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The Data Science Directory

Advanced/Data Analytics refers to knowledge, technologies and processes that help analyze big data. They are generally more advanced than methods and knowledge used in traditional data analysis, and fall into three categories: descriptive, predictive and prescriptive

Sneharaghunath
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The Data Science Directory

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  1. Advanced/Data Analytics refers to knowledge, technologies and processes that help analyze big data. They are generally more advanced than methods and knowledge used in traditional data analysis, and fall into three categories: descriptive, predictive and prescriptive. ● Big Data refers to large, complex volumes of data that require advanced analytics for interpretation.

  2. ● Data Analysis refers to traditional methods – statistical, mathematical and logical - used to interpret data. ● Data Wrangling is the process of converting complex data into simpler forms. ● Deep Analytics is the kind of analytics that helps interpret events and outcomes in great depth. It is typically descriptive in nature. ● Descriptive Analytics is the type of analytics that interprets and explains data using statistical concepts. ● Exploratory Analysis is the step in the data science journey that seeks to formulate hypotheses. Visualization is an important part of this step.

  3. ● A Feature is a part of your data set that demonstrates a specific characteristic or trait. ● Predictive Analytics is the type of analytics that uses advanced analytics to reason and forecast future events or outcomes. ● Prescriptive Analysis is the type of analytics that suggests optimal solutions for better decision-making. ● Production Code is the source code used repeatedly by a variety of people. ● Product Requirements Document (PRD) is a document that outlines what features and functionalities should be developed in a product.

  4. ● Statement of Work (SoW) is a document that outlines the schedule and objectives to be achieved in a project. ● Target Variable describes the desired outcome in machine learning. It can either be present in the data set, or must be constructed separately by the data scientist.

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