Real world Data Analysis using Healthcare Data

I want to share the details of my personal data analysis project, where I delved into real-world healthcare sample data provided by Curtis Norman. The project took me through the process of transforming raw data into meaningful insights, and I’m excited to walk you through the steps of my exploration.

The Data:

The sample dataset is provided in the format of an Excel worksheet. The data, a snapshot of real-world healthcare information, was the starting point for my analytical journey. Understanding the nuances of healthcare data is a challenge, but it’s also an opportunity to make a meaningful impact.

Creating the Database:

To unlock the full potential of the data, I decided to use Microsoft SQL Server. I began by creating a relational database, a structured foundation that would facilitate seamless data management and analysis. The process involved defining tables, establishing relationships, and ensuring data integrity, laying the groundwork for an analytical framework.

Importing Data and Building the Data Model:

The next step was to import the healthcare data from the Excel worksheet into the SQL Server database. This process is crucial for transforming raw data into a format conducive to analysis. Once the data was securely housed in the database, I set about constructing a comprehensive data model. This involved organizing and structuring the data in a way that would enable efficient querying and analysis.

Writing Multiple Views:

To enhance the flexibility of my analysis, I crafted multiple views within the SQL Server database. Views act as virtual tables, presenting data in a way that aligns with specific analytical needs. This step allowed me to tailor my approach, ensuring that I could explore different facets of the healthcare data with ease.

Connecting SQL Server Database to Power BI:

The true magic happened when I connected the SQL Server database to Power BI, a dynamic business analytics tool. This integration opened up a world of possibilities, enabling me to visually represent the data and create insightful reports. Power BI’s user-friendly interface and powerful features allowed me to translate complex data into meaningful visuals.

Building Multiple Page Reports:

With the SQL Server database seamlessly integrated into Power BI, I set about constructing a multi-page report. Each page focused on a specific aspect of the healthcare data, providing a holistic view of the information. From demographic trends to Payer Analysis, the report became a narrative, weaving together the story hidden within the numbers.

Designing mobile dashboards like a pro: Best practices for Power BI users

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Data plays a critical role in making informed decisions in today’s rapidly changing world. With mobile devices becoming an inseparable aspect of our daily lives, we are increasingly dependent on them for conducting business-related tasks. As a result, it has become essential to create mobile dashboards that provide easy access to critical data on the go. Power BI mobile apps allow your end users to access any Power BI report page on their device. But reports designed for use on a computer can be difficult to read and interact with on mobile phones.

Therefore, I would like to share some of the best practices that I follow in Power BI to create professional-looking mobile dashboards.

Keep it simple

The first and most important rule of creating a mobile dashboard is to keep it simple. Mobile screens are smaller than computer screens, so it’s essential to focus on the most critical information. Avoid cluttering the dashboard with too many visuals, charts, and tables. Use only the necessary elements that convey the data story.

Plan your dashboard layout

Before you begin creating your dashboard, it’s important to plan out the basic layout. Think about what information you want to present and how you want to present it. You should also consider the target audience and what devices they are likely to use. With this in mind, you can design the dashboard to be mobile-friendly from the beginning.

Use a grid layout

A grid layout is an effective way to organize your dashboard. It helps to maintain a consistent look and feel throughout the dashboard.

Use mobile formatting options

In Power BI, use mobile formatting options to style and format visuals in a mobile-optimized layout without affecting their formatting in a web layout.

Optimize visualizations for mobile

Power BI offers a variety of visualizations; however, not all of them are responsive and suitable for mobile devices. Avoid using visualizations that are too complex or require too much interaction, as they can be difficult to use on a small screen.

Use drill-through navigation

Drill-through navigation allows users to click on a data point to see more detailed information. This feature is particularly useful on mobile devices, as it reduces the need for users to navigate through multiple pages to find what they need.

Test your dashboard

Before publishing your dashboard, test it on multiple devices to ensure that it is mobile-friendly. Make sure that the visuals are easy to read and interact with on a small screen and that the layout is optimized for mobile devices.

Publish your dashboard

Once you’re happy with your mobile-friendly dashboard, you can publish it to the Power BI service. Users can access the dashboard from any device with an internet connection, making it easy to stay up-to-date on the go.

In conclusion, creating a mobile-friendly dashboard in Power BI requires careful planning and consideration of the target audience and device. By following these practices, you can create a professional-looking dashboard that is easy to use and provides valuable insights on any device.

Mastering data visualization: 5 essential color selection best practices

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As a business intelligence analyst, one of the most critical aspects of your job is communicating data insights effectively. To do that, you need to make sure that the data visualizations you create are not only accurate but also engaging and easy to understand. And one of the key factors that can make or break a visualization is the colour scheme.

Colour selection for data visualization is not just about making things look pretty. It’s about making sure that the message you want to convey is clear and easy to understand.

Following are some best practices for colour selection for data visualization.

1. Consider the audience

The first thing you need to consider when choosing colours for your data visualization dashboard is your audience. Who will be looking at your dashboard? What are their preferences? What do they expect to see? For example, if you’re designing a dashboard for a business audience, you may want to use more conservative colours like blues and grays.

2. Use colour to highlight important data points

One of the main goals of data visualization is to draw attention to the most important data points. You can use colour to help you do that. For example, you might use a different colour to highlight a data point that is outside of a normal range or that represents a significant change.

3. Be consistent

Consistency is key when it comes to colour selection for data visualization. You want to make sure that the colours you use are consistent across your dashboard. This not only makes it easier for your audience to understand the information but also makes your dashboard look more professional.

4. Consider colorblindness

It’s important to consider colorblindness when choosing colours for your data visualization. It’s important to make sure that your colour scheme is still easily understandable to those with color vision deficiencies. You can do this by using colour schemes that have high contrast or by using patterns or textures in addition to colour.

5. Use colour sparingly

Colours should be used sparingly in data visualization. Using too many colours can make your dashboard look cluttered and confusing. Stick to a few key colours that work well together, and use them consistently throughout your dashboard.

In conclusion, colour selection for data visualization is a key aspect of developing successful and engaging data visuals.

  • Consider your audience
  • Use colour to emphasize important data points
  • Be consistent
  • Consider colour blindness
  • Use colour wisely

to create data visualizations that are not only accurate but also visually appealing and easy to understand.