Data Analysis With Power BI

3. Data Analysis Process

Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights.

• Data Requirement Gathering: Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.
• Data Collection: Guided by your identified requirements, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
• Data Cleaning: Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
• Data Analysis: Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
• Data Interpretation: Now that you have your results, you need to interpret them and come up with the best courses of action based on your findings.
• Data Visualization: Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.

Introduction to Power BI:

Power BI is an increasingly popular business intelligence and visualization tool from Microsoft that enables data analytics and data visualization. Ron George initially designed Power BI in the summer of 2010. The initial release was available for public download the following year. Power BI Desktop, Power BI Service, Power BI Gateway, Power BI Report Server, and Power BI Mobile Apps are the key components of the platform.

Overview of Power BI

Microsoft’s Power BI is a business and data analytics service that enables professionals to process, analyze, and visualize vast volumes of data. It helps extract insights, draw conclusions, and share results in the form of reports and dashboards across various departments. It provides an easy drag and drops feature with a range of interactive data visualizations to generate reports and dashboards

Key Features of Power BI

• Power BI is faster and performs better when there is a smaller volume of data
• Power BI provides an interface based on Microsoft Office 365 that is user-friendly, intuitive, and easy to understand
• Using Power BI, you can work with several data sources, such as Excel, Text/CSV, JSON, SQL Server databases, IBM DB2, MySQL, etc.
• Power BI can connect with the R programming language, and it also supports various Data Analysis Expression (DAX) functions and measures
• Power BI has functional integration with the Microsoft Azure cloud platform. It helps to analyze insights and patterns in datasets

1. Getting Started with Power BI: 3.1 Installing Power BI Desktop 3.2 Connecting to Data Sources 3.3 Loading Data into Power BI

2. Data Transformation and Modeling: 4.1 Cleaning and Shaping Data 4.2 Creating Relationships between Tables 4.3 Data Modeling Techniques

3. Data Visualization: 5.1 Creating Basic Visualizations 5.2 Advanced Visualizations and Customization 5.3 Interactive Reporting with Filters and Slicers

4. Analyzing Data with Power BI: 6.1 Using Measures and Calculated Columns 6.2 Aggregating and Summarizing Data 6.3 Applying Advanced Data Analysis Techniques

5. Sharing and Collaboration: 7.1 Publishing Reports to Power BI Service 7.2 Creating Dashboards and Reports 7.3 Sharing and Collaboration Features

6. Power BI and Data Refresh: 8.1 Automatic and Scheduled Data Refresh 8.2 Troubleshooting Data Refresh Issues

7. Power BI and Advanced Analytics: 9.1 Integrating Power BI with Machine Learning 9.2 Using Power BI for Predictive Analytics

8. Power BI Best Practices: 10.1 Designing Effective Dashboards and Reports 10.2 Performance Optimization Techniques 10.3 Security and Data Governance