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:
ℹ️ Introduction to Data Science Notebooks
You can skip this section if you are already familiar with data science notebooks.
Data science notebooks
A data science notebook is a document containing text cells (what you're reading now) and code cells. What is unique with a notebook is that it's interactive: You can change or add code cells and then run a cell by selecting it and then clicking the Run button to the right ( ▶, or Run All on top) or hitting control + enter.
The result will be displayed directly in the notebook.
Try running the Python cell below:
# Run this cell to see the result (click on Run on the right, or Ctrl|CMD + Enter)
100 * 1.75 * 20Modify any of the numbers and rerun the cell.
You can add a Markdown, Python|R, or SQL cell by clicking on the Add Markdown, Add Code, and Add SQL buttons that appear as you move the mouse pointer near the bottom of any cell.
🤖 You can also make use of our AI assistent, by asking it what you want to do. See it in action here.
Here at DataCamp, we call our interactive notebook Workspace. You can find out more about Workspace here.
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 and numpy packages
import pandas as pd
import numpy as np
# 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?
2️⃣ Part 2 (SQL) - Understanding movie data 🎥
📖 Background
You have just been hired by a large movie studio to perform data analysis. Your manager, an executive at the company, wants to make new movies that "recapture the magic of old Hollywood." So you've decided to look at the most successful films that came out before Titanic in 1997 to identify patterns and help generate ideas that could turn into future successful films.
💾 The data
You have access to the following table, cinema.films:
| Column name | Description |
|---|---|
| id | Unique movie identifier. |
| title | The title of the movie. |
| release_year | The year the movie was released to the public. |
| country | The country in which the movie was released. |
| duration | The runtime of the movie, in minutes. |
| language | The original language the movie was produced in. |
| certification | The rating the movie was given based on their suitability for audiences. |
| gross | The revenue the movie generated at the box office, in USD. |
| budget | The available budget the production had for producing the movie, in USD. |
You can click the "Browse tables" button in the upper right-hand corner of the SQL cell below to view the available tables. They will show on the left of the notebook.
The data was sourced from IMDb.
SELECT *
FROM cinema.films
LIMIT 10💪 Challenge II
Help your team leader understand the data that's available in the cinema.films dataset. Include:
- How many movies are present in the database?
- There seems to be a lot of missing data in the gross and budget columns. How many rows have missing data? What would you recommend your manager to do with these rows?
- How many different certifications or ratings are present in the database?
- What are the top five countries in terms of number of movies produced?
- What is the average duration of English versus French movies? (Don't forget you can use the AI assistant!)
- Any other insights you found during your analysis?