Content／学習内容

• Oxford University Data Science Course (Hybrid-type) 2026 Summer School
  
  ◎AM Lessons：International English Lessons Onsite with the students from Europe, Asia, South America, Mid-East
  
  ◎PM Lessons：1to1 Lessons Online（Hybrid-type）
  
  ★For whom：18+ years old
  
  ★Apply：https://internationalcommunicationscouncil-japan.com/index.php
  https://icc-edu.com/entryform/form.php
  
  Dates
  ◎　Schedule A：10 August (Sun) 2026 - 23 August (Sun) 2025 (2 weeks)
  Course code：DS0908262WOX
  ◎　Schedule B：09 August (Sun) 2026 - 30 August (Sun) 2026 (3 weeks)
  Course code：DS0908253WOX

  リンク：

Staff／スタッフ

• 
  • Coordinator

  Masumi Akaishi

  ICC International Communications Council

  Programme Director
  Career

  Japanese Embassy in Washington D.C, USA

  University of Oxford Lecturer

  University of Cambridge Lecturer

  University of London SOAS Lecturer

  University of British Columbia Lecturer

  ICC International Communications Council Programme Director

   

  

   

ICC International Communications Council supports the “Tobitate! Study Abroad Initiative” launched by the Ministry of Education, Culture, Sports, Science and Technology (MEXT)-Japan

Oxford University Data Science Course (Hybrid-type) 2026 Summer School

★ Decision-Making Using Data

1. Introduction to Data Science and the Data Science Pipeline
2. Data Cleaning and Exploration
3. Probability and Inferential Thinking
4. Intro to Machine Learning
5. Storytelling with Data

Application:

ICC Japan  https://internationalcommunicationscouncil-japan.com/index.php

ICC | 応募フォーム   https://icc-edu.com/entryform/form.php

 

The following universities in Japan have sent their students for Data Science Course of University of Oxford.

Hokkaido area	・University of Hokkaido
Tohoku area	・University of Iwate	・University of Tohoku
Kanto area	・University of Tshukuba	・Keio Gijuku University	・Tokyo University of Science	・Ochanomizu Women’sUniversity	・University of Saitama
	・Tokyo Institute of Technology	・Meiji University	・Toyo University	・Nihon University	・Chuo University
	・University of Saitama	・Waseda University	・Aoyama Gakuin University
Chubu area	・University of Kanazawa	・University of Nagoya	・University of Gifu	・University of Shiga	・Meijo University
	・University of Shizuoka
Kansai area	・University of Osaka	・Osaka Institute of Technology	・Osaka Metropolitan University	・Kobe City University of Foreign Studies	・Kinki University
	・University of Kobe	・Kansai University
Chugoku area	・University of Hiroshima	・University of Okayama
Shikoku area
Kyushu・Okinawa area	・University of Fukuoka	・Ritsumeikan Asia Pacific University		・University of the Ryukyus
Student Feedback for Oxford Online Tuition　☜Click here！

Competency／コンピテンシー

• Literacy
• Logical thinking ability
• Problem finding ability
• Problem solving skill
• Creativity
• Influencing skill
• Practical ability
• Situation grasping ability
• Communication ability
• Management ability
• Group skill
• Leadership ability
• International character
• Autonomy
• Flexibility
• Ethics
• Language proficiency

Application:

ICC Japan  ICC | 応募フォーム

https://internationalcommunicationscouncil-japan.com/index.php

Dates:

Schedule A: 9 August 2026 – 23 August 2026 (2 weeks)

Schedule B: 9 August 2026 – 30 August 2026 (3 weeks)

Tuition fee:

Schedule A: 4,670 GBP(Round-trip air fare is not included.)

Schedule B: 5,980 GBP(Round-trip air fare is not included.)

Orientation on 10 August 2026

8：30－Oxford International College 36-37 Pembroke Street, Oxford

1：45－Weston Lecture Theatre (front of Admissions Office) University of Oxford Bodleian Library, Broad Street, Oxford

Oxford University Data Science Course Summer School (Hybrid-type)

AM Lessons:

★　International English lessons with the students from Europe, South America, Asia, and Middle-East.

★　The classes are divided according to the students’ level of English.

Refer to the following Scale of English.

English Proficiency Conversion（マウスをあてて、クリックしてください。）

★　15 to 18 students per class

Students’ nationalities：

PM Lessons:

★　One to one Online lessons (Hybrid-type) by Oxbridge tutor’s PBL(Project-based learning) tutorial lessons

★　More than 400 qualified tutors are officially registered .

The tutors have a degree from University of Oxford, University of Cambridge, or Imperial College London.

The following is an example of Outstanding Team of Tutors.

1. Tutor Tereza	2. Tutor Elias	3. Tutor Rhei	4. Tutor Oliver
5. Tutor James	6. Tutor Jamie	7. Tutor Kate	8. Tutor Owen

Students would learn about commonly used practices by data science professionals. Below is an outline of what students could expect to cover during each week. As this is a highly condensed course, students may also be asked to do extra reading outside of their tutorial sessions in order to practise what they have learnt, and to gain a firm understanding of key concepts.

What does a data scientist do?

Typical workflow patterns

Tech and software used

Approach to business problems

What is machine learning?

What is a model?

Supervised vs Unsupervised ML

Regression vs Classification

What the typical workflow of a data scientist is, the software and technologies used, including common approaches to business problems. 

What machine learning is and an intuitive understanding of what “a model” represents. This will follow with examples of supervised and unsupervised learning, and understanding the differences and use cases for regression and classification models. 

Variables:

independent, dependent

continuous vs categorical

Common statistics:

mean, median, mode (when to use which?)

variance, standard deviation

covariance, correlation

What variables are; the difference between independent and dependent variables; continuous, categorical and ordinal variables. 

An intuitive understanding of common statistics, such as mean, median, mode (and when to use which), variance, standard deviation, covariance and correlation. 

Command line interface (intro)

Github (intro)

Jupyter notebooks (intro and orientation)

Python

Datatypes: int, float, dict, list, str, Bool, tuple, set

Loops (for, while, list comprehensions) and if/elif/else statements

An introduction to the common tech stack used by data scientists, including an introduction to the command line interface, Github version control, and Jupyter notebooks. 

An introduction to Python, including the datatypes available (eg int, float, dict, list, str, Booleans, tuples and sets), use of conditional statements (if, elif, else) and iterating using loops (for, while, and list comprehensions). 

Information／その他の情報

Contact／お問合せ先

ICC Japan

iccjapan@internationalcommunicationscouncil-japan.com

2-1-15, Nishiai, Ibaraki-city, Osaka, 567-0008  Japan

ICC Asia

japanoffice@internationalcommunicationscouncil.com

16-1, Aoba-dai, Kita-ku, Kobe-city, Hyogo-prefecture, 651-1231 Japan

Related Contents／関連コンテンツ

Contents

【Lecture Introduction】 Lectures in English and Japanese at National Taiwan University

• Information
• Link

Free

On Air

Department of Civil Engineering, National Taiwan University

• Information
• Link

Free

On Air

Master Talk: Meet Marketing Strategy and Branding Master - Jessie Chan

• Information
• Link

Free

On Air

🌟Lab Spotlight🌟｜Unboxing the Wonders of "The New World of Science"! —— NTU NEMS Research Center

• Information
• Link

Free

On Air