LearnWithMey
  • 👩‍💻Getting started
  • 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
    • Chapter 5: A Survey of Probability Concepts
    • 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
  • Machine Learning
    • Page
    • 1. Introduction
    • Machine Learning Exercises
  • React
    • Lazy Loading
  • Search Tips
  • Secure Product Design
  • Google like a pro
  • Project Management
  • SEO in React.js
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  1. Introduction

Data Analytics Use Cases

PreviousType of Data AnalyticsNextData Analytics Tools

Last updated 1 year ago

Nowadays, there are many sector interested in Data Analytics especially Business field:

  1. Descriptive Data Analytics:

  • Year-over-year pricing change

  • Month-over-month sales growth

  • The number of users or the total revenue per subscriber

  • Many more…

  1. Diagnosis Data Analytics:

  • Explore Market Demand

  • Understand Customer Behavior

  • Improve Company Culture

  1. Predictive Data Analytics(Example of Run a hotel):

  • Want to predict how many of rooms will be occupied next week?

  • How: identifying patterns in historical data and then + using statistics to make inference

  1. Prescriptive Data Analytics:

  • Recommendation Engine

  • Simulation

The Data Analytics Lifecycle outlines how data is created, gathered, processed, used, and analyzed to meet corporate objectives. It provides a structured method of handling data so that it may be transformed into knowledge that can be applied to achieve organizational and project objectives:

  • Phase 1: Discovery

    The data science team learn and investigate the problem. Develop context and understanding. Come to know about data sources needed and available for the project. The team formulates initial hypothesis that can be later tested with data.

  • Phase 2: Data Preparation

    Finding datasource, then team start to execute, load and transform to get data into work area ( ELT ). Data preparation tasks are likely to be performed multiple times and not in predefined order. Several tools commonly used for this phase are – Hadoop, Open Refine, Excel, etc.

  • Phase 3: Model Planning

    Team explores data to learn about relationships between variables and selects the most suitable models. Prepare data sets for training, testing, and production purposes. Several tools commonly used for this phase are – Matlab, STASTICA, SAS.

  • Phase 4: Model Building

    Execute model with testing, training dataset. Team also considers whether its existing tools will be enough for running models or they need more robust environment for executing models. Free or Open Source Tools – Matlab , WEKA.

  • Phase 5: Communication Results

    Remember the goal you had set for business in phase 1, Now it is time to check if those criteria are met by the tests you run in the previous phase. Communication step start with collaboration with stakeholders to determine if project result are success or failure. Team should identify key findings of analysis, estimated business value associated with result , and produces narrative to summarize and convey findings to business owner

Factors influencing customer behavior