JONATHAN SHER

Data Analyst & Business Intelligence Specialist

Student passionate about AI, machine learning, and advanced data analytics dedicated to solving real-world problems and delivering measurable value through strategic, data-driven solutions.

About Me

I'm an undergraduate at California State University, Northridge, studying Computer Information Technology, with a prior foundation in Data Science from the University of Sydney. My studies integrate cloud systems, computer networks, and machine learning equipping me to solve complex problems at the intersection of business and technology.

I've built hands-on projects in Python, SQL, R, and Java, developing solutions that span from data pipelines to infrastructure automation. Alongside my technical growth, I'm deeply invested in sharpening my business acumen understanding how systems drive decisions, and how data can be used not just for insight, but to drive impact.

Looking ahead, I plan to pursue graduate studies in AI, ML, and advanced data analytics further aligning my technical skills with strategic problem-solving. I'm driven to build scalable, intelligent solutions that solve real problems, deliver measurable value, and shape how modern organizations operate.

Jonathan Sher

🚀 FEATURED PROJECTS

A showcase of visually stunning, high-impact portfolio work.

Global EV Market Strategic Dashboard

Global EV Market Strategic Dashboard

Independently developed a strategic analytics platform simulating market intelligence for the $784B global electric vehicle industry. Applied Porter’s Five Forces and MECE methodology to deliver consulting-style insights through a fully interactive dashboard.

Key Impact

Modeled insights across a high-growth, $784B industry with 27% YoY expansion — designed to demonstrate structured thinking and analytical depth aligned with top-tier consulting standards.

Technologies

React 18Tailwind CSSRechartsFramer MotionVite

Challenge

Navigating high-growth sectors like EVs requires structured frameworks to break down competitive dynamics, yet few interactive tools exist that combine business reasoning with technical execution.

Solution

Built a consulting-style dashboard that visualizes market share, competitive positioning, and strategic recommendations across global players using business frameworks and responsive UI components.

Result

Created a business-facing analytics tool demonstrating strategy formulation, market structuring, and product design—positioned as a simulation of MBB-style market work.

RetailMax Digital Transformation Strategy

RetailMax Digital Transformation Strategy

Simulated a $38M enterprise transformation for a $3.2B retail company using consulting frameworks and BI tools. Integrated Porter’s Five Forces, McKinsey 7-S, and MECE methodology to deliver executive-level insights across platform, customer, and analytics phases.

Key Impact

Modeled 25%+ digital revenue growth and 30% operational efficiency gains

Technologies

React 18Tailwind CSSRechartsRSQLPower BITableau

Challenge

Legacy systems and poor customer experience created operational bottlenecks and revenue stagnation in a competitive retail environment

Solution

Developed an analytics-driven roadmap with phased rollout across infrastructure, customer experience, and AI/BI layers using Agile and McKinsey 7-S frameworks

Result

Simulated enterprise value creation with 60% automation, 35% CLV uplift, and MBB-standard insights

Movie Recommendation System

Movie Recommendation System

Developed a collaborative filtering-based movie recommendation system using the MovieLens 100k dataset. Implemented k-NN, SVD, and hybrid models, and deployed an interactive Streamlit web app for real-time recommendations.

Key Impact

Enabled personalized movie suggestions and demonstrated scalable recommender system design with multiple ML models.

Technologies

Pythonscikit-learnStreamlitSVDk-NNPandasMachine Learning

Challenge

Users struggle to find relevant movies due to overwhelming choices and lack of personalized recommendations.

Solution

Built a system that analyzes user ratings and preferences to suggest movies using collaborative filtering and matrix factorization.

Result

Delivered an interactive web app for movie recommendations, combining multiple ML models for improved accuracy. Live demo and code available on GitHub.

TECHNICAL MASTERY

SKILLS & EXPERTISE

Strategic capabilities honed through years of experience in data-driven decision making!

Data Analysis

SQL Expert
Python Expert
R Expert
Statistical Analysis Expert
Data Visualization Expert

Business Intelligence

Tableau Intermediate
Power BI Advanced
Data Modeling Expert
ETL Processes Intermediate
Dashboard Design Expert

Machine Learning

Predictive Modeling Advanced
Classification Advanced
Regression Analysis Expert
Clustering Advanced
Time Series Analysis Expert
PROFESSIONAL CREDENTIALS

CERTIFICATIONS & TRAINING

Validating expertise through industry-recognized certifications and continuous learning

Machine Learning Specialization

2025

Stanford University & DeepLearning.AI

Verified Credential

Data Scientist with R

2021

DataCamp

Verified Credential

SQL Fundamentals

2021

DataCamp

Verified Credential
LET'S CONNECT

GET IN TOUCH

I'm always open to discussing new projects, partnerships, or opportunities to collaborate. Feel free to reach out through any of these channels.

Contact Information

I'm always open to discussing new projects, partnerships, or opportunities to collaborate. Feel free to reach out through any of these channels.

Send Me a Message