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 |
|
|
|
|
|
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 |