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.

Getting started with Power BI: A beginner’s guide for data enthusiasts

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If you’re interested in learning about Power BI and how to get started with this powerful data visualization tool, then this post is for you. As someone who has been using Power BI for a while now, I wanted to share some tips and insights with those who are just starting out.

First of all, let me tell you that Power BI can seem overwhelming at first. But don’t worry; you’re not alone. There are so many features and options to choose from, and it can be hard to know where to begin. However, with a little bit of guidance, you’ll be able to create stunning visualizations and become a Power BI pro in no time.

Download and install Power BI Desktop

To start off, you’ll need to download Power BI Desktop. This free tool allows you to create, view, and share interactive reports and dashboards. You can download Power BI Desktop from the official Microsoft website.

Click here to download

Connect to tour data sources

Once you have Power BI Desktop installed, the next step is to connect to your data sources. Power BI supports a wide range of data sources. To connect to your data, click on the “Get Data” button in the Home tab of the Power BI Desktop. From there, you can choose from a variety of data sources and connection types. For example, if you want to import data from an Excel spreadsheet, you can select the “Excel” option and then navigate to the file on your computer. If you want to connect to a database, you can select the appropriate database type (such as SQL Server or Oracle) and then enter your server and login credentials. Once you’ve connected to your data source, you’ll see a preview of the data and can begin exploring and manipulating it.

Transform and clean your data

Now it’s time to transform and clean your data. Power BI offers a powerful data transformation and cleaning tool called Power Query, which allows you to perform a variety of data transformation tasks without leaving the application. With Power Query, you can easily clean up messy data, remove duplicates, split and merge columns, and perform calculations and other transformations. You can also use Power Query to reshape your data by pivoting, unpivoting, and grouping it in different ways.

To use Power Query, simply click on the “Transform Data” button in the Home tab of the Power BI Desktop. This will bring up the Power Query Editor, where you can perform a variety of data transformation tasks using an intuitive, drag-and-drop interface. By transforming and cleaning your data in Power Query, you can ensure that your data is accurate, consistent, and ready for analysis. This will save you time and effort down the line and help you get more value out of your data. Plus, by cleaning your data within Power BI, you can easily refresh your reports and visualizations with updated data as it becomes available.

Understand relational data modeling

But before you dive into creating visualizations in Power BI, it’s important to understand the relational data model. The relational data model is a method of organizing data into tables and establishing relationships between those tables. This allows you to easily analyze and manipulate the data to gain insights. To understand the relational data model, it’s important to know the different types of relationships that can exist between tables. These include one-to-one, one-to-many, and many-to-many relationships. In a one-to-one relationship, each record in one table is associated with only one record in another table. In a one-to-many relationship, each record in one table can be associated with multiple records in another table. In a many-to-many relationship, multiple records in one table can be associated with multiple records in another table.

DAX measures and calculated columns

DAX (Data Analysis Expressions) is a formula language used in Power BI to create custom calculations and measures. Measures are calculations based on the values in a column or columns in your data model, while calculated columns are new columns that you create based on a formula that you define. These DAX expressions can be used in visualizations to create more advanced analysis and insights. Measures are typically used for aggregations, such as summing up sales or calculating averages. Calculated columns, on the other hand, are used for more complex calculations that can’t be done using measures alone. For example, you might use a calculated column to create a new column that categorizes data based on a specific criteria, or to calculate the difference between two columns.

When creating DAX expressions, it’s important to understand the syntax and logic behind the language. Fortunately, Power BI offers a number of built-in functions that can be used to create more complex calculations without having to write custom code from scratch. Additionally, there are many resources available online, including tutorials and community forums, where you can learn more about DAX and get help with specific challenges. By learning how to use DAX measures and calculated columns in Power BI, you’ll be able to create more sophisticated analyses and visualizations that provide deeper insights into your data.

