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Data Analysis
Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Data analytics is divided into four basic types i.e descriptive analytics describes what has happened over a given period of time, diagnostic analytics focuses more on why something happened, predictive analytics moves to what is likely going to happen in the near term, and prescriptive analytics suggests a course of action.
Data Assets
A collection of data that holds valuable information or
knowledge. This can include databases, CRM systems,
spreadsheets, mailing lists, records of transactions or
bookings, collections/libraries of documents or images.
Data Cleaning
Whenever the data is collected it contains a lot of incorrect, incomplete, irrelevant, or duplicated records. Data cleaning is the process of fixing or removing those incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. It is the one of the most important steps in the data analysis process that helps to improve the quality of data by fixing errors and ensure it is accurate, correct and ready for business use.
Data Collection
Data collection is the process of gathering data for use in business decision-making, strategic planning, research, and other purposes. There are many ways of data collection such as surveys, transactional tracking, interviews and focus groups, observations, online tracking, social media monitoring, etc.
Data Engineering
Data engineering is the designing and building systems for collecting, storing, and analyzing data at scale. Data engineers help an organization collect data from various sources efficiently and effectively and also maintain the flow of data throughout the organization such that a data analyst or data scientist can use it easily for business purposes. Whenever a huge amount of data is collected, data privacy is the other most important thing data engineers take care of.
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