Imperial data science online winter school

data science winter school

Engage with Imperial academics!
23 January - 5 February 2024

Data Science is successfully adding value to all the business models by using statistics and deep learning to make better decisions. A growing number of companies are now hiring data scientists to crunch data and predict possible situations and risk for businesses.

This winter school is designed for undergraduate students studying IT, computing or any engineering degrees at a well-recognised university, with an interest in data science. Students will be introduced to the concept, develop an understanding of data science, hear from industry expert on data science applications and work in teams towards a technical project.

Students will:

  • Learn the concept of Data Science;
  • Develop an understanding of data analysis, AI, machine learning for data science, exploratory data analysis and visualization;
  • Learn about data science products;
  • Understand the real-world applications in data science and hear from industry expert;
  • Get an insight into advances in data science;
  • Gain an understanding of data privacy and ethics;
  • Learn from research experts in data economy and block chain;
  • Develop valuable professional skills in team building, communication and presentation;
  • Experience team-based learning through a technical data science project;
  • Practice and improve their English language.

Students will be working in project teams to create a technical demonstration and to engage with Imperial supervisors throughout the programme.  Previous projects include developing a brain tumour detector device.

In addition, students will have an opportunity to meet and discuss with Imperial ambassadors online, sharing their experiences on what it is like to study in a world class university and to discuss opportunities for future study.

For registration, please contact:

A scholarship of up to £600 will be provided by Global University Online. To apply, please visit:
http://www.globaluniversityonline.org/hqdx.php/scholarship/index/13704.html?lang=en

More information

Programme structures & format

59 contact hours spread over 2 weeks covering lectures, workshops, tutorials, project work and self-study time. Classes will be delivered on weekdays.

Students will be allocated in small groups for Project work which will be done through team-based learning with supervision. Final project will be presented in groups to a panel of experts on the last day of the programme. A prize will be awarded to the team presented with the best project.

The entire programme will be taught in English.

Teaching faculty

The summer school is  directed by Dr Kai Sun and Huang Ping and taught by a multi-disciplinary teaching faculty from the Data Science Institute and other departments of Imperial College London.

Certification

Students will receive an Imperial College London digital certificate on successful completion of the winter school and a prize will be awarded to the best project team. Each student will also receive a transcript for their project mark.

Entry requirements

All students are expected to be studying an undergraduate degree in any engineering discipline, IT or computing degree.

English requirements:

All students are required to have a good command of English, and if it is not their first language, they will need to satisfy the College requirement as follows:

  • a minimum score of IELTS (Academic Test) 6.5 overall (with no less than 6.0 in any element) or equivalent.
  • TOEFL (iBT) 92 overall (minimum 20 in all elements)
  • CET- 4 (China) minimum score of 550
  • CET- 6 (China) minimum score of 520
Technical knowledge requirements:

As the project has a strong technical element, students are expected to have the following technical knowledge and interest:

  • Interested in computer visualisation / natural language processing;
  • Have at least intermediate level at one of the common programming language (Python, Java, C ++, etc.);
  • Have mathematical foundation (probability theory, linear algebra, etc.);
  • Have understanding of the Linux environment;
  • Knowledge of Machine Learning knowledge with experience in using PyTorch / Tensorflow / Keras.

Students will need to have access to a computer pre-installed with python, have a webcam, microphone and good internet connection to attend the live classes.