Data Collection in Data Science

Today we are going to discuss data collection in data science. Data collection is a crucial step in the data science process, as the quality and quantity of data collected can have a significant impact on the accuracy and validity of our analyses.

To start, let’s consider a case study from the book “Python for Data Science Handbook” by Jake VanderPlas. In Chapter 2, VanderPlas presents a case study of analyzing the iris dataset, a classic dataset in the field of machine learning. The dataset contains measurements of the sepal length, sepal width, petal length, and petal width for 150 iris flowers, with 50 flowers from each of three species.

The iris dataset is a good example of well-curated and readily available data. However, in many cases, we may need to collect our own data. For instance, in the book “Data Science from Scratch” by Joel Grus, Grus presents a case study of predicting the prices of Airbnb listings. To do this, Grus collected data on Airbnb listings in New York City using the Airbnb API, which allows developers to access Airbnb data in real-time. Grus collected data on the price, location, amenities, and other features of over 40,000 Airbnb listings in New York City.

Collecting data can be a challenging process, and there are several considerations to keep in mind. One important consideration is data privacy and ethics. It is essential to obtain consent from individuals whose data we are collecting, and to handle the data responsibly and securely. Another consideration is the quality of the data. To ensure high-quality data, we may need to clean and preprocess the data before using it in our analyses. This may involve removing missing or duplicate values, dealing with outliers, and transforming the data to a more suitable format.

In addition to collecting our own data, we can also use existing datasets that are publicly available. There are several sources of publicly available datasets, including government agencies, academic institutions, and research organizations. For example, the UCI Machine Learning Repository is a popular source of datasets for machine learning research.

Data collection involves obtaining data from various sources, such as websites.

Web scraping is the process of extracting data from websites using software. BeautifulSoup is a Python library that allows us to parse HTML and XML documents and extract data from them. Let’s consider two examples of web scraping using BeautifulSoup and Requests Python Libraries.

In the first example, we will extract data from a website that contains information about books. We will extract the title, author, and price of each book. Here is the code:

Code Block 1.0 – Web Scraping

   import requests
   from bs4 import BeautifulSoup

   # URL of the website
   url = 'https://www.example.com/books'

   # Request the HTML page
   response = requests.get(url)

   # Parse the HTML page
   soup = BeautifulSoup(response.content, 'html.parser')

   # Find all the book items
   book_items = soup.find_all('div', class_='book-item')

   # Extract the data from each book item
   for book_item in book_items:
       title = book_item.find('h2', class_='title').text
       author = book_item.find('p', class_='author').text
       price = book_item.find('p', class_='price').text
       print(title, author, price)

In the second example, we will extract data from a website that contains information about movies. We will extract the title, year, and rating of each movie. Here is the code:

Code Block 2.0 – Web Scraping

   
   import requests
   from bs4 import BeautifulSoup

   # URL of the website
   url = 'https://www.example.com/movies'

   # Request the HTML page
   response = requests.get(url)

   # Parse the HTML page
   soup = BeautifulSoup(response.content, 'html.parser')

   # Find all the movie items
   movie_items = soup.find_all('div', class_='movie-item')

   # Extract the data from each movie item
   for movie_item in movie_items:
       title = movie_item.find('h2', class_='title').text
       year = movie_item.find('p', class_='year').text
       rating = movie_item.find('p', class_='rating').text
       print(title, year, rating)

n both examples, we use the requests library to request the HTML page from the website and the BeautifulSoup library to parse the HTML page and extract the data we need. We use the find_all method of BeautifulSoup to find all the items of interest and the find method to find a specific item.

In conclusion, data collection is a critical step in the data science process, and it is essential to collect high-quality data that is both relevant and ethical. Whether we are collecting our own data or using publicly available datasets, it is important to consider the quality, privacy, and ethics of the data we are using. By keeping these considerations in mind, we can ensure that our data science projects are robust and reliable.