LearnWithMey
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  • Introduction
    • What and Why Data Analytics?
    • Data Analyst Roadmap
    • Data Analysis Steps
    • Type of Data Analytics
    • Data Analytics Use Cases
    • Data Analytics Tools
    • Skills Requirement
    • Data Analyst vs Data Scientist
      • Key notes
  • STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
    • Chapter 1: What is Statistics?
      • 1.1 What is meant by Statistics?
      • 1.2 Types of Statistics?
      • 1.3 Types of Variables
      • 1.4 Levels of Meaurement
      • Chapter Summary
    • Chapter 2: Describing Data
      • 2.1 Introduction
      • 2.2 Constructing a Frequency Table
      • 2.3 Constructing the Frequency Distributions
    • Chapter 3: Describing Data - Numerical Measures
      • 3.1 Introduction
      • 3.2 The population Mean
      • 3.3 The Sample Mean
      • 3.4 Properties of the Arithmetic Mean
      • 3.5 The Weighted Mean
      • 3.6 The Median
      • 3.7 The Mode
      • 3.8 The Geometric Mean
      • 3.9 Why Study Dispersion?
      • 3.10 Measures of Dispersion
      • 3.11 Interpretation and Uses of the Standard Deviation
      • 3.12 The Mean and Standard Deviation of Grouped Data
    • Chapter 4: Describing Data - Display and Explore Data
      • 4.1 Introduction
      • 4.2 Dot Plots
      • 4.3 Stem-and-Leaf Displays
      • 4.4 Measures of Position
      • 4.5 Skewness
      • 4.6 Describing the Relationship between Two Variables
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    • Chapter 6: Discrete Probability Distribution
    • Chapter 7: Continuous Probability Distribution
    • Chapter 8: Sampling Methods and Central Limit Theorem
    • Chapter 9: Estimation and Confidence Intervals
      • 9.1 Introduction
    • Chapter 10: One-Sample Tests of Hypothesis
      • 10.1 Introduction
      • 10.2 Hypothesis Testing
    • Chapter 11: Two-Sample Tests of Hypothesis
    • Chapter 12: Analysis of Variance
    • Chapter 13: Correlation and Linear Regression
    • Chapter 14: Multiple Regression Analysis
    • Chapter 15: Index Number
    • Chapter 16: Time Series and Forcasting
    • Chapter 17: Nonparametric Methods: Goodness-of-Fit Tests
    • Chapter 18: Nonparametric Methods: Analysis of Ranked Data
    • Chapter 19: Statistical Process Control and Quality Management
    • Chapter 20: Decision Theory
    • Tables ( Z and Student's T )
  • Python for Data Science
    • Handbook
    • Introduction
    • Installation
    • Basic Syntax
    • 2. Introduction to Numpy
  • Excel for Data Analysis
    • Introduction
  • Data Warehousing
    • Introduction
    • Data Warehouse Architecture
  • Data Visualization with Power BI
    • Introduction
  • Data Analysis with Python
    • Importing Datasets
      • Understanding Data
      • Python Packages for Data Sciences
    • Data Wrangling
      • Pre-processing Data in Python
      • Dealing with Missing Values in Python
      • Data Formatting in Python
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    • 1. Introduction
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  1. Introduction

Type of Data Analytics

PreviousData Analysis StepsNextData Analytics Use Cases

Last updated 1 year ago

Here are some common types of data analytics:

  1. Descriptive Analytics: Descriptive analytics focuses on summarizing and describing historical data to gain an understanding of what has happened in the past. It involves techniques such as data aggregation, data visualization, and basic statistical analysis to answer questions like "What happened?" and "How did it happen?"

  2. Diagnostic Analytics: Diagnostic analytics aims to identify the reasons behind past events or outcomes. It involves analyzing historical data to uncover relationships, patterns, and dependencies. Diagnostic analytics helps answer questions like "Why did it happen?" and "What were the contributing factors?"

  3. Predictive Analytics: Predictive analytics leverages historical data and statistical algorithms to make predictions about future events or outcomes. It involves building models based on historical patterns and using them to forecast future trends or behaviors. Predictive analytics helps answer questions like "What is likely to happen next?" and "What will be the outcome of a specific event?"

  4. Prescriptive Analytics: Prescriptive analytics goes beyond prediction by providing recommendations and optimal courses of action. It combines historical data, predictive models, and optimization techniques to suggest the best possible actions to achieve desired outcomes. Prescriptive analytics helps answer questions like "What should we do?" and "What is the best course of action?"

  5. Diagnostic Analytics: Diagnostic analytics aims to identify the reasons behind past events or outcomes. It involves analyzing historical data to uncover relationships, patterns, and dependencies. Diagnostic analytics helps answer questions like "Why did it happen?" and "What were the contributing factors?"

  6. Text Analytics: Text analytics involves analyzing and extracting valuable insights from unstructured text data, such as customer reviews, social media posts, emails, and documents. It includes techniques like natural language processing (NLP), sentiment analysis, topic modeling, and text classification.

  7. Spatial Analytics: Spatial analytics focuses on analyzing geographic or location-based data. It involves techniques such as geographical information systems (GIS), spatial data mining, and spatial modeling to gain insights into patterns, relationships, and trends in spatial data.

  8. Real-time Analytics: Real-time analytics deals with analyzing and processing data as it is generated in real-time. It often involves stream processing techniques to analyze and act upon data as it flows, enabling immediate insights, alerts, and actions.