Everyone Can Learn Data Scholarship
📖 Background
The second "Everyone Can Learn Data" Scholarship from DataCamp is now open for entries.
The challenges below test your coding skills you gained from beginner courses on either Python, R, or SQL. Pair them with the help of AI and your creative thinking skills and win $5,000 for your future data science studies!
The scholarship is open to secondary and undergraduate students, and other students preparing for graduate-level studies (getting their Bachelor degree). Postgraduate students (PhDs) or graduated students (Master degree) cannot apply.
The challenge consist of two parts, make sure to complete both parts before submitting. Good luck!
💡 Learn more
The following DataCamp courses can help review the skills to get started for this challenge:
1️⃣ Part 1 (Python) - Dinosaur data 🦕
📖 Background
You're applying for a summer internship at a national museum for natural history. The museum recently created a database containing all dinosaur records of past field campaigns. Your job is to dive into the fossil records to find some interesting insights, and advise the museum on the quality of the data.
💾 The data
You have access to a real dataset containing dinosaur records from the Paleobiology Database (source):
| Column name | Description |
|---|---|
| occurence_no | The original occurrence number from the Paleobiology Database. |
| name | The accepted name of the dinosaur (usually the genus name, or the name of the footprint/egg fossil). |
| diet | The main diet (omnivorous, carnivorous, herbivorous). |
| type | The dinosaur type (small theropod, large theropod, sauropod, ornithopod, ceratopsian, armored dinosaur). |
| length_m | The maximum length, from head to tail, in meters. |
| max_ma | The age in which the first fossil records of the dinosaur where found, in million years. |
| min_ma | The age in which the last fossil records of the dinosaur where found, in million years. |
| region | The current region where the fossil record was found. |
| lng | The longitude where the fossil record was found. |
| lat | The latitude where the fossil record was found. |
| class | The taxonomical class of the dinosaur (Saurischia or Ornithischia). |
| family | The taxonomical family of the dinosaur (if known). |
The data was enriched with data from Wikipedia.
# Import the pandas,numpy,seaborn and matplotlib packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the data
dinosaurs = pd.read_csv('data/dinosaurs.csv')# Preview the dataframe
dinosaurs💪 Challenge I
Help your colleagues at the museum to gain insights on the fossil record data. Include:
- How many different dinosaur names are present in the data?
- Which was the largest dinosaur? What about missing data in the dataset?
- What dinosaur type has the most occurrences in this dataset? Create a visualization (table, bar chart, or equivalent) to display the number of dinosaurs per type. Use the AI assistant to tweak your visualization (colors, labels, title...).
- Did dinosaurs get bigger over time? Show the relation between the dinosaur length and their age to illustrate this.
- Use the AI assitant to create an interactive map showing each record.
- Any other insights you found during your analysis?
The dataset have a 4951 rows and 12 columns
#the shape of a dataset
print(dinosaurs.shape)# Display basic information about the dataset
print("Dataset Information:")
print(dinosaurs.info())# Summary statistics
print("\nSummary Statistics:")
print(dinosaurs.describe(include='all'))
- The count plot reveals that herbivorous dinosaurs dominate, with over 2000 instances, making them the most abundant diet category among the dinosaurs in the dataset.
# Count of dinosaurs by diet
plt.figure(figsize=(10, 6))
sns.countplot(data=dinosaurs, x='diet', order=dinosaurs['diet'].value_counts().index)
plt.title('Count of Dinosaurs by Diet')
plt.xlabel('Diet')
plt.ylabel('Count')
plt.show()Answer to the first question
How many different dinosaur names are present in the data?