), statistical modelling, and machine learning (scikit-learn). On the final day, you will solidify your newly-acquired skills in a mini-hackathon, where you’ll be working in teams to solve real-world data challenges. Instead of theory, we focus on practical skills conveyed via a hands-on approach.

We will focus on data science in the programming language Python - beginner-friendly, general purpose language that is currently the best choice for data science and machine learning applications. During the first day, we will walk you through the basics of this language without assuming prior knowledge.

Programme of Oxford Data Academy

1st Day

• Welcome and opening
• Intro to Python programming (data types, strings handling, for loops, if else)
• Data importing, cleaning, filtering (Pandas library)
• Missing values and data aggregation (Pandas, Numpy libraries)

2nd Day

• Workshop: Fundamentals of data visualisation (Plotly, Seaborn libraries)
• Workshop: Interactive graphs (Plotly Express library)
• Presentation: Data communication: Do’s and Don'ts
• Workshop: Exploratory data analysis using visualisation
• Workshop: Geographical plotting

3rd Day

• Intro to Statistical Inference and Hypothesis testing
• Presentation: Causality vs Correlation 
• Workshop: Regression Modelling
• Presentation: Predictive performance measures
• Workshop: Logistic Regression
• Presentation: Statistics vs Machine Learning 
• Exercise: Exploratory data analysis

4th Day

• Intro to Machine Learning
• Workshop: Unsupervised Learning
• Dimensionality Reduction
• Clustering
• Workshop: Supervised Learning
• Classification, regression (linear regression, logistic regression, LASSO regression)
• Model inspection, feature importance, partial dependence
• Churn prediction, K-nearest neighbour, Decision tree, Random forest, Neural network
• Exercise: Determining the best predictive performance

5th Day

• Mini-hackathon: Real world data problems solved in teams
  • Selection from three topics: Biomedical, Healthcare, Economics

(The programme is subject to slight changes)

Cancellation and refund policy

The cancellation deadline is 20 days before Summer Data School begins. No refunds will be given for cancellations after midday (12:00) on this day. Please submit cancellations by emailing the organisers. Processing of refunds may take 4 – 6 weeks.

Attendance substitutes

After the cancellation deadline, you will not be eligible for a refund. However, we are happy to accept name changes. Please email us the substitute's first and last name, email and telephone number, along with your details to the organisers.

Failing to show up

If you fail to show up, no refunds will be given.