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Add beginner-friendly examples with comprehensive documentation
Co-authored-by: leestott <[email protected]>
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‎.gitignore‎

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.ionide/
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.vscode/settings.json
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# Example output files (generated by running example scripts)
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examples/*.png
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examples/*.jpg
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examples/*.jpeg
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‎README.md‎

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# Getting Started
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> **Complete Beginners**: New to data science? Start with our [beginner-friendly examples](examples/README.md)! These simple, well-commented examples will help you understand the basics before diving into the full curriculum.
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> **Teachers**: we have [included some suggestions](for-teachers.md) on how to use this curriculum. We'd love your feedback [in our discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
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> **[Students](https://aka.ms/student-page)**: to use this curriculum on your own, fork the entire repo and complete the exercises on your own, starting with a pre-lecture quiz. Then read the lecture and complete the rest of the activities. Try to create the projects by comprehending the lessons rather than copying the solution code; however, that code is available in the /solutions folders in each project-oriented lesson. Another idea would be to form a study group with friends and go through the content together. For further study, we recommend [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
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> **A note about quizzes**: All quizzes are contained in the Quiz-App folder, for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized.
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## 🎓 Beginner-Friendly Examples
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**New to Data Science?** We've created a special [examples directory](examples/README.md) with simple, well-commented code to help you get started:
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- 🌟 **Hello World** - Your first data science program
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- 📂 **Loading Data** - Learn to read and explore datasets
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- 📊 **Simple Analysis** - Calculate statistics and find patterns
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- 📈 **Basic Visualization** - Create charts and graphs
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- 🔬 **Real-World Project** - Complete workflow from start to finish
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Each example includes detailed comments explaining every step, making it perfect for absolute beginners!
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👉 **[Start with the examples](examples/README.md)** 👈
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## Lessons
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‎examples/01_hello_world_data_science.py‎

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"""
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Hello World - Data Science Style!
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This is your very first data science program. It introduces you to the basic
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concepts of working with data in Python.
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What you'll learn:
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- How to create a simple dataset
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- How to display data
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- How to work with Python lists and dictionaries
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- Basic data manipulation
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Prerequisites: Just Python installed on your computer!
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"""
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# Let's start with the classic "Hello, World!" but with a data science twist
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print("=" * 50)
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print("Hello, World of Data Science!")
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print("=" * 50)
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print()
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# In data science, we work with data. Let's create our first simple dataset.
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# We'll use a list to store information about students and their test scores.
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# A list is a collection of items in Python, written with square brackets []
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students = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
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scores = [85, 92, 78, 95, 88]
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print("Our Dataset:")
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print("-" * 50)
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print("Students:", students)
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print("Scores:", scores)
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print()
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# Now let's do something useful with this data!
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# We can find basic statistics about the scores
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# Find the highest score
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highest_score = max(scores)
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print(f"📊 Highest score: {highest_score}")
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# Find the lowest score
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lowest_score = min(scores)
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print(f"📊 Lowest score: {lowest_score}")
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# Calculate the average score
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# sum() adds all numbers together, len() tells us how many items we have
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average_score = sum(scores) / len(scores)
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print(f"📊 Average score: {average_score:.2f}") # .2f means show 2 decimal places
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print()
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# Let's find who got the highest score
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# We use index() to find where the highest_score is in our list
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top_student_index = scores.index(highest_score)
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top_student = students[top_student_index]
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print(f"🏆 Top student: {top_student} with a score of {highest_score}")
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print()
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# Now let's organize this data in a more structured way
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# We'll use a dictionary - it pairs keys (student names) with values (scores)
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print("Student Scores (organized as key-value pairs):")
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print("-" * 50)
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# Create a dictionary by pairing students with their scores
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student_scores = {}
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for i in range(len(students)):
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student_scores[students[i]] = scores[i]
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# Display each student and their score
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for student, score in student_scores.items():
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# Add a special marker for the top student
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marker = "⭐" if student == top_student else " "
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print(f"{marker} {student}: {score} points")
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print()
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print("=" * 50)
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print("Congratulations! You've completed your first data science program!")
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print("=" * 50)
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# What did we just do?
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# 1. Created a simple dataset (student names and scores)
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# 2. Performed basic analysis (max, min, average)
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# 3. Found insights (who is the top student)
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# 4. Organized the data in a useful structure (dictionary)
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#
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# These are the fundamental building blocks of data science!
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# Next, you'll learn to work with real datasets using powerful libraries.

‎examples/02_loading_data.py‎

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"""
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Loading and Exploring Data
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In real data science projects, you'll work with data stored in files.
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This example shows you how to load data from a CSV file and explore it.
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What you'll learn:
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- How to load data from a CSV file
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- How to view basic information about your dataset
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- How to display the first/last rows
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- How to get summary statistics
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Prerequisites: pandas library (install with: pip install pandas)
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"""
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# Import the pandas library - it's the most popular tool for working with data in Python
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# We give it the short name 'pd' so we can type less
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import pandas as pd
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print("=" * 70)
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print("Welcome to Data Loading and Exploration!")
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print("=" * 70)
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print()
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# Step 1: Load data from a CSV file
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# CSV stands for "Comma-Separated Values" - a common format for storing data
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# We'll use the birds dataset that comes with this repository
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print("📂 Loading data from birds.csv...")
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print()
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# Load the data into a DataFrame (think of it as a smart spreadsheet)
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# A DataFrame is pandas' main data structure - it organizes data in rows and columns
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data = pd.read_csv('../data/birds.csv')
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print("✅ Data loaded successfully!")
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print()
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# Step 2: Get basic information about the dataset
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print("-" * 70)
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print("BASIC DATASET INFORMATION")
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print("-" * 70)
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# How many rows and columns do we have?
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num_rows, num_columns = data.shape
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print(f"📊 Dataset size: {num_rows} rows × {num_columns} columns")
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print()
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# What are the column names?
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print("📋 Column names:")
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for i, column in enumerate(data.columns, 1):
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print(f" {i}. {column}")
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print()
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# Step 3: Look at the first few rows of data
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# This gives us a quick preview of what the data looks like
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print("-" * 70)
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print("FIRST 5 ROWS OF DATA (Preview)")
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print("-" * 70)
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print(data.head()) # head() shows the first 5 rows by default
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print()
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# Step 4: Look at the last few rows
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print("-" * 70)
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print("LAST 3 ROWS OF DATA")
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print("-" * 70)
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print(data.tail(3)) # tail(3) shows the last 3 rows
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print()
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# Step 5: Get information about data types
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print("-" * 70)
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print("DATA TYPES AND NON-NULL COUNTS")
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print("-" * 70)
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print(data.info()) # Shows column names, data types, and count of non-null values
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print()
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# Step 6: Get statistical summary
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print("-" * 70)
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print("STATISTICAL SUMMARY (for numerical columns)")
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print("-" * 70)
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# describe() gives us statistics like mean, std, min, max, etc.
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print(data.describe())
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print()
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# Step 7: Check for missing values
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print("-" * 70)
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print("MISSING VALUES CHECK")
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print("-" * 70)
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missing_values = data.isnull().sum()
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print("Number of missing values per column:")
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print(missing_values)
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print()
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if missing_values.sum() == 0:
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print("✅ Great! No missing values found.")
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else:
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print("⚠️ Some columns have missing values. You may need to handle them.")
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print()
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# Step 8: Get unique values in a column
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print("-" * 70)
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print("SAMPLE: UNIQUE VALUES")
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print("-" * 70)
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# Let's see what unique values exist in the first column
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first_column = data.columns[0]
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unique_count = data[first_column].nunique()
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print(f"The column '{first_column}' has {unique_count} unique value(s)")
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print()
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# Summary
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print("=" * 70)
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print("SUMMARY")
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print("=" * 70)
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print("You've learned how to:")
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print(" ✓ Load data from a CSV file using pandas")
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print(" ✓ Check the size and shape of your dataset")
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print(" ✓ View the first and last rows")
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print(" ✓ Understand data types")
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print(" ✓ Get statistical summaries")
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print(" ✓ Check for missing values")
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print()
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print("Next step: Try loading other CSV files from the data/ folder!")
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print("=" * 70)
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# Pro Tips:
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# - Always explore your data before analyzing it
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# - Check for missing values and understand why they might be missing
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# - Look at the data types to ensure they make sense
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# - Use head() and tail() to spot any obvious issues with your data

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