Course Overview
This 12-hour hands-on course teaches you everything R / RStudio does — and more — using Python. By the end you will have built a fully functional AI web application that loads data, explores it visually, trains a machine learning model, and surfaces results through an interactive Streamlit interface powered by an LLM chatbot.
Who Is This Course For?
- R / RStudio users who want to transition to Python
- Data analysts and business intelligence professionals
- Fresh graduates entering data science / AI roles
- Working professionals upskilling in AI-driven analytics
- Anyone who found R syntax confusing and wants a cleaner alternative
Prerequisites
- Basic computer literacy (file management, installing software)
- No prior Python experience required
- No R knowledge required — we make direct comparisons throughout
- A laptop with internet access and at least 8 GB RAM recommended
What You Will Build (Capstone Project)
| 🚀 PROJECT | An AI-Powered Sales Intelligence Dashboard — a Streamlit web app that ingests a sales CSV, cleans and visualises the data, trains a predictive model, and answers natural language questions about the data using an integrated LLM chatbot. |
R / RStudio vs Python — Side-by-Side
Every concept in this course maps directly to an R / RStudio equivalent. Here is the full translation map:
| Feature | R / RStudio | Python (Our Course) |
| IDE / Notebook | RStudio IDE | Jupyter / VS Code |
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| Data Manipulation | dplyr / tidyr | pandas / polars |
| Visualisation | ggplot2 | matplotlib / seaborn / plotly |
| Statistical Modelling | lm(), glm() | statsmodels / scipy |
| ML / AI | caret / mlr3 | scikit-learn / PyTorch |
Course Structure at a Glance
The course runs across 6 sessions of 2 hours each. Each session is a blend of concept explanation, live coding, and hands-on practice.
| Session | Topic | Duration | Type |
| 1 | Python Setup, Jupyter & pandas Fundamentals | 2 hrs | Theory + Lab |
| 2 | Data Wrangling & EDA — The pandas Equivalent of dplyr + ggplot2 | 2 hrs | Lab-heavy |
| 3 | Statistical Analysis & Visualisation with Python | 2 hrs | Lab-heavy |
| 4 | Machine Learning with scikit-learn | 2 hrs | Lab + Project |
| 5 | AI & NLP — HuggingFace, LangChain & LLM Integration | 2 hrs | Lab + Project |
| 6 | Building & Deploying the AI App (Streamlit + FastAPI) | 2 hrs | Capstone Build |