Build A Diabetes Dashboard With Python, Streamlit & Ml

A fast-track, project based course covering data science basics, ML, and visualizations.

What you'll learn

Build an interactive Streamlit dashboard app in Python from scratch, using real-world diabetes data.

Create insightful data visualizations with Pandas, Matplotlib, and Seaborn to explore health datasets.

Develop and integrate machine learning models (e.g., logistic regression, decision tree) into a deployable web app for diabetes prediction.

Deploy a polished, user-friendly data science project that demonstrates both coding and applied ML skills — perfect for portfolios or job applications.

Requirements

A computer (Windows, Mac, or Linux) with internet access.

Basic familiarity with Python (variables, functions, simple scripts) is helpful but not required — I’ll guide you step by step.

Willingness to install Anaconda (free, beginner-friendly Python distribution) — I’ll walk you through the setup process in detail.

No prior experience with data visualisation, machine learning, or Streamlit is needed — we’ll build everything from scratch.

Description

Are you ready to fast-track your data science journey and build real projects you can proudly showcase? This course is your direct path to becoming a practical, project-oriented data scientist. We've eliminated the endless theory and created a curriculum that gets you hands-on from Day 1. Instead of spending weeks stuck in dry concepts, you’ll learn by doing—working through a complete, project-based curriculum that takes you from raw data all the way to a polished, interactive application.We'll use the Pima Indians Diabetes Dataset as our guide, a classic challenge that provides the perfect opportunity to master a full, end-to-end data science workflow:Data exploration & cleaning – Learn how to quickly uncover insights in real-world datasets.Data visualization – Transform numbers into clear, meaningful charts and graphs.Machine learning models – Train and evaluate predictive models step-by-step.Streamlit web apps – Bring your work to life with shareable, interactive dashboards.A Portfolio Piece That Gets You NoticedBy the end of this course, you won't just "know the concepts"—you’ll have a fully functional data science project to add to your resume, GitHub, or LinkedIn. This isn’t just about earning a certificate; it’s about building a portfolio that proves you have the skills to solve real-world problems. You'll be able to confidently discuss your work, share your code, and showcase a finished product that demonstrates your ability to navigate the entire data science lifecycle. Whether you’re a student, a career-changer, or a busy professional, this fast-track approach ensures you skip the fluff and focus on what really matters: building a skillset through projects.This course is fast to learn, practical to apply, and built for real results. Stop dreaming about a career in data science and start building your future.

Overview

Section 1: Introduction

Lecture 1 Introduction - What we'll create in this course!

Lecture 2 Anaconda Download Instructions

Lecture 3 Download Link for Jupyter Files

Lecture 4 Opening the Files in Jupyter

Lecture 5 Python Crash Course

Lecture 6 Explanation of the Dataset

Section 2: Extracting Basic Insights

Lecture 7 Coding Basic Commands

Lecture 8 Uploading Basic Commands to App - Part 1

Lecture 9 Uploading Basic Commands to App - Part 2

Section 3: Data Visualization

Lecture 10 Why Visualize Data?

Lecture 11 Histograms and KDEs

Lecture 12 Adding Histograms to Webapp

Lecture 13 Adding KDEs to Webapp

Lecture 14 Scatterplots

Lecture 15 Subplots

Lecture 16 Heatmaps and Correlations

Lecture 17 Data Cleaning

Lecture 18 Adding Scatterplots, Correlation Heatmap, and Cleaned Data to App

Section 4: Machine Learning - Logistic Regression & Decision Tree Classifiers

Lecture 19 Logistic Regression Theory

Lecture 20 Logistic Regression in Jupyter

Lecture 21 Extra! In-Depth, Explained: Logistic Regression in Jupyter

Lecture 22 Explanation of Metrics

Lecture 23 Dictionaries Crash Course - Skip if familiar with Python dictionaries

Lecture 24 Logistic Regression in Streamlit

Lecture 25 Supervised vs. Unsupervised Learning

Lecture 26 Decision Tree Classifier Theory

Lecture 27 Decision Trees in Jupyter

Lecture 28 Decision Trees in Streamlit

Section 5: Extra: Overfitting & Deploying the App

Lecture 29 Extra ML Lecture: What is overfitting?

Lecture 30 Extra App Lecture: Deploying your app through GitHub

Beginner Python developers curious about Data Science,Students interested in conducting medicine/STEM research using ML,Students wanting to learn how to develop an app with Python through Streamlit