Sisekelo Sinyolo | Junior Data Scientist | Sisekelo Sinyolo

Hi, I'm Sisekelo!

I'm currently looking for a data analyst role where I can strengthen my skills in data visualisation and storytelling, particularly generating strategic and meaningful insights to support decision-making. A possibility to explore machine learning as the role progresses would be a plus.

About Me

I'm a data analyst with a passion for storytelling.

I have working experience in HR, Sales, and Marketing.
I have research experience in business, economics, and linguistics.
Working with different types of data has been integral in all my work experiences and personal research projects.

I recently completed a 7-month Data Science bootcamp:

Initial focus on data collection, analysis, and visualisation
Built machine learning models for company projects with Immoweb, Orange, Accenture.
My most significant learning, beyond these projects, was exploring the isiNdebele language NLP pipeline

Aside my technology interests, I am an avid reader and creative.

I particularly love reading about history, economics, culture and sometimes, self-development.
Hobbies include public speaking
I am finalising my first book, "My Identity, My Dreams," which is an autobiographical exploration of identity

My Work

NLP Pipeline for isiNdebele - a low-resource language

Machine Learning Modeling | Company project (Orange)

Portfolio

Developing Natural Language Processing (NLP) techniques tailored specifically for the isiNdebele language:

Utilized a range of NLP methodologies including text scraping, data cleaning, language modeling, and semantic analysis to transform raw isiNdebele text data into actionable insights.
Tools used: Python, BeautifulSoup, Sci-kit learn

Wine Market Data Analysis: Analyzed wine market trends and patterns, extracting actionable insights to guide marketing and sales strategies.
Tools used: Tableau, SQL

Tableau analysis | Company project (Accenture)

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Developed a subscription plan recommender system using advanced machine learning techniques, achieving an accuracy of 80.9% on unseen data.

Conducted thorough exploratory data analysis (EDA) and feature engineering, uncovering key insights and optimizing model performance with PyCaret and XGBoost.
Tools used: Python, Sci-kit learn, Pycaret

Explore the project