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Code Caribbean

Data science and AI coding built around Caribbean problems, Caribbean data, and Caribbean context. Because relevance is the best teacher.

Why It Exists

Learn data science using
data that actually matters.

Most data science courses use American housing prices or global e-commerce data. Code Caribbean uses Jamaican crime statistics, Caribbean climate datasets, local election results, and CARICOM trade records.

When the data is familiar, the learning sticks. You understand why a model is underfitting because you know what the actual weather in Kingston looks like. You spot outliers because you know Jamaican agriculture.

This is a project-driven program. You do not spend months in theory before touching real problems. From week one, you are cleaning, exploring, and building with real Caribbean data.

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Caribbean Datasets Throughout

Every dataset, every project, every case study uses regional data. You graduate knowing how Caribbean data behaves.

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Project Portfolio Included

Five completed projects on GitHub by the end of the program. Employers see real work, not just a certificate.

Data visualization charts on screen
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Caribbean First All projects use local data
Curriculum

The full stack of
data science skills.

From raw CSV files to deployed machine learning pipelines. Every tool covered has a direct job market application.

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Python for Data

The right Python for data work. Not software engineering, not web dev. Data-focused Python from lists and dicts to file handling and APIs. Practical from day one.

Tools: Python 3, Jupyter Notebooks, VS Code
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Pandas and Data Wrangling

The reality of data science is that 70% of your time is cleaning. We teach you to do it fast. DataFrames, merging, groupby, handling missing values, time series.

Tools: Pandas, NumPy
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Data Visualization

A chart that confuses people is useless. Learn to make visualizations that tell a clear story. Static and interactive. Present findings to non-technical audiences.

Tools: Matplotlib, Seaborn, Plotly
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Machine Learning with Scikit-learn

Classification, regression, clustering. Random forests, gradient boosting, SVMs. How to choose the right algorithm. How to know when your model is lying to you.

Tools: Scikit-learn, XGBoost
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ML Pipelines and Deployment

Building a model that works on your laptop is not enough. Package it, automate the preprocessing, version it, and deploy it so other people can actually use it.

Tools: Scikit-learn Pipelines, Streamlit, Flask
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Caribbean Case Studies

Tourism demand forecasting. Hurricane track prediction. Agricultural yield modeling. Crime hotspot analysis. Real problems, real data, real impact. Your capstone comes from here.

Data: PIOJ, STATIN, CIMH, FAO Caribbean
Example Projects

Work that shows up
in your portfolio.

These are the kinds of projects past participants have built. Yours will be yours to own and publish.

Caribbean weather data visualization
Climate

Jamaica Rainfall Predictor

A regression model that predicts monthly rainfall for Jamaican parishes using 40 years of meteorological data from the Climate Studies Group Mona.

Time Series AnalysisRegression
Agricultural field data
Agriculture

Crop Yield Dashboard

An interactive dashboard showing how temperature, rainfall, and soil type correlate with yam, banana, and sugarcane yields across Jamaican regions.

Plotly DashCorrelation Analysis
Tourism data analysis
Tourism

Visitor Flow Forecaster

A forecasting model for tourist arrivals to Jamaica using JTB data, flight search trends, and seasonal patterns. Built and deployed with Streamlit.

ARIMA + MLStreamlit Deploy
Career Paths

Where do Code Caribbean
graduates go?

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Data Analyst

Government agencies, financial institutions, and NGOs across Jamaica all need analysts who can translate data into decisions. Entry-level roles start at competitive local salaries.

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Data Scientist

Build predictive models for companies in Jamaica or work remotely for international clients. Remote data science salaries are often 5-10x local averages.

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BI Developer

Business intelligence is huge in Caribbean banking, retail, and logistics. Build dashboards and reporting systems that drive real decisions.

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Research Assistant

UWI, PIOJ, and regional development organizations hire data-skilled research assistants. Great entry point with strong career progression.

FAQs

Quick answers
to the common questions.

How is Code Caribbean different from Genius Bootcamp?

Genius Bootcamp goes broad across all of AI including neural networks, NLP, and computer vision. Code Caribbean goes deep specifically into data science, the analysis and modeling side. Many participants do both in sequence.

Do I need any math background?

CSEC Mathematics level is enough to start. You do not need calculus or linear algebra from day one. We build the math intuition you need alongside the coding, not before it.

How long is the program?

Ten weeks, two to three sessions per week. Roughly 120 hours of learning total including project work. You set the pace on the independent components.

Will I get a certificate?

Yes. You get a Genius Project completion certificate and we help you earn relevant free certifications from Google and IBM as you go through the program. But your GitHub portfolio matters more than any certificate.

Ready to start your
data science journey?

Applications open three times per year. Get on the list and we will contact you when the next cohort opens.

Apply for Code Caribbean → See Genius Bootcamp Too