Data Analytics using Microsoft Power BI

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•  Power BI  1
•  Different Products of Power BI 
•  Python Projects Assignments
•  Power Query - Introduction 
•  Import data into Power Query 
• Assignments 

•  Fundamentals of M Language  Part 1
•  Fundamentals of M Language  Part 2
•  Calculating ratio inside Power Query 
•  Projects 
• Assignments  

•  Power View in Power BI 
•  Power BI Desktop Tool 
•  Visualizations and PBI Canvas 
•  PBIX and PBIT File Formats 
•  Visual Interactions in Power BI 
•  Power View- Hierarchies 
•  Assignment
•  Quiz 

•  Internship Project 1 - 2 
• Internship Project 3 - 4
•  Report Writing 
• GitHub
•  Technical Interview Questions
•  Quiz 2
•  Assignment
•  Quiz 3 

•  Resume Building
•  Technical Mock Interview
•  LinkedIn
•  HR Mock Interview
•  Task Submission 

Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making.  

Power BI is a suite of Business Intelligence(BI), reporting, and data visualization products and services for individuals and teams. Power BI stands out with streamlined publication and distribution capabilities, as well as integration with other products and services.

• Descriptive analytics tell us what happened.
• Diagnostic analytics tell us why something happened.
• Predictive analytics tell us what will likely happen in the future.
• Prescriptive analytics tell us how to act.

In a constantly changing business environment, it may be hard to predict your next move. That’s where data analytics comes in. By quickly accessing data across teams and the enterprise, you can drive better decisions by getting deeper insights about:

• Who your customers are and how to reach them
• The market, including competitors
• What has happened in the past
• What’s happening now
• What the future holds for your business

Data Analytics and Data Science are closely related. Data analytics is a component of data science, used to understand what an organization’s data looks like. Generally, the output of data analytics is reports and visualizations. Data science takes the output of analytics to study and solve problems.