Analytics in Industry 4.0
e-Attendance
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- Click this link
- Get your Student ID
- Class ID: 32721
- User Guide
Course Outline
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
Extra Resources
Pre-Assessment
Click here for Pre-Assessment
Post-Assessment
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Data Visualisation Begins with Me
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- Get your Student ID
- Class ID: 32725
- User Guide
-
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
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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
Click here for Pre-Assessment
Dataset:
- Download Financial-Data Files.
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:
- Download Simple Financial Data File.
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:
- Download Maven Market Files
Click here for Post-Assessment
Quantitative Data Reasoning
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- Get your Student ID
- Class ID: 32731
- User Guide
Summary Outline
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- 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
Click here for Pre-Assessment
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
Click here for Post-Assessment