Intermediate
Data Science & ML
A comprehensive 5-day bootcamp advancing from data manipulation with Pandas to building machine learning models with real-world datasets.
Intermediate Python Structures
1Advanced Data Types
- Dictionaries and key-value pairs
- Sets and tuples
- Nested lists and dictionaries
2File Handling
- Reading from and writing to text files
- Working with CSV files manually
- Context managers (with statements)
3Error Handling
- Try, Except blocks
- Handling user input errors gracefully
4Hands-on project: Digital Contact Book
Create a program that stores contact details (name, email, phone) in a dictionary and saves them to a file, allowing the user to retrieve them later.
Introduction to Data Analysis
1Getting Started with Pandas
- Installation and setup
- Understanding DataFrames and Series
- Loading data from Excel or CSV
2Data Inspection
- Viewing top/bottom rows (head, tail)
- Getting statistical summaries (describe)
- Selecting specific columns and rows
3Basic Data Cleaning
- Handling missing values
- Filtering unwanted data
4Hands-on project: Grade Analyzer
Load a dataset of student grades. The script will calculate class averages, find the highest score, and filter for students who passed.
Data Visualization
1Plotting with Matplotlib
- Basic line and bar charts
- Adding titles, labels, and legends
- Customizing colors and styles
2Introduction to Seaborn
- Easier plotting for dataframes
- Creating histograms and scatter plots
- Visualizing relationships in data
3Hands-on project: Weather Report Visualizer
Take a dataset of daily temperatures and create a visual dashboard showing temperature trends over time and a histogram of temperature distribution.
Foundations of Machine Learning
1Machine Learning Workflow
- Data preparation (Features vs Targets)
- Splitting data: Training sets vs Testing sets
- The concept of "fitting" a model
2Introduction to Scikit-Learn
- Importing standard models
- Simple Classification (Decision Trees)
- Simple Regression (Predicting a number)
3Evaluating Models
- How to tell if your model is working
- Accuracy score basics
4Hands-on project: Flower Classifier
Use the classic Iris dataset to train a Decision Tree. The program will take measurements of a flower and predict which species it belongs to.
Applied Data Projects
Congratulations! Today we move away from theory and focus on applying our data skills to build functional tools.
1Objectives
- Integrate File I/O, Pandas, and Logic
- Understand the flow of data in an application
- Prepare for advanced AI topics
2Project 1: Basic Movie Recommender
Build a system that takes a genre as input and returns the top 5 highest-rated movies from a dataset using Pandas filtering.
3Project 2: House Price Estimator
Create a script that uses a simple linear model to estimate the price of a house based on its square footage, training on real historical data.
4Next Steps
You now have the foundation for AI! The next logical steps are Deep Learning (Neural Networks), Computer Vision, or Advanced Statistics.