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Usage Guide

Dis guide dey show example and common way wey you fit take use di Data Science for Beginners curriculum.

How to Use This Curriculum

Dis curriculum dey flexible, you fit use am in different ways:

Self-paced learning: Work through di lessons by yourself for your own time
Classroom instruction: Use am as structured course wey get teacher guidance
Study groups: Learn together with your friends
Workshop format: Short-term intensive learning session

Working with Lessons

Each lesson get di same structure to help you learn well:

Lesson Structure

Pre-lesson Quiz: Test wetin you sabi already
Sketchnote (Optional): Visual summary of di main ideas
Video (Optional): Extra video content
Written Lesson: Main concepts and explanation
Jupyter Notebook: Hands-on coding exercise
Assignment: Practice wetin you don learn
Post-lesson Quiz: Make sure you understand di lesson

Example Workflow for a Lesson

# 1. Navigate to the lesson directory
cd 1-Introduction/01-defining-data-science

# 2. Read the README.md
# Open README.md in your browser or editor

# 3. Take the pre-lesson quiz
# Click the quiz link in the README

# 4. Open the Jupyter notebook (if available)
jupyter notebook

# 5. Complete the exercises in the notebook

# 6. Work on the assignment

# 7. Take the post-lesson quiz

Working with Jupyter Notebooks

Starting Jupyter

# Activate your virtual environment
source venv/bin/activate  # On macOS/Linux
# OR
venv\Scripts\activate  # On Windows

# Start Jupyter from the repository root
jupyter notebook

Running Notebook Cells

Run one cell: Press Shift + Enter or click di "Run" button
Run all cells: Go "Cell" → "Run All" for di menu
Restart kernel: Go "Kernel" → "Restart" if problem dey

Example: Working with Data in a Notebook

# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load a dataset
df = pd.read_csv('data/sample.csv')

# Explore the data
df.head()
df.info()
df.describe()

# Create a visualization
plt.figure(figsize=(10, 6))
plt.plot(df['column_name'])
plt.title('Sample Visualization')
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.show()

Saving Your Work

Jupyter dey auto-save from time to time
Save manually: Press Ctrl + S (or Cmd + S for macOS)
Your work go dey save inside .ipynb file

Using the Quiz Application

Running the Quiz App Locally

# Navigate to quiz app directory
cd quiz-app

# Start the development server
npm run serve

# Access at http://localhost:8080

Taking Quizzes

Pre-lesson quizzes dey for di top of each lesson
Post-lesson quizzes dey for di bottom of each lesson
Each quiz get 3 questions
Quizzes dey to help you learn, no be to test you too much

Quiz Numbering

Quizzes dey numbered 0-39 (40 quizzes total)
Each lesson dey usually get pre and post quiz
Quiz URLs dey include di quiz number: https://ff-quizzes.netlify.app/en/ds/quiz/0

Common Workflows

Workflow 1: Complete Beginner Path

# 1. Set up your environment (see INSTALLATION.md)

# 2. Start with Lesson 1
cd 1-Introduction/01-defining-data-science

# 3. For each lesson:
#    - Take pre-lesson quiz
#    - Read the lesson content
#    - Work through the notebook
#    - Complete the assignment
#    - Take post-lesson quiz

# 4. Progress through all 20 lessons sequentially

Workflow 2: Topic-Specific Learning

If you wan focus on one topic:

# Example: Focus on Data Visualization
cd 3-Data-Visualization

# Explore lessons 9-13:
# - Lesson 9: Visualizing Quantities
# - Lesson 10: Visualizing Distributions
# - Lesson 11: Visualizing Proportions
# - Lesson 12: Visualizing Relationships
# - Lesson 13: Meaningful Visualizations

Workflow 3: Project-Based Learning

# 1. Review the Data Science Lifecycle lessons (14-16)
cd 4-Data-Science-Lifecycle

# 2. Work through a real-world example (Lesson 20)
cd ../6-Data-Science-In-Wild/20-Real-World-Examples

# 3. Apply concepts to your own project

Workflow 4: Cloud-Based Data Science

# Learn about cloud data science (Lessons 17-19)
cd 5-Data-Science-In-Cloud

# 17: Introduction to Cloud Data Science
# 18: Low-Code ML Tools
# 19: Azure Machine Learning Studio

Tips for Self-Learners

Stay Organized

# Create a learning journal
mkdir my-learning-journal

# For each lesson, create notes
echo "# Lesson 1 Notes" > my-learning-journal/lesson-01-notes.md

Practice Regularly

Make time every day or week to learn
Try finish at least one lesson every week
Go back to review di lessons wey you don do before

Engage with the Community

Join di Discord community
Participate for #Data-Science-for-Beginners Channel for Discord Discord Discussions
Share wetin you don learn and ask questions

Build Your Own Projects

After you don finish di lessons, use wetin you don learn for your own projects:

# Example: Analyze your own dataset
import pandas as pd

# Load your own data
my_data = pd.read_csv('my-project/data.csv')

# Apply techniques learned
# - Data cleaning (Lesson 8)
# - Exploratory data analysis (Lesson 7)
# - Visualization (Lessons 9-13)
# - Analysis (Lesson 15)

Tips for Teachers

Classroom Setup

Check for-teachers.md for detailed guide
Set up shared environment (GitHub Classroom or Codespaces)
Create communication channel (Discord, Slack, or Teams)

Lesson Planning

Suggested 10-Week Schedule:

Week 1-2: Introduction (Lessons 1-4)
Week 3-4: Working with Data (Lessons 5-8)
Week 5-6: Data Visualization (Lessons 9-13)
Week 7-8: Data Science Lifecycle (Lessons 14-16)
Week 9: Cloud Data Science (Lessons 17-19)
Week 10: Real-World Applications & Final Projects (Lesson 20)

Running Docsify for Offline Access

# Serve documentation locally for classroom use
docsify serve

# Students can access at localhost:3000
# No internet required after initial setup

Assignment Grading

Check student notebooks to see di exercises wey dem don complete
Use quiz scores to check understanding
Grade final projects based on data science lifecycle principles

Creating Assignments

# Example custom assignment template
"""
Assignment: [Topic]

Objective: [Learning goal]

Dataset: [Provide or have students find one]

Tasks:
1. Load and explore the dataset
2. Clean and prepare the data
3. Create at least 3 visualizations
4. Perform analysis
5. Communicate findings

Deliverables:
- Jupyter notebook with code and explanations
- Written summary of findings
"""

Working Offline

Download Resources

# Clone the entire repository
git clone https://github.com/microsoft/Data-Science-For-Beginners.git

# Download datasets in advance
# Most datasets are included in the repository

Run Documentation Locally

# Serve with Docsify
docsify serve

# Access at localhost:3000

Run Quiz App Locally

cd quiz-app
npm run serve

Accessing Translated Content

Translations dey available for more than 40 languages:

# Access translated lessons
cd translations/fr  # French
cd translations/es  # Spanish
cd translations/de  # German
# ... and many more

Each translation dey follow di same structure as di English version.

Additional Resources

Continue Learning

Microsoft Learn - More learning paths
Student Hub - Resources for students
Azure AI Foundry - Community forum

Getting Help

Check TROUBLESHOOTING.md for common problems
Search GitHub Issues
Join our Discord
Check CONTRIBUTING.md to report problems or contribute

Disclaimer:
Dis docu don dey translate wit AI translation service Co-op Translator. Even though we dey try make am accurate, abeg sabi say automated translations fit get mistake or no correct well. Di original docu for im native language na di main correct source. For important information, e good make una use professional human translation. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because of dis translation.

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