Sprint Challenge
Sprint Challenge Overview
The Sprint Challenge is a comprehensive assessment that allows you to demonstrate your mastery of the concepts covered in this sprint. You will apply your knowledge of Python, pandas, data wrangling, and visualization to solve real-world data problems.
Challenge Setup
To get started with the Sprint Challenge, follow these steps:
- View the Sprint Challenge Setup video above for a walkthrough of the process.
- Access the Jupyter notebook using the link below.
- Complete all tasks in the notebook, demonstrating your understanding of the sprint concepts.
- Submit your completed challenge according to the provided instructions.
Challenge Expectations
The Sprint Challenge is designed to test your mastery of the following key concepts:
- Python fundamentals: Working with basic data types, functions, and loops
- Data wrangling with pandas: Loading, cleaning, and transforming data
- Data exploration: Using pandas methods to gain insights from datasets
- Data reshaping: Pivoting, melting, and joining datasets
- Data visualization: Creating informative plots to communicate findings
What to Expect
The Sprint Challenge will involve working with a real-world dataset and demonstrating your ability to:
- Load and explore the dataset using pandas
- Clean and transform the data to make it suitable for analysis
- Create meaningful visualizations that highlight key insights
- Apply feature engineering techniques to enhance the data
- Draw conclusions based on your analysis
Sample Code
# Example of what you might be asked to do:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load dataset
df = pd.read_csv('challenge_data.csv')
# Data cleaning
df = df.dropna()
df['date'] = pd.to_datetime(df['date'])
df['year'] = df['date'].dt.year
# Feature engineering
df['price_per_sqft'] = df['price'] / df['square_feet']
# Visualization
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='square_feet', y='price', hue='neighborhood')
plt.title('Price vs. Square Footage by Neighborhood')
plt.xlabel('Square Feet')
plt.ylabel('Price ($)')
plt.tight_layout()
plt.show()Sprint Challenge Resources
Use the study guide below to help you prepare for the Sprint Challenge: