Iris Dataset Curriculum

Week 1: Introduction to Data Science and the Iris Dataset

Day 1: Introduction to Data Science

What is data science?

Key concepts: Data collection, data processing, data analysis.

Day 2: Getting to Know the Iris Dataset

Overview of the Iris dataset.

Significance in machine learning.

Download and explore the dataset using Python (Pandas).

Day 3: Basics of Data Manipulation

Introduction to Pandas and NumPy.

Basic operations: filtering, sorting, and summarizing data.

Day 4: Data Visualization

Introduction to Matplotlib and Seaborn.

Visualizing data distributions and relationships (scatter plots, histograms, box plots).

Day 5: Review and Practice Problems

Solve exercises based on week’s content.

Week 2: Statistical Foundations and Data Preprocessing

Day 1: Descriptive Statistics

Mean, median, mode, range, variance, standard deviation.

Application to the Iris dataset.

Day 2: Data Preprocessing

Handling missing data.

Data normalization and standardization.

Day 3: Introduction to Probability

Basic concepts of probability.

Probability distributions.

Day 4: Correlation and Causation

Understanding correlation coefficients.

Visualizing correlations in the Iris dataset.

Day 5: Review and Practice Problems

Case studies and problem-solving session.

Week 3: Introduction to Machine Learning

Day 1: Machine Learning Overview

Types of machine learning: supervised, unsupervised, reinforcement.

Introduction to machine learning algorithms.

Day 2: Supervised Learning - Classification

Deep dive into classification.

Building a classifier using the Iris dataset.

Day 3: Model Evaluation

Splitting data into training and test sets.

Metrics for evaluating classifiers (accuracy, precision, recall, F1-score).

Day 4: Overfitting and Underfitting

Concepts of bias and variance.

Techniques to combat overfitting (cross-validation, regularization).

Day 5: Project: Build a Classifier

Apply the week’s learning to build and evaluate a classifier using the Iris dataset.

Week 4: Advanced Topics and Project Work

Day 1: Unsupervised Learning - Clustering

Introduction to clustering algorithms (K-means, hierarchical clustering).

Clustering the Iris dataset.

Day 2: Introduction to Neural Networks

Basics of neural networks.

Simple neural network model for classification.

Day 3: Deep Learning Frameworks

Introduction to TensorFlow/Keras.

Building a simple neural network using Keras for the Iris dataset.

Day 4 & Day 5: Capstone Project

Students will choose a project to apply all the concepts learned.

Presentation of projects.

Continuous Learning

Encourage students to participate in online competitions.

Recommend advanced courses and resources for further learning.

Each week includes theoretical understanding paired with hands-on practice, ideally using Python, which is a popular language for data science due to its simplicity and powerful libraries. This curriculum is designed to provide a solid foundation in data science and introduce machine learning concepts in a structured way.