Data is everywhere, and it's the key to identifying patterns, making projections, and resolving problems, but often the data is complicated and difficult to understand. That's where data analysis comes into play. Using various methods, it's possible to analyze data and translate the valuable information into something more useful. Even the terminology relating to data analysis and statistical analysis is slightly confusing for the uninitiated, which is why learning different data analysis definitions is a good starting point for anyone keen to learn more.
Data vs. Statistics
To understand the definition of data analysis, you first need to understand the definitions of "data" and "statistics." You may have heard people use the two terms interchangeably, but if you're planning to become a data analyst, it's important to know what makes each term distinct:
- Data is raw information.
- Statistics are the results of data analysis.
Simply put, data analysis is the process of interpreting and presenting raw data as statistics. Outputting statistics as graphs is common, as visual elements are easier to understand. The statistics are useful for statistical analysis in research methods, where experts apply statistical processes to derive understanding of a total population from selected samples. For example, it's possible to use statistics to make business projections, or to identify which demographic is most likely to purchase a product.
Why Is Data Analysis Important?
Raw data contains all the information necessary for business owners and managers to make the important decisions that make or break a company, but that information is often difficult to understand for anyone who doesn't have the correct technical knowledge. In some cases, data may be impossible to decipher without a computer program to decode it, making the data useless in its original form. Analysis is essential, because it comprises the various calculations and computations that translate the data into a format suitable for use by non-technical decision-makers.
Data Analysis Techniques
Preparing data involves numerous methods. The following definitions cover some of the most commonly related terms:
- Data mining: Processing large data sets to identify patterns and predict trends. For example, companies may data mine customer databases to find out which types of people buy certain products.
- Business intelligence: The collection, analysis, and presentation of business information.
- Data visualization: Translating raw data into a visual format such as a graph to aid comprehension. This includes graph visualization, which is representing connected data as a link and node model to more easily identify relationships.
- Text mining: Using computers to parse text for data.
Improving Your Data Basics
Now that you know the different data analysis definitions, it's time to expand your knowledge even further. Knowing more about analytical techniques has the potential to improve your decision-making processes, improve efficiency within a company, or lay the foundations for a career as a data scientist. If you want to know more, browse the Udacity Catalog to discover a range of courses covering the core concepts of data science and data analysis.