Data 1

What you will learn

Key steps in Data Science

Here are the key steps in data science:

Import Data: Begin by bringing your data into R, which is usually stored in files, databases, or web APIs.
Tidy Data: Organize your data consistently, making sure each column represents a variable and each row corresponds to an observation.
Transform Data: Refine your data by focusing on specific observations, creating new variables, and calculating summary statistics.
Visualization: Use visualizations to uncover unexpected patterns, prompt new questions, and gain insights. However, visualizations may be limited by human interpretation.
Modeling: Employ mathematical or computational models to answer well-defined questions. Models can scale well and provide quantitative insights, but they depend on assumptions.
Communication: Effectively convey your findings to others through clear communication. This step is essential to ensure that insights are understood and actionable.

The importance of data cleaning

A significant portion of the data analysis process involves tasks such as cleaning, transforming, and merging datasets to ensure that the data is in a usable and accurate form.

This survey and the article reveal that data preparation to be both the most time-consuming and the least enjoyable task in the data science process. Data professionals spend around 80% on tasks related to cleaning, organizing, and transforming data before it can be analyzed.

Garbage In, Garbage Out

GIGO is a concept in computer science and data analysis that emphasizes the idea that if you input poor quality or incorrect data into a system, the output or results produced by that system will also be of poor quality or incorrect.

This principle underscores the importance of ensuring accurate, reliable, and well-structured data for any data analysis or computational process to yield meaningful and accurate results.