Become an AI-Ready Data Scientist
Inside Tibora's Four-Month Data Science & Modern AI Certification Program
Every year, thousands of people finish a "data science bootcamp" that teaches them how to fit a linear
regression and call it a day. Then they go looking for a job — and discover that companies aren't just
hiring for machine learning anymore. They're hiring for people who can build with LLMs, design
intelligent agents, work with databases, and ship real AI applications end to end.
That gap is exactly why Tibora AI Institute built this program.
Our Data Science & Modern AI Certification is a 16-week, 5/6-hours-a-week journey that takes you from
Python fundamentals to building your own Retrieval-Augmented Generation chatbot, an autonomous AI
agent, and a business-ready AI capstone project. No fluff, no passive video-watching — just a structured
path through the tools and techniques the industry is actually hiring for today.
Here's what four months at Tibora looks like.
What You Actually Need to Start
● WiFi
● Internet
● Interest
That's it. No prerequisite degree, no prior coding experience required. We meet you where you are and
build the foundation from the ground up.
Weeks 1–2: Introduction to Data Science
You'll start by understanding the field itself — what data scientists actually do, what today's job postings
are really asking for, and the CRISP-DM workflow that structures every real-world data science project.
From there, it's hands-on: setting up your Python environment in Anaconda, Jupyter, or Google Colab,
followed by a Python programming refresher in the lab so everyone starts on equal footing.
Weeks 3–4: Data Wrangling and Exploration
Real-world data is messy, and this is where you learn to tame it. Lectures cover data collection, web
scraping, loading and saving data, and SQL, along with the cleaning techniques every dataset eventually
demands — handling missing values, outliers, and duplicates. You'll also get your first introduction to
visualization with Matplotlib and the core ideas behind exploratory data analysis (EDA).
In the lab, you'll get your hands dirty cleaning real datasets with pandas and building visualizations in
Matplotlib and Seaborn.Weeks 5–7: Machine Learning Basics
This is the machine learning core of the program. You'll cover the divide between supervised and
unsupervised learning, then work through the classic algorithm toolkit: regression, decision trees, KNN,
and k-means clustering. Just as important, you'll learn how to evaluate a model honestly, using proper
metrics, validation techniques, and hyperparameter tuning with GridSearchCV.
Labs put every algorithm into practice, building and evaluating each model type using scikit-learn.
Weeks 8–10: Deep Learning and Transfer Learning
This is where things get interesting. Beyond feature engineering and selection, you'll be introduced to
neural networks and deep learning, including feedforward networks and CNN classifiers, ensemble
methods, and autoencoders. You'll also dig into foundation models — understanding what pretraining
and fine-tuning actually mean, using architectures like VGG16 and modern foundation model classifiers.
In the lab, you'll implement feature engineering pipelines in scikit-learn, then build both a basic neural
network and a deeper network in PyTorch, training and evaluating ensemble models along the way.
Week 11: Time Series Analysis
A focused week on sequential data: an introduction to time series analysis, recurrent neural networks
(RNNs), and Tiny Time Mixer (TTM) models — the kind of forecasting techniques used across finance,
operations, and industrial applications.
Weeks 12–13: Generative AI, LLMs, RAG, and Agentic AI
This is where the program pushes into the technology reshaping every industry. You'll get an overview of
generative AI's applications and potential, then go deep on large language models — Transformers,
GPT, LLaMA, and the architectures behind them. From there, you'll learn how Retrieval-Augmented
Generation (RAG) connects LLMs to external knowledge sources, and how Agentic AI systems reason,
plan, use tools, and complete multi-step tasks autonomously.
In the lab, you'll fine-tune a pretrained LLM for a specific task using Hugging Face or a similar
framework, practice prompt engineering, build a RAG system by combining an LLM with a vector
database like FAISS, and create a chatbot that can answer domain-specific questions using your own
documents. You'll also get hands-on building an AI agent capable of reasoning and using external tools.
Weeks 14–16: Capstone Project & AI for Business Creators
The capstone begins in week 14, giving you three full weeks to apply everything you've learned — from
problem definition through data collection, modeling, evaluation, and presentation — on a project of realsubstance.
In the final two weeks (15–16), the program adds a dedicated AI for Business Creators track alongside
your capstone work. This module is built for anyone who wants to go beyond technical skill and actually
launch something with what they've built. You'll learn how to:
● Package an AI project into a product, service, or consulting offer
● Identify real business use cases for LLMs, RAG, and AI agents
● Position and price AI-powered solutions for clients or employers
● Pitch and present an AI capstone project to a non-technical, business audience
By the end of week 16, you'll walk away with a finished capstone project and a clear framework for
turning your AI skills into a business, a freelance offering, or a stronger case for the job you want next.
A Portfolio, Not Just a Certificate
By the end of the program, you won't just hold a certificate — you'll have a body of work to show for it:
● 5 mandatory projects spanning the core skills of the program
● 2 optional advanced projects for students who want to push further
● 1 capstone project, developed across weeks 14–16 and framed for a business audience
Together, these form a GitHub portfolio built specifically to strengthen your resume and prepare you for
technical interviews — and, for those pursuing it, a foundation for launching an AI-driven business.
Why This Program Is Different
Most certification programs stop at machine learning. Ours doesn't, because the job market didn't stop
there either. Over 16 weeks, you'll move from Python basics and SQL, through classical machine
learning and deep learning, into the technologies defining this decade of AI: large language models,
Retrieval-Augmented Generation, and autonomous AI agents — then close with a capstone and a
business track that turns that skill into something you can actually launch.
Six hours a week. Four months. One portfolio of real, working AI systems.
Learn → Build → Practice → Deploy → Showcase → Get Hired.
That's the Tibora philosophy — and it's exactly what this program is built to deliver.