NodeBook Private Limited

Data Analysis with Power BI

Data Analyst with Microsoft Power BI

Microsoft Power BI is the industry-leading business intelligence platform with more than 115 million users — and its growing. In 2020, the Power BI market represented more than $20 billion, and is estimated to double by 2026. 50,000 companies worldwide use Power BI to clean, transform, model, and visualize their data. Power BI usage has been growing quickly, with over 80,000 open jobs on LinkedIn.
By learning Microsoft Power BI, you can accelerate your career and become a data professional. You will also get ready for Microsoft PL-300 exam (an updated version of the DA-100 exam).

No prior knowledge is required. Learning will start from scratch.

Course Title Data Analyst with Microsoft Power BI
Days per week 1 Session per week
Number of hours per week 3 hours per week
Total study time 8 classes – 24 credit hours (2 Months)
  • Advance Excel
  • Working with data in excel
  • Data Analysis in Excel
  • Data Visualization in Power BI
  • Power Queries and Editor
  • Data Modeling in Power BI
  • DAX in Power BI
  • Power BI Service.
  • Creating Dashboards
  • Data Connections in Power BI.

Working with Data in Excel

  • Entering data and formatting cells
  • Basic formulas and functions
  • Sorting and filtering data
  • Conditional formatting
  • Data validation

Advanced Excel Functions

  • Advanced formulas and functions (e.g. VLOOKUP, IF statements)
  • Using Named Ranges and Absolute References
  • Pivot Tables: Creating, formatting, & filtering data
  • Pivot Charts: Creating & formatting pivot charts

Data Analysis in Excel

  • Advanced charting techniques
  • Data visualization techniques
  • Using Histograms for data visualization
  • Data analysis tools (e.g. Goal Seek, Solver)
  • Scenario Manager: Creating and managing scenarios
  • Descriptive statistics: Mean, Median, Mode, Variance, and Standard Deviation

Collaborating in Excel

  • Sharing and protecting workbooks
  • Tracking changes and comments
  • Collaborating with OneDrive and SharePoint
  • Exporting data and reports

Introduction to Power BI and Connect to Data

  • Getting started with Power BI Desktop
  • Explore Power BI tools.
  • Connect to Data Sources in Power BI Desktop
  • How to Import Excel Workbooks and CSV Files.
  • How to analyze Services Tabular Data.
  • Connect to Web data.
  • Importing and analyzing data from a web page.
  • Analyzing sales data from Excel and an OData feed
  • Bringing your own data and analyze it in Power BI.

Data Visualization in Power BI

  • Explore all visualization tools.
  • Add data to your visual.
  • Format your report page.
  • Add further analysis to your visual.
  • Apply filters to your visualization.

Working with Reports

  • Discuss report view / sort by column.
  • Tips and tricks for creating reports.
  • Import and display KPIs (Preview)
  • Creating reports on Finances of a company.


Power Query and Combine Data

  • Getting started with Power Query Editor
  • Query Overview
  • Data categorization
  • Column and Row management
  • Data types
  • Statistics, Standard and Scientific operations.

Data modeling

  • Relationship/Model view
  • Create and manage relationships in Power BI Desktop

Data Fundamentals and DAX Functions

  • Data Type in Power BI Desktop
  • DAX Basics
  • Measures in Power BI Desktop
  • Create your own measures
  • Calculated columns
  • Create calculated columns
  • Difference between Calculated Measures and Calculated Columns

Context in DAX Formulas

  • Introduction to Context
  • Row Context
  • Filter Context
  • Iterator Function
  • Calculate Function
  • Variables
  • Variable in conjunction with calculate Function

The Date Table in DAX

  • Calculated tables
  • Working with dates
  • Importance of date table
  • Creating date table
  • Adding column inside a table
  • Connecting columns of tables

Quick Measures in DAX

  • Explore quick measures pane
  • Working with all calculation’s tools in quick measures
  • Aggregate, filters and time intelligence functions, etc.

Power BI Service

  • The data flow process
  • Power BI desktop connections
  • Welcome to Power BI Services
  • Desktop vs Services

Workspaces in Power BI Service

  • Creating apps and workspace
  • Templates vs organizational apps
  • Workspace defaults

Securing Content in Power BI

  • Securing Datasets
  • Row level security benefits
  • Manage dataset permissions
  • Matching and Granting permission on activities.
  • Enabling report discover
  • Personalize a report
  • Personalizing a visual

Dashboards

  • Importance of Dashboards
  • How to create and manage dashboards in Power BI
  • Configuring Dashboards

Sharing your Work

  • Steps to publish an app
  • Publish from Power BI Desktop
  • Share a dashboard from Power BI
  • Exploring Power BI Mobile

Working with SQL server

  • Connect to SQL Data Sources in Power BI Desktop.
  • How to import and analyze SQL Data.

Working with Microsoft Azure

  • Connect to Azure Database in Power BI Desktop
  • How to create database in Azure
  • How to import, connect and analyze data from Azure Database.

Social Media Analysis

  • Social Media Platforms
  • How to extract data from LinkedIn and Facebook
  • LinkedIn Analysis
  • Facebook Analysis

Bravo with Power BI

  • Introduction to Bravo
  • Connecting Power BI with Bravo
  • Analyzing datasets
  • Formatting DAX formulas
  • Date Tables and Time Intelligence in Bravo
  • Exporting datasets from Bravo

Data source settings and M Language

  • Discovering M Language
  • Concepts of Lists and Records
  • How to create lists and records
  • How to extract data from lists and records.

Power bi Mobile

  • How to work for Power BI Mobile Interface
  • Formatting for Mobile Interface
  • Tool and Editing options
  • Kevin Cookie Company Financials
  • Apply relationship and Predicting Gold and other Commodities (2000 – 2022)
  • Applying DAX and Evaluating Adventure Works (Bikes) Sales
  • Loan Prediction in a Bank
  • In-Depth Analysis of Information Technology (IT) Expenditure Dataset
  • Predictive Analysis for Retail Sales Trends
  • Comprehensive Facebook Engagement Analytics
  • Thorough LinkedIn Performance Analytics
  • Retrieving Data from a SQL Server Database
  • Analyzing Employee Information Data from Microsoft Azure SQL Server
  • Publishing Comprehensive Financial Analysis for the Company
  • Creating Dashboards on Global Super Store dataset.

Overview

Course Modality

Course Duration

Course

Course Support

Course Language

Trainer Info

Wardah Arshad – Trainer Nodebook Private Limited
Ziauddin University Lecturer: Specializes in Python, Data Analysis, Data Structures. 4 years experience as Data Scientist, Analyst, ML expert.

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