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Mohammad Adnan

Mohammad Adnan

Computer Science Graduate

Talent
Northampton, NorthamptonshireMember since April 27, 2023
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About

A recent Computer Science graduate who has achieved First Class Honors from Coventry University.

I have a keen interest in data and machine learning and hope to utilize my knowledge in a business setting.

Some of my key skills include:

  • Experience with Microsoft Office applications such as Excel.
  • Building interactive dashboards using Microsoft Excel.
  • Querying data using SQL.
  • Deriving meaningful insights using data analysis techniques.
  • Building predictive machine learning models using Python libraries.

Experience

CHRISTMAS CASUAL

Royal Mail

December 2022 – January 2023

  • Performed quality control checks to ensure there were no damages to the incoming parcels.
  • Distributing parcels to the correct bays for dispatching after sorting them into appropriate categories by utilising sorting machines or similar administrative technologies.
  • Supporting fellow colleagues during peak work times when additional capacity is required in a fast-paced environment.
ASSISTANT LIBRARIAN

Coventry University

October 2021 – January 2022

  • Guided students and members of the public community who were seeing information by directing them to specific library databases and other reference materials.
  • Liaising with lecturers and students and helping them reserve private rooms for teaching.
  • Provided computer literacy training sessions to community members and students and also help develop tutorials to aid individuals access technology.

Education

BACHELOR OF SCIENCE IN COMPUTER SCIENCE

Coventry University

September 2018 – July 2022

  • Achieved First Class Honours.
  • Relevant modules: Database Systems, Machine Learning and Related Applications, Data Science, Big Data Programming Project, Theory of Computation, Introduction to Artificial Intelligence.
  • Final year Computer Science Dissertation: Comparing the differences between supervised and semi-supervised learning algorithms in the detection of strokes by building predictive models.

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