Analytics in Industry 4.0

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Module 1 

  • Understanding Digital Transformation
  • Digital Transformation Framework

Module 2 

  • The Fourth Industrial Revolution or Industry 4.0
  • Key Digital Technologies & their applications or Pillars of IR 4.0

Big Data, Data Analytics and Machine Learning

 

Discussion Link: Click here

 

  • Download Dataset for Loan Prediction.
    • Dataset : KYC

 

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Data Visualisation Begins with Me

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  • Introduction

    • Overview of Basic Sales Analytics Session
    • Bird-eye view of Sales Data
    • Getting Started with Power BI
    • Get Data in Power BI and Develop Relationships
    • Develop Key Calculation Table and Calculate Total Sales
    • Develop Dates Table
    • Calculate Total Cost and Total Profits
    • Calculate Total Unit Sold, Total Products and Profit Margin Percentage
    • Dashboard – Page Background with Title and key cards
    • Dashboard – Slicer of Years and Quarter
    • Dashboard – Total Sales by Location, Dates and Salesperson
    • Dashboard – Product-wise Sales with Map and Review of Sales Performance

 

  • Intermediate Sales Analytics Session

    • Overview of Intermediate Sales Analytics Session
    • Introduction of Intermediate Sales Analytics
    • Product Insight – Calculate Top 5 Products
    • Product Insight – Calculate Year on Year (YoY) Sales Growth
    • Product Insight – Calculate Product Group Table and place it accordingly
    • Product Insight – Create Scatter Chart with Product Growth Groups
    • Product Insight Dashboard – Product Table Visualization
    • Product Insight Dashboard – Scatter Chart and Top Products
    • Product Insight Dashboard – Final Visualization and Product Review
    • Customer Insight – Calculate Top 5 Customers
    • Customer Insight – Time Intelligence Calculations
    • Customer Insight – Customer Ranking in the Scatter Chart
    • Customer Insight Dashboard – Final Visualization and Customer Review
    • Sales Summary Dashboard – Final Visualization and Sales Review
    • Sales Budget – Overview and Utilization of Time Intelligence Technique
    • Sales Budget – Budget Formula and Final Visualization

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Delicious Pizza

Sample Dashboard:

Dataset:

Dataset:

Hints:

Transformation:

  • Check all data type 
  • Create Date table 
  • Create New Measurements
    • Total Sales [Sum of Sales]
    • Total Margin [Sum of Profit]
    • Total COGS [Sum of COGS]
    • Sales vs COGS [Total Sales – Total COGS]
    • Profit % [Total Margin / Total COGS]
    • Average Order [Total Sales / Total Number of Row*(use COUNTROWS Function)]

Modeling : 

  • Create Relationship between Financial & Date table

Your manager wants to see a report on your latest sales figures. They’ve requested an executive summary of:

  • Which month and year had the most profit?
  • Where is the company seeing the most success (by country)?
  • Which product and segment should the company continue to invest in?
  • Top 2 profitable Products.
  • What is the Total sales without discount?
  • Find Country-wise Sales %.
  • What is the Product-wise Profit Margin % ?
  • What is the Year to Date Sales Value?
  • Need to find YoY Sales Growth

Note: 

  • Montana product was discontinued last month. 
  • All Segment Name Need to show in Uppercase

Dataset:

 

The Delicious Pizza and Financial Reporting clients were so impressed by your work that they referred you for another contract. This time you will be working with Maven Market, a multi-national grocery chain with locations in Canada, Mexico and the United States. They are asking your Data Analysis and Visualization expertise to do a report, like below: 

Dataset:

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Quantitative Data Reasoning

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Summary Outline

    • The logic of reasoning quantitatively with data
    • Hypothesis formulation, operationalising constructs
    • Data collection and cleaning
    • Use of descriptive statistics and data visualisation methods; when to use what in which situations
    • The role of probability in reasoning with data
    • Statistical inference – drawing conclusions from our data
    • Using models – how to mathematically represent relationships in our data
    • How to effectively communicate our results
  •  

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Descriptive Statistics

  • A class has a mean score of 65 (μ=65) and a standard deviation of 7 (σ=7). Later 3 points are added to every student’s score. What are the new values for the mean and standard deviation? A class has a mean score of 65 (μ=65) and a standard deviation of 7 (σ=7). Later 3 points are added to every student’s score. What are the new values for the mean and standard deviation?
  • In the birth register maintained by the hospital, one of the columns is the gender of the newborn child. What type of data is this?
  • What symbol is used to denote the mean of a population?
  • Find the variance of the following sample data: 1, 2, 3
  • If the standard deviation of the data is 0.36, what is the variance of this data?
  • The mean of 4 numbers is 28. If three of the numbers are 10, 20, 40, what is the value of the fourth number?
  • What is the median of the following data set? Data: 24, 4, 20, 8, 1, 17
  • What is the mode of the following data set? Data: 24, 4, 20, 8, 1, 17, 4
  • What is the term used to describe the distribution of a data set that has 1 mode?
  • The mean and the standard deviation of two independent equal size groups are as follows: mean(A) = 100, sd(A) = 3, mean(B) = 25, sd(B) = 4. What will be the mean and standard deviation of (A-B) ?
  • Find the Inter-Quartile Range for the following data: 24, 4, 20, 8, 1, 17, 6
  • What is the mode of the data shown in the histogram below?

  • What is the median of the data shown in the Box-and-Whisker plot below?

  • What is the Inter-quartile Range of the data shown in the Box-and-Whisker plot Above?
  • The mean of a set of numbers is 100, the mode is 60 and the median is 80. What is the shape of the distribution (Symmetric/Normal/Right Skewed/Left Skewed)?
  • Calculate the standard deviation of the following set of sample data: 1.1, 2.3, 4.0, 2.3, 1.7

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