Applied Data Science: Introduction

Overview

Data science is a new emerging multidisciplinary domain that is focused on applying Data for understanding and drawing specific conclusions in many sectors such as business, medical, financial, manufacturing, etc. The key skills required in this field  are the ability to hypothesise, improving business outcomes, based on initial analysis with continued proof points, through continuously testing those base conclusions and the iterative improvements to ensure the conclusions reached continue to be correct and true.

The Applied Data Science (ADS) course aims to introduce students to a blend of Data Science concepts and technologies in order to enable them to work with everyday Data related issues analytically. Students will gain experience of the entire Data Science supply chain, namely Data collection, processing, analysis and visualisation, using an innovative hands-on practical Data Science Group project.

This course could be taken after the companion course 'An Overview of Data Science'.

Programme details

Course begins: 2 Oct 2024

Week 1: Introduction to Applied Data Science

Week 2: Data Science Programming Languages 

Week 3: Applied Data Science Using Python

Week 4: Role of Statistics in Data Science    

Week 5: Principles of AI & ML

Week 6: Data Analytics using Python & SciKit

Week 7: Usability in Data Science Projects

Week 8: Group Project

Week 9: Group Project

Week 10: Group Project

Certification

To complete the course and receive a certificate, you will be required to attend and participate in at least 80% of the live sessions on the course and pass your final assignment. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so.

Digital credentials

All students who pass their final assignment, whether registered for credit or not, will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that assignments are not graded but are marked either pass or fail. 

Fees

Description Costs
Course Fee £310.00
Take this course for CATS points £30.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutors

Dr Sepi Chakaveh

Dr Sepi Chakaveh is Senior Associate Tutor (Data Science) at the Oxford University Department for Continuing Education. She has a degree in Electronic Engineering and a PhD in Experimental Astrophysics & Space Sciences. Sepi has been teaching and researching Dynamic Objects and Data convergence. She is co-founder and former director of the Southampton Data Science Academy. Sepi is the winner of Everywoman Innovator Award 2020.

Dr Selvakumar Ramachandran

Teaching Assistant

Selvakumar (Selva) Ramachandran is Technologist and Entrepreneur developing 360-degree VR-based tourism to VR-travellers and virtual tourism. Selva has a Bachelor Degree of Electrical and Electronics Engineering form Madurai Kamaraj University, India and a MSc – Software Engineering from Blekinge Tekniska Hogskola, Sweden and a PhD in Information Science from University of Rome, Italy. Dr Selva Ramachandran is the winner of several UK industrial awards as well as the recipient of Google Scholarship for showing excellence in the field of Computer Science – 2012.

Course aims

This course will introduce the learner to Applied Data Science, focusing more on the hands-on techniques and methods for data analytics purposes.

Course Objectives:

  • Data Analytics using Python.
  • Machine Learning & AI.
  • Data Science Group Project.

Teaching methods

The course will include a mix of lectures, and hands-on exercises and invited talks from expert data science practitioners.

Learning outcomes

By the end of this course, students will be expected to:

  • explain how data is collected, managed and stored for data science;
  • demonstrate an understanding of statistics and machine learning concepts that are vital for data science;
  • implement Python code to statistically analyse a dataset to be used in the group project.

Assessment methods

The students will need to complete a project which include understanding & applying all the above knowledge & techniques and submit a report of around 2000 words to reflect this.

Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work the required standard.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form - Declaration of Authorship form

 

Application

We will close for enrolments 14 days prior to the start date to allow us to complete the course set up. We will email you at that time (14 days before the course begins) with further information and joining instructions. As always, students will want to check spam and junk folders during this period to ensure that these emails are received.

To earn credit (CATS points) for your course you will need to register and pay an additional £30 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.

Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an Enrolment Form (Word) or Enrolment Form (Pdf)

Level and demands

The students should have some previous exposure to Data Science, as provided, e.g., by the Department’s “Overview of Data Science” course.

The Department's Weekly Classes are taught at FHEQ Level 4, i.e. first year undergraduate level, and you will be expected to engage in a significant amount of private study in preparation for the classes. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class.

Credit Accumulation and Transfer Scheme (CATS)

To earn credit (CATS points) you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. Students who register for CATS points will receive a Record of CATS points on successful completion of their course assessment.

Students who do not register for CATS points during the enrolment process can either register for CATS points prior to the start of their course or retrospectively from the January 1st after the current full academic year has been completed. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.