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Types Of Data Analytics | Best Tools For Data Analysis

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Types Of Data Analytics

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Today we are talking about the types of data analytics. Read this article till the end to find out the list of tools used by EZtek data scientists. On our Blog channel, we share thoughts on recent developments in the tech industry, follow us not to miss new articles.

Stages of Data Analytics

  • Dividing an array of data into subsets
  • Making conclusions of a certain nature from it
  • Transforming by a specific model.

Types of Data Analytics

Analytics allows getting answers to questions arising in various industries. So, what types of data analytics exist? There are several original approaches to analytics that we consider universal, read further to learn more about them.

Types of Data Analytics according to Jeffrey Leek

Jeffrey Leek is an American data scientist working as a professor at John Hopkins Bloomberg school of public health. He is an author of the most used classification which identifies six kinds of analysis.

  • A Descriptive or common method is the basic stage of analytics. Its goal is to describe a set of data.
  • The Exploratory method of analysis is aimed at identifying trends and patterns unknown before.
  • The Inferential approach aims to use a relatively small sample of data to say something about a bigger population. It allows getting statistical conclusions about the nature of objects.
  • The Predictive method’s goal is to use the data on some objects to predict values for another object. It allows making accurate hypotheses about further changes.
  • Causal type of analytics allows finding how changing one parameter will cause changes in others.
  • The mechanistic method is the most challenging. Its goal is to understand the exact changes in variables that lead to changes in other variables for individual objects.

Other Classification

  • Prescriptive analytics that answer the question: What is the best action?
  • Predictive analytics tells us what is likely to happen.
  • Diagnostic analytics aims to answer the question of Why something happened.
  • Descriptive analytics answers the question of What happened.

Classes of Data Analysis Techniques

One-dimensional

Where the descriptor is a single criterion by which data can be searched and sampled.

Multi-dimensional

Which considers two or more descriptors. In turn, multi-dimensional types of analytics are divided into Cluster and Factor analysis.

  • In Cluster analysis, an expert chooses a sample of objects that are within their subsets as close as possible to each other.
  • During the Factor analysis, a scientist considers the largest possible number of parameters to analyze a certain object group. These parameters are combined into groups to get several instead of several dozen variables.

Tools used by Data Scientists

  • Pandas or Apache Spark
    Which offer data structures and operations for manipulating numerical tables and time series.
  • Numpy is a must have tool to effectively handle numerical data.
  • Skippy or Python’s built-in statistics are used for data analysis.
  • Statsmodels provides classes and functions for the estimation of different models
  • Scikit-learn is great for evaluating the accuracy of a classification.
  • Matplotlib, Seaborn or Plotly are used for creating statistical, animated and interactive visualizations.

We use analytical data processing in all areas of activity to provide diagnostics of possible problems, track changes and develop forecasts. Data science is a complex field of knowledge that requires trained and experienced staff.
This article was prepared by the EZtek team. We provide software development, UI/UX design, QA and testing services to top brands worldwide.

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