Power BI Assignments

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Senthil Kumar Kanagaraj Dec 03 2020 · 4 min read
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This is my first assignment as part of the iNeuron Business Data Analytics course. Here we are going to cover the basics of Power BI and Why are we using Power BI ahead of other Tools and small hands-on in PowerBI

What is Power BI?:

Power BI is data visualization tool which help to understand the insights of the data which we take it from various data sources irrespective of structed or no-structured data and make the business decision from that and present the detail evaluation to the stakeholders and higher managements. Power BI is a collection of software services, apps and connectors that work together to turn our raw data into useful information. Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with anyone or everyone you want.

Why Power BI?

Power BI is easy, fast, and robust, which makes it possible even for the non-technical people to analyze the data visually and share the same. Moreover, the tool empowers Business Intelligence in a way that it reduces the distance between data and insights. 

Power BI Key Features

1. Interactive Power BI desktop

2. Customized Visualization

3. Visibility

Power BI- Top Three Components

1. Power Pivot

2. Power Query

3. Power View

Assignment - 1

In this assignment we are performing the below steps.

1. Connecting a Sky Transport excel sheet with the Power BI

2. Hiding the columns Category ID and Origin State from the report 

3. Create a table showing by Customer Name

         1. Revenue

         2. Total Miles

        3. Trip Type

4. Summarize the Revenue & Total Miles

5. Save this Power BI file with the name SkyData.

First we have to load the data by clicking on the Import data from Excel icon on the screen.

                                                      1. Power BI Home Screen

Select the Sky Transport excel sheet from the respective path and open it on the Power BI Application. Once its opened, Navigator window will get opened and the table from the Sky Transport excel sheet get displayed on the screen. By selecting the checkbox besides the Trips table, user can see the data gets displayed on the Navigator window along with Load and Transform Data button gets enabled. 

                                                                 2. Select the DataSet
                                                                              3. Load DataSet

By clicking on the load button, Trips data gets loaded in the Power BI application and the table and columns details are displayed under the fields section.

                                                      4. Trips table view in Power BI

To hide the columns from the table, user need to navigate to Data layout by selecting Data icon from left side of the screen. Right click on the respective columns and select "Hide in Report View" option. After selecting "Hide in Report View", the selected columns should grayed out and the columns will be disappeared from the Fields section.

                                                           5. Hide Columns from the DataSet
                                  6. Hidden Columns were removed from the Fields Section

To create a better visualization we create a table with four fields and create few charts for better understanding. To create a table need to select the 'table' icon from the visualization section and select the columns 'Customer, Revenue, Total Miles and Trip Type'. After selecting the column we could see the data automatically updated in the table.

In this report we get to see the Sky transport data report, we can see Total miles covered  and total revenue of Sky Transport data.

Table which contains columns like  Customers Revenue ,   Total Miles , Trip Type  provides information to the stakeholders so that they get to know about their customers.

A visualization 'Pie Chart' clearly shows the total miles covered (13.76%) and total revenue generated (86.24%). 

Bar chart show the total revenue earned by the customer.

                                                   7. Final Report of the Assignment 1

Assignment - 2


The Key Performance Indicators (KPIs) are crucial metrics that evaluate and measure the performance of a business. KPIs reflect the way that a business is performing in terms of key parameters that every manager should identify. These parameters should be easy to understand to create the most effective meanings for all levels of a company and to evaluate whether the strategy in on track.

As a data scientist or data analyst, not only we should measure the performance of our models, we also pay attention to the performance of our projects identifying, first the best metrics, and second measuring the project KPIs

Data Set

Data set we are going to use is Sky Transport, which contains the data from 2016 and in contains various parameters. From that we are going to derive the KPIs for better understanding and analyze he key data insights.

                                                          1. Dataset used for KPI Analysis

KPIs Analysis

The KPIs analyzed are the following:

  • KPI for Total Miles, Revenue, and Cost.
  • Revenues vs Miles by Shipping State.
  • Revenue vs Trips by Trips Types.
  • Total Trips vs Tripps by Tips Types.
  • % of Trips by Shipping State.
  • Revenue Miles vs Total Miles
  •                                                     1. Trip Data Visualization I

    KPI for Total Miles, Revenue, and Cost

    KPI have been calculated for Revenue  based on the Shipping cost by Total miles. We got a positive positive KPI score where you can see the below.

    KPI analysis based on the Revenue, Shipping Cost and Miles

    The conclusions of the analysis shown in the figure above are the following.

  • KPI for Total Revenue, Total Shipping Cost and Total Revenue - Three of the data cards shows, the total revenue, total shipping and total revenue achieved by the transport. To show the single data we should select the Card icon from the Visualizations section. We could see the total revenue as 8740K, total shipping cost as 7943K and the total miles covered 1395K.
  • The clustered bar chart show the Revenue and Total Miles by Shipping City. It shows the revenue and total miles covered based on the Shipping City.
  • One of the Pie Chart shows the Total Miles covered based on the Trip Type. Domestic trip covered 61.5% (2/3rd) of the total miles covered. After that International trips covered the 2nd most miles of 33.8% followed by Intercom covered 4.67%.
  • 2nd Pie chart shows the Trip % by shipping State details. Illinois covered the maximum trip covered with 33.45%.
  • One of the Line and Clustered column chart contains Shipping Miles and Total Miles details. Tool tip shows, Total Miles covered as 61.39% followed by 38.61% which is for Shipping Miles.
  • Donut chart contains the data of Total Miles Revenue and Shipping Cost.
  • Stacked Bar Chart contains the visualization data of Revenue and Total Miles by Shipping States. Illinois coming up in the first place with the total revenue value as 2971250 and total miles covered with 476289 followed by Michigan with revenue 1882710 and miles covered with 284027.
  • Line chart contains the Revenue by Trip Type whereas Domestic trip provided high revenue value and Intercom gave less revenue. 
  •                                                               3. Trip Data Visualization - II
                                                              Total Trips vs Tripps by Tips Types

    To create a chart, one must select the respective chart from the Visualization section, after that need to select the data from the Fields section. This fields go as X-Axis, Y-Axis or Shared Axis depending on the visuals created by the user.

    To make the better visuals one can change the properties by selecting Format icon from the Visualizations section and do the necessary changes like change font, text size, color etc.

    This is my understanding and the visualization on the assignment. Please read and suggest me for better visualization and ideas.

    Thank you iNeuron, Amit and Teams :)

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