Angela Afeeva
M:- | L: www.linkedin.com/in/angela-afeeva | E:-| A: London, SE2
Dynamic Data and Technology Professional with a strong foundation in Python, SQL, and data science. Holding an MSc in
Computer Science and a BSc in Mathematics, I have hands-on experience in data analysis, visualisation, and machine
learning. Skilled at leveraging state-of-the-art algorithms and libraries to solve complex problems, I have experience
managing teams and setting up optimized database systems. Eager to contribute to innovative projects within a
collaborative team
Professional Qualifications
AgilePM Foundation
Education
MSc Computer Science
University of Greenwich
04/2023
University of Greenwich
05/2021
Pass
BSc Mathematics
BSc Hons 2:1
Skills
Programming/Computer Skills
• Python
• T-SQL and MySQL
• Power BI
• Google Big Query
• HTML
• R
• CRM
• Microsoft Office: Excel, Word,
PowerPoint, Outlook
Technical Skills
• Machine Learning
• Data Science
• Data analysis
• Data visualisation
• Data exploration
• Statistics
• Deep Learning Architectures
(Transformers, CNNs, RNNs,
Attention Mechanisms)
• Libraries and Frameworks
(NLTK, SpaCy, TensorFlow,
PyTorch, Scikit-Learn)
Soft Skills
• Teamwork
• Eagerness to learn new things
• Problem-solving
• Presenting
• Communication
• High levels of autonomy and
ownership
• Resourcefulness
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Time management
Experience
Database Administrator
Key Responsibilities
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08/2024 - Present
Support Group wide business requirements by creating suitable database solutions.
Maintain and create the SQL schema (stored procedures, functions etc.).
Help to build, develop and maintain data warehouses.
Maintain own technical competence with in-depth knowledge sufficient to fulfil the role.
Database Administrator
Key Responsibilities
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Lloyd and Whyte
Professional Development Group
10/2021 - 08/2024
Conducted data analysis using Python, MySQL, Microsoft Access, Google BigQuery, and Power BI for management
reports and informed decision-making.
Managed remote data-building team operations, ensuring efficient workflow and timely task delivery.
Administered the CRM system, ensuring GDPR compliance and timely data imports.
Troubleshot and resolved technical issues for key systems, collaborating with support teams.
Collaborated with Delegate Acquisition and Production teams to ensure commercial success.
Managed data projects using methodologies like Agile.
Sourced, set up, and thoroughly tested new databases and data handling systems.
Achievements
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Automated processes with Python, reducing processing time by 75%.
Demonstrated strong leadership and time management skills as the sole member of the data team for nearly a
year, handling diverse tasks independently and driving projects forward proactively.
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Efficiently managed data scraping projects, reducing reliance on external data suppliers and achieving significant
cost savings by decreasing cost per record from £1.50 to £0.31.
Educational Experience
MSc Computer Science
University of Greenwich
04/2023
Relevant Modules
Machine Learning Dissertation
Achievements
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Developed a computer science algorithm utilising deep learning architectures (Transformers, CNNs, RNNs) and
machine learning libraries (NLTK, SpaCy, TensorFlow, PyTorch, Scikit-Learn).
Analysed logos for adherence to the golden ratio principle.
Distinguished logos with and without the golden ratio with a 80% success rate.
Demonstrated proficiency in computer vision, image analysis, and machine learning methodologies.
Programming Fundamentals for Data Science
Achievements
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Analysed Iris dataset using Python’s pandas library to calculate mean, median, standard deviation, and range for
petal widths across different species.
Visualised statistical data using Seaborn and Matplotlib to create boxplots and analyse key metrics for species
differentiation.
Created parallel coordinate plots to compare Sepal and Petal attributes, identifying Petal length as the best
feature for distinguishing Iris species.
Utilised R programming for data wrangling and analysis, including:
Reshaping datasets with pivot_longer and modifying data fields using mutate.
Calculated statistical metrics such as mean, median, mode, variance, standard deviation, and interquartile range
(IQR) on the Nile dataset.
Produced visualisations like histograms and QQ plots to explore distribution and normality of river flow data.
Developed and exported cleaned datasets to CSV files.
Demonstrated proficiency in Python’s data analysis libraries (pandas, NumPy, matplotlib) and R’s data
manipulation functions.
Applied Machine Learning
Achievements
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Leveraged Pandas, NumPy, Matplotlib, and Scikit-Learn to achieve 90% accuracy on a dataset of
28,153 Amazon reviews.
Developed a Python-based machine learning solution for multi-task classification on Amazon review
text data.
Designed and implemented a model to predict review star ratings (1-5) and classify products into
Luxury Beauty or Prime Pantry categories.
Data Preparation: Conducted exploratory data analysis, data cleaning, and split data into training and
test sets.
Text Processing: Employed tokenization, stemming, lemmatization, and text vectorization to structure
textual data for model analysis.
Model Evaluation and Ethical Considerations: Assessed model performance and addressed ethical
concerns, including recommendation biases and data privacy issues.
