A career-boosting
data science bootcamp

Reuben College, Oxford — 24th - 28th June 2024

TARGET AUDIENCE

1

DPhil students who need to handle data, use statistics or build models in their research.

2

Analysts who want to swing away from Excel and start working with big data, applying machine learning and create eloquent visualisations.

3

Professionals wanting to learn more about machine learning and statistics.

FORMAT

  • Intense 5-days of coding and thinking about data science problems
  • Hands-on workshops with real-world datasets and data challenges
  • Interactive lectures on important data science concepts
  • Group projects with individual support by our expert team
  • A combination of theoretical and practical workshops
  • A data-hackathon on the 5th day

GOALS

  • Educate about data science
  • Provide the necessary skills to handle data
  • Raise awareness of strengths and weaknesses of machine learning models
  • Cover the important topics often overlooked by online courses
  • Advance the research and performance of the attendees by supporting their journey towards data literacy
  • Talk about problems that seniors encountered in their workflows
  • Teach how to avoid "junior" mistakes in interpretation of analytical results

PROGRAM

  • Day 1: Welcome and Opening. Intro to python and data processing
  • Day 2: Data Visualisation
  • Day 3: Statistics and regression modelling
  • Day 4: Machine Learning
  • Day 5: Data-hackathon
    • 3 data challenges to choose from
    • Solidifying concepts and skills acquired during first 4 days in practical challenges in small teams

DETAILED PROGRAM

1st DAY 2nd DAY 3rd DAY 4th DAY 5th 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)
  • 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
  • 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
  • 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
  • Mini-hackathon: Real world data problems solved in teams
    • Selection from three topics: Biomedical, Healthcare, Economics

MORE INFORMATION

FAQs

A course day starts at 9:00 and ends at 18:00 with breaks for coffee and lunch. Oxford Data Academy will run from 21st to 25th June 2024 at Reuben College in the centre of Oxford.

No programming experience is required to attend. However, we highly encourage checking out the recommended topics to familiarise yourself with before the bootcamp. We will send materials approximately a week before the bootcamp starts.

The bootcamp lasts for 5 days, with the course day starting at 9:00 and ending at 18:00. Participants are encouraged to spend about 10 hours prior to the course reading about the topics we cover - this will help them to understand the concepts faster and get more out of the bootcamp.

Teaching in our Data Schools is structured into workshops because you learn data science and programming by doing. Every topic we teach is accompanied by 2 versions of Jupyter notebooks - one full version with the code, code annotations and theory and the second one contains the theory and code annotations, but not the code. Participants use the empty notebooks to write the code in real time. Materials for each Day are distributed to participants one day prior to the workshops.

Participants can reach us all the time during the course as well as after the bootcamp is finished. During the bootcamp, one-on-one mentorship is available for ouŕ participants as well as any type of technical assistance. We also encourage participants to contact us with any questions via our emails, phones or LinkedIn.

At Data Science Academy bootcamps, we teach programming in Python. To facilitate learning, we use an interactive IDE called Jupyter Notebook, which allows for easy annotation and presentation of code. We always send out a comprehensive guide on what to install and how to do so before the course begins.

Required knowledge

Basic quantitative skills
No programming skills needed

Familiarity with computer programming or database structures is a benefit, but not a requirement. Oxford Data Academy is set up in a way that beginners will learn the basics and do some hands-on experimentation with guidance, while participants with some experience will be able to see best practices, utilise their knowledge and do some additional magic on really cool datasets while consulting with experienced mentors!

PYTHON

Widely considered as one of the best programming languages for beginners, Python is a 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 and how to use it to solve data science tasks.

PRICE: £1000

Refunds and cancellations

CANCELLATIONS FOR OXFORD DATA ACADEMY

The cancellation deadline is 20 days before Oxford Data Academy begins. No refunds will be given for cancellations after midday (12:00) on this day. Please submit cancellations by emailing jakub@dsacademy.sk. Processing of refunds may take 4 - 6 weeks.

ATTENDANCE SUBSTITUTES

After the cancellation deadline, you are responsible for the full payment. However, we are happy to accept name changes. Please email the substitute's first and last name, email and telephone number. Additionally, please include your name in the email. After sending this email, you will still be responsible for the payment of the entry fee.

FAILING TO SHOW UP

If you fail to show up, you must pay for Exord Data Academy in full, NO REFUNDS will be given.

TEAM

Imrich Berta

Applied mathematics graduate from University of Cambridge, experienced in machine learning models for disease prediction. Currently works as a consultant for government on cancer epidemiology and public health. Actively mentors analysts and organizes coding workshops for students.

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Laura Johanesová

Laura is a bioinformatician and biomedical scientist currently at the University of Vienna. The skills she has in R, Linux and Python are crucial for her research in regeneration and she also developed interest in biotechnology and medicine, which helped her team win the first place in a biotech incubator.

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Jakub Hantabal

Jakub is a biomedical data scientist studying at Oxford and specializing in oncology collaborating with British and Slovak institutions. Jakub also consults clients in life science on business and technology development, and is passionate about education of future data scientists.

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Ján Dudek

Magna cum laude economics and econometrics graduate from Rice and Oxford. Improved the risk-equalization model at the Ministry of Health and implemented ML fraud detection algorithms in Slovak healthcare. Currently a senior data scientist specializing in the health insurance industry.

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PARTNERS

COMMUNITY PARTNERS