![]() Data engineers build systems that collect, manage, and convert raw data into usable information. Data engineeringĭata engineering is the process of making data accessible for analysis. It’s important to make sure you have clean data prior to analysis because unclean or dirty data can lead to inaccurate conclusions and misguided business decisions. When cleaning your data, you verify that your data is accurate, complete, consistent, and unbiased. Data cleaningĭata cleaning, cleansing, or scrubbing is the process of preparing raw data for analysis. Data architects design the blueprints that organizations use for their data management systems. This can include all touchpoints in the data lifecycle, including how the data is gathered, organized, utilized, and discarded. Prescriptive analytics tell us how to actĭata architecture, also called data design, is the plan for an organization’s data management system. Predictive analytics tell us what will likely happen in the future There are four key types of data analytics:ĭescriptive analytics tell us what happenedĭiagnostic analytics tell us why something happened Data analysts, data scientists, and data engineers are all data analytics professionals. Data analytics encompasses data analysis (the process of deriving information from data), data science (using data to theorize and forecast) and data engineering (building data systems). Data analyticsĭata analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making. Dashboards are typically connected to databases and feature visualizations that automatically update to reflect the most current data in the database. DashboardĪ dashboard is a tool used to monitor and display live data. This is a common file type when downloading data files for analysis, as it tends to be compatible with common spreadsheet and database software. CSV (comma-separated values) fileĪ CSV file is a text file that separates pieces of data with commas. ![]() Data cleaning, an important step in the data analysis process, involves checking your data for inaccuracies, inconsistencies, irregularities, and biases. ![]() Clean dataĬlean data is data that is accurate, complete, and ready for analysis. This can be helpful in the event that you need to return to your original data or recall how you prepared your data for analysis. ChangelogĪ changelog is a list documenting all of the steps you took when working with your data. Labeling columns clearly and precisely can enable you to keep your data organized and ready for analysis. When working in a spreadsheet or database, an attribute is a common descriptor used to label a column. You’ll find common data analysis terms in the glossary below. ![]()
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