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Difference Between Data Analytics And Data Analysis

Data Analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. 

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

Difference Between Data Analytics And Data Analysis

The difference between analysis and analytics is that analytics is a broader term for which analysis forms a subcomponent. it refers to the process of compiling and analyzing data to support decision making, whereas analytics also includes the tools and techniques used to do so.

Data Analysis 

  • is a specialized form of data analytics used in businesses to analyze data and take some insights into it.
  • consisted of defining a data, investigation, cleaning, transforming the data to give a meaningful outcome.
  • he data OpenRefine, KNIME, RapidMiner, Google Fusion Tables, Tableau Public, NodeXL, WolframAlpha tools are used.
  • The sequence followed in it is gathering, scrubbing, analysis of data and interpret it precisely so that you can understand what your data want to say.
  •  can be used in various ways like one can perform analysis like descriptive, exploratory, inferential, predictive analysis and take useful insights.

Data analytics

  • is the general form of analytics which is used in businesses to make decisions from data which are data-driven.
  • consist of data collection and inspect in general and it has one or more users.
  • There are many analytics tools in a market but mainly R, Tableau Public, Python, SAS, Apache Spark, Excel are used.
  • the life cycle consists of Business Case Evaluation, Data Identification, Data Acquisition & Filtering, Data Extraction, Data Validation & Cleansing, Data Aggregation & Representation, Analysis, Visualization, Utilization of Analysis Results.
  • can be used to find masked patterns, anonymous correlations, customer preferences, market trends and other necessary information that can help to make more notify decisions for business purpose.

analysis is a sub-component of analytics is specialized decision-making tool which uses different technologies like tableau public, Open Refine, KNIME, Rapid Miner etc. and are useful in when performing exploratory analysis and produce some insights from data using a cleaning, transforming, modeling and visualizing the data and produce outcomes.

Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources and can be integrated with new products and systems as they are brought on-line. When implemented on high-performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions

analytics tools are used by Data Analysts

  • Tableau Public
  • OpenRefine
  • KNIME
  • RapidMiner
  • Google Fusion Tables
  • NodeXL
  • Wolfram Alpha.
  • Google Search Operators.
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