10 Academy profile site

Wandera Martin

Kampala, Uganda

Strathmore University (2019-2023)

BSc. (Business Information Technology)

Email: martinwandera@gmail.com

My GitHub

Gen AI Engineering

Prompt Engineering
RAG
Fine-tuning
Vector DB
Langchain
Pytorch
Tensorflow

Machine Learning

EDA
Scikit-learn
Matplotlib
CNN & RNN
Keras

Software Engineering

Python
JavaScript
Fast API
Tailwind CSS
Streamlit
Testing, Unit Testing

DevOps &MLops

Docker
CI/CD
Travis CML
DVC
MLflow
Apache Airflow
Apache Kafka

About me

A data enthusiast, with experience in SQL, data exploration, data preprocessing, and versioning with DVC and Insight visualization in python language. I also take pride in cleaning data for accurate machine learning models. I am looking to be a Data Engineer. I am open to learning new techniques of data engineering

Education

10 Academy ( 2024-2024 )

Data Engineering Training

Skills Gained:

Business and Data Understanding
Designing and Building Data Pipelines (ELT and ETL)
Dashboard & Visualization
Modelling, Evaluation, and Deployment
MLOps & Continuous Delivery
Technical Writing and Blogging
Collaboration, Teamwork, and Leadership
Community Building and Career Skills

Strathmore University (2018-2022)

Diploma in Business Information Technology

Major Courses:

Software Engineering
Object-Oriented Programming
Data Structures and Algorithms
Database Systems

Work Experience

Junior Front-End Web Developer | UX Designer

SMS One (February 2023–December 2023)

Tasks:

Conducted user interviews and surveys to understand user needs for a new mobile app
Created wire-frames, prototypes, and mock-ups to iterate on the app's design
Collaborated with back-end developers to implement responsive and user-friendly interfaces
Developed skills in user flow mapping to improve design communication

Download CV here

Projects

Traffic Data Analytics Data Warehouse

The objective of this initiative is to support the city traffic department by employing swarm UAVs (drones) to gather comprehensive traffic data from various city locations. The collected data will be utilized to enhance traffic flow within the city and contribute to undisclosed projects. To facilitate this, we aim to construct a scalable data warehouse capable of hosting vehicle trajectory data derived from the analysis of footage captured by swarm drones and static roadside cameras. The design of the data warehouse accommodates future requirements and effectively organizes the data to enable efficient querying for various downstream projects. The chosen framework for this endeavour is the Extract Load Transform (ELT) approach, leveraging the capabilities of DBT and Airflow for orchestration.

Scalable Backtesting Infrastructure for Crypto Trading (Group Work)

A startup called Mela (our client for this week’s project) wants to make it simple for everyone to enter the world of cryptocurrencies and general stock market trade. It also wants to give investors a reliable source of investment while lowering the risk associated with trading cryptocurrencies. The objective of this project is very straightforward: design and build a robust, reliable, large-scale trading data pipeline, for both crypto and stock market trading that can run various backtests and store various useful artifact in a strong data warehouse system, users will be prompted with several stock and crypto trading options and parameters. After processing these parameters users will be provided with several back testing outputs using different strategies on the specific stock or crypto trading.

Causal Inference for Logistic optimization: Delivery drivers location optimization

The objective of this initiative is to support the city traffic department by employing swarm UAVs (drones) to gather comprehensive traffic data from various city locations. The collected data will be utilized to enhance traffic flow within the city and contribute to undisclosed projects. To facilitate this, we aim to construct a scalable data warehouse capable of hosting vehicle trajectory data derived from the analysis of footage captured by swarm drones and static roadside cameras. The design of the data warehouse accommodates future requirements and effectively organizes the data to enable efficient querying for various downstream projects. The chosen framework for this endeavour is the Extract Load Transform (ELT) approach, leveraging the capabilities of DBT and Airflow for orchestration.