Basil Latif is a data scientist with expertise in SQL, Python, and machine learning, specializing in data analysis, predictive modeling, and data pipeline design. He combines strong technical execution with business intuition to uncover insights, optimize performance, and build end-to-end analytics solutions.
A good data science project should have a well defined data source and a clearly understandable deliverable. Below, you can peek at a sample of some of those pieces published across the Internet.
CA ZEV Population Dashboard
Problem Summary:
The State of California needed a way to keep track of all deployed zero-emission (ZE) vehicles.
My Approach:
Collected the data, wrote a Python script to centralize all the data and add new features, then built a Tableau dashboard dynamically plotting the output.
Result:
The Dashboard Shown Below
Click to View the Dashboard Here
First/Last-Mile Emissions Estimation Tool
Problem Summary:
FedEx wants to quantify the emissions reduction impact from deplyoing small zero emission vehicles for last mile delivery
My Approach:
I built a JavaScript calculator app which dynaamically calculates the emissions reduction for any given input.
Result:
Link to the tool is below
Click to View Interactive Tool
Keepa API: Amazon Price Data
Problem Summary:
Amazon sellers need access to real-time analytics
My Approach:
I build a custom tool using the Keepa API to dynamically deliver analytics on a given Amazon product.
Result:
Link to my project writeup is below
Buy the Amazon Price Data - Keepa API
How I Built My Own Fitness Tracker Using Google Fit Data
Problem Summary:
Fitness data is stored in Google Fit but not easily visualized anywhere else
My Approach:
I build a custom tool using Google Sheets to visualize the data
Result:
Link to my my Towards Data Science writeup: "How I Built My Own Fitness Tracker Using Google Fit Data"
Project Using a Data Pipeline in Google Apps Script
Read the Article
Predicting Stock Price Using Natural Language Processing
Problem Summary:
Each quarter CFOs and CEOs of major corporations talk to investors via an earnings call. This text data can be used for predicting stock price.
My Approach:
I used NLP libraries like TextBlob and ELI5 to do sentiment analysis on text data
Result:
A Comprehensive ML experiment that uses sentiment analysis to predict stock price
Link to Medium Article