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📚 Principles of Data Science
Chapters ▾
1 What Are Data and Data Science?2 Collecting and Preparing Data3 Descriptive Statistics: Statistical Measurements and Probability Distributions4 Inferential Statistics and Regression Analysis5 Time Series and Forecasting6 Decision-Making Using Machine Learning Basics7 Deep Learning and AI Basics8 Ethics Throughout the Data Science Cycle9 Visualizing Data10 Reporting Results

Chapter 2: Collecting and Preparing Data

A person fills out paperwork at a table under a canopy with “Census 2010” signs, while a diverse team assists.
Figure 2.1 Periodic population surveys such as censuses help governments plan resources to supply public services.Periodic population surveys such as censuses help governments plan resources to supply public services. (credit: modification of work “Census 2010 @ La Fuente” by Jenn Turner/Flickr, CC BY 2.0)

Data collection and preparation are the first steps in the data science cycle. They involve systematically gathering the necessary data to meet a project's objectives and ensuring its readiness for further analysis. Well-executed data collection and preparation serve as a solid foundation for effective, data-driven decision-making and aid in detecting patterns, trends, and insights that can drive business growth and efficiency.

With today’s ever-increasing volume of data, a robust approach to data collection is crucial for ensuring accurate and meaningful results. This process requires following a comprehensive and systematic methodology designed to ensure the quality, reliability, and validity of data gathered for analysis. It involves identifying and sourcing relevant data from diverse sources, including internal databases, external repositories, websites, and user-generated information. And it requires meticulous planning and execution to guarantee the accuracy, comprehensiveness, and reliability of the collected data.