Low-Code Data Scientist: Low-Code AI Apps including LLMs and ChatGPT (online)


This course explores how to create artificial applications using low-code and large language models (LLMs), including ChatGPT. 

The course is designed for industry professionals and domain experts, who are not developers, who aspire to learn artificial intelligence (AI) applications.   

A unique feature of the course is the ability of creators of AI applications to collaborate with generative AI applications (such as GPT and chatGPT) through the OpenAI APIs and prompt engineering. 

The course will cover the entire pipeline for data science through low code tools primarily using the OpenAI platform, Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).

Dates, Times and Delivery

The taught course will run from 18 September - 4 October 2023. 

Tutorials will be delivered via Microsoft Teams on Mondays, Wednesdays and Fridays from 14:00 - 18:30 (UK time) with a 30-minute break during this time period.

Session Dates:

  • Monday 18 September
  • Wednesday 20 September
  • Friday 22 September
  • Monday 25 September
  • Wednesday 27 September
  • Friday 29 September
  • Monday 2 October
  • Wednesday 4 October

A world clock, and time zone converter can be found here: https://bit.ly/3bSPu6D

Programme details

Themes covered by the course include:  

  • Machine learning and deep learning concepts  

  • Building blocks of low-code ecosystems   

  • Using low-code and large language models (LLMs), including ChatGPT 

  • Implementing the machine learning pipeline in low-code systems    

  • Prompt engineering 

  • Design and development of AI applications in the three low-code platforms:  

    • AWS

    • Azure 

    • GCP (e.g., Microsoft Power platforms, Amazon SageMaker, and Google Vertex AI platform)  

  • Deploying low-code AI services in cloud-native MLOps environments   

  • Implementing generative technologies in low-code platforms such as chatGPT  

  • UX and integration for low-code   

  • Use of pre-built models and templates for creating AI applications  

  • Comparing and contrasting capabilities of low-code and full-code AI solutions   

  • Scaling low-code AI solutions  

  • Security and access for low-code applications  

  • Using tools like GitHub Copilot 

  • Using generative tools in the creative space like DALL-E 

  • Examples of low-code applications including: 

    • image recognition 

    • business-based applications (e.g., invoice processing)  

    • sentiment analysis 

    • time series 

    • regression analysis.  

  • Creating and managing datasets for low-code applications:  

    • labeling data 

    • dataset creation 

    • annotation, etc.  

  • Workflow and process automation for low-code applications  

  • Ethical AI and Responsible AI considerations for low-code and generative applications  

Themes covering the integration of low-code and generative AI applications include:   

  • Introduction to large language models   

  • Building AI applications using large language models  

  • Understanding OpenAI, GPT and chatGPT  

  • Introducing GPT-3 and the OpenAI API  

  • Strategies for designing prompts  

  • Understanding OpenAI models:  

    • Davinci 

    • Babbage 

    • Curie 

    • Ada   

  • Working with the OpenAI Playground.

The above may be subject to minor changes and adjustments


Participants who attend the full course will receive a University of Oxford electronic certificate of attendance. 

The certificate will show your name, the course title and the dates of the course you attended.

You will be required to attend all of the live sessions on the course in order to be considered for an attendance certificate.


Description Costs
Standard course fee £1395.00


Fees include electronic copies of course materials.

All courses are VAT exempt.


Ajit Jaokar

Course Director

Based in London, Ajit's work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). 

Ajit is the Course Director and/or tutor for Continuing Education’s portfolio of artificial intelligence courses for professionals:

Ajit is also a Visiting Fellow in the Department of Engineering Science here at the University of Oxford.

He also works as a Data Scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare.

Besides the University of Oxford, Ajit has also conducted AI courses in the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM) and as part of the The Future Society at the Harvard Kennedy School of Government.

He is also currently working on a book to teach AI using mathematical foundations at high school level. 

Ajit was listed in the top 30 influencers for IoT for 2017 along with Amazon, Bosch, Cisco, Forrester and Gartner by the German insurance company Munich Re.

Ajit publishes extensively on KDnuggets and Data Science Central.

He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), and 19th among the top 50 twitter IoT influencers (IoT Institute). 

His PhD research is based on AI and Affective Computing (how AI interprets emotion).

Ms Ayşe Mutlu

Course Director

Data Scientist

Ayşe Mutlu is a data scientist working on Azure AI and devops technologies. Based in London, Ayşe’s work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines).

She enjoys coding in Python and contributing to Open Source Initiatives in Python.


Please use the 'Book' or 'Apply' button on this page. Alternatively, please contact us to obtain an application form.

Level and demands

For this course, no prior coding experience is needed but some technical industry background (non coding) or domain knowledge in a tech field is advisable. Our aim is to keep the course inclusive for people who do not have coding/low-code experience. However, if you are still unsure about the suitability of this course, please email us and we will answer any questions you have. We are also happy to arrange to speak to you.

IT requirements

This course is delivered online using Microsoft Teams. You will be required to follow and implement the instructions we send you to fully access Microsoft Teams on the University of Oxford's secure IT network.

This course is delivered online; to participate you will need regular access to the Internet and a computer meeting our recommended Minimum computer specification.

It is advised to use headphones with working speakers and microphone.