Summary

Overview

Work History

Education

Skills

Accomplishments

Laboratory Skills

Other Qualifications

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Timeline

MICHAEL WONG

Manchester

Summary

An aspiring data scientist with a strong background in statistical analysis, machine learning, and data visualization, I bring a unique blend of technical expertise and a passion for uncovering insights from complex datasets. My journey in data science began during my undergraduate studies at Blackett Laboratory, Imperial College London, where I developed a deep interest in leveraging data to test hypotheses and drive innovative solutions. Throughout my academic career, I have honed my skills in Python, SQL, and various data science libraries such as NumPy, pandas, scikit-learn, Matplotlib, and Seaborn. I have successfully analysed and visualised data, developed predictive models, and automated data analysis processes. For example, in one of my projects, I created a predictive model that improved forecast accuracy by 15%, demonstrating my ability to deliver tangible results. I am adept at communicating complex technical concepts clearly and effectively, a skill that has been critical in presenting my research findings to both academic and professional audiences.

Overview

years of professional experience

Work History

Law firm internship at R&A solicitors

02.2024 - 04.2024

Utilized Python to manage and analyse large datasets containing client information, improving data accessibility for legal teams
Conducted data cleaning and data wrangling using pandas and NumPy to ensure the accuracy and consistency of client records
Developed interactive data visualizations with Matplotlib and Seaborn to provide insights into client demographics and case outcomes, aiding lawyers in strategic decision-making
Collaborated with legal teams to understand data requirements and provide tailored data solutions, demonstrating strong communication and collaboration skills

Dissertation Project, University of Edinburgh

07.2023 - 10.2023

Improved manipulation of differential equations using a SQL database
Reduced numerical algorithm processing time, by optimising the form of differential equations
Discovered quasi-normal modes using Statically modelled hydrodynamics systems
Communicated findings using detail visualisation of differential equations
Improved findings of quasi-normal modes using pre-processing metrics on python and mathematica

Team based problem solving - kaon decay verification , Imperial College

09.2021 - 01.2022

Used data analytical techniques to verify a statistical probability of the existence of kaon decay using excel, python, and machine learning techniques, which was used during the period
Use of K- clustering and supervised learning was used to recognise and filter out noise signals from other particles
Excel, python, SQL used to analysis data
Improved filtering accuracy by 15% by use of self taught of machine learning techniques (K- clustering and supervised learning)
Reduced machine learning training time by optimising bias variance trade off by creating metrics to have efficient training (keras, tensorflow)
Demonstrated strong problem-solving skills by identifying and resolving complex technical issues
Increased team's output by organising smaller groups based on each member's speciality by a questionnaire
Made statistical conclusions from data analysis and hypothesis testing
Communicated findings to a cohort of scientist via a 30 minute presentation
Enhanced presentation of findings by creating visualisations of data and statistical testing

Assistant teacher and data manager

04.2017 - 05.2017

Improved children proficiency, in Rolls crescent primary school in computer science, maths, and general science
Reduced costs for trips by making advisable financial spending based on data on excel

Carer in a retirement home

01.2018 - 10.2018

Developed and maintained positive relationships with residents, fostering a supportive and compassionate environment
Provided high-quality care and support to residents, ensuring their physical, emotional, and social needs were met
Communicated effectively with residents' families, updating them on their loved ones' conditions and addressing any concerns
Monitored patient health, behavioural and physical changes, promptly reporting concerns to ward supervisor

Education

Master of Theoretical Physics - Theoretical Physics

University of Edinburgh

11.2023

Bachelor of Theoretical Physics - Theoretical Physics

Imperial college London

06.2022

Imperial College London