Create visuals

Once you have a good grasp of the data model and how to create calculated columns and measures, you can start building your visualizations. The best way to do this is to start with a clear goal in mind. What do you want to communicate with your data? Is there a particular insight you’re trying to uncover? 

Once you know what you’re trying to achieve, you can select the appropriate visualization type and start customizing it to your liking. One of the great things about Power BI is that it offers a wide variety of visualization types to choose from. You can create simple bar charts, line graphs, or pie charts, or you can get more creative with heat maps, tree maps, and other custom visuals. You can drag and drop fields from your data sources onto the canvas to create your visualizations. Also, it’s very easy to switch between different visualizations and see how your data looks in each one. This allows you to experiment and find the best way to communicate your insights.

Customize your visualizations

Once you’ve created your visualizations, you can customize them to your liking. Power BI offers a range of formatting options, including colors, fonts, and backgrounds. You can also add interactive features like drill-downs and filters to make your visualizations more engaging. You can also add additional visualizations to your report and arrange them in different layouts to create a more cohesive and engaging story. For example, you can use a combination of charts and graphs to illustrate trends over time, highlight comparisons between different data sets, or showcase geographical patterns.

To customize your visualizations in Power BI, simply click on the visualization you want to edit, and then use the formatting and customization options in the Visualizations pane to make your changes. With a little bit of experimentation and practice, you can create visualizations that are both informative and visually appealing, helping you better communicate your data insights to your audience.

Share your reports and dashboards

The final step is to share your reports and dashboards with others. Whether you’re collaborating with colleagues or sharing your data insights with stakeholders, Power BI provides a range of options for sharing your content by publishing it to a Power BI workspace or app. You can also control the level of access they should have. For example, you can share a report with a colleague and give them view-only access, or you can share a dashboard with a stakeholder and give them the ability to edit and analyze the data. Additionally, you can share your content via email or embed it on a website or application.

By sharing your reports and dashboards in Power BI, you can ensure that everyone who needs to see your data insights has access to them, regardless of their location or device. This can help increase collaboration and drive better decision-making across your organization.

In conclusion, I want to assure you that starting your Power BI journey might seem overwhelming at first, but trust me, it’s worth it. With the right guidance, you can quickly master the art of creating visually stunning reports and dashboards that will impress your colleagues and clients alike. Just follow the steps I’ve outlined, and you’ll be amazed at what you can achieve. And don’t forget, practice makes perfect! The more you work with Power BI, the more confident and innovative you’ll become in creating beautiful and effective data visualizations. So go ahead and give it a try; I promise you won’t regret it!

Jumpstart your career: Tips for becoming a business intelligence analyst

Tips for Becoming a Business Intelligence Analyst

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Are you interested in becoming a business intelligence analyst? With the demand for data-driven insights on the rise, this field is a great choice for those looking for a challenging and rewarding career. Here are a few tips to help you get started:

Develop your technical skills

To succeed as a business intelligence analyst, you need to have a solid foundation in data analysis and visualization tools. Familiarize yourself with SQL, Excel, and Power BI or Tableau, and consider taking courses or earning certifications in these areas.

Build your business acumen

To be effective in this role, you need to have a deep understanding of the industry you’re working in. Take the time to learn about the products, services, and customers of the company you’re interested in, and read up on industry trends and best practices.

Network with others in the industry

Attend conferences, join professional associations, and connect with other business intelligence analysts on LinkedIn. These connections can provide valuable insights into the industry and help you stay up-to-date on the latest trends and technologies.

Look for opportunities to gain experience

Look for internships or entry-level positions in business intelligence. Even if you start in a junior role, the experience you gain will be invaluable in developing your skills and building your career.

Highlight your achievements

When applying for jobs, make sure to highlight your achievements in data analysis and visualization. Share examples of how you’ve used data to solve business problems and drive decision-making. Try to use industry keywords in your resume.

Starting a career in business intelligence can be challenging, but with the right skills and mindset, you can succeed in this exciting field. So take the first step today and start developing the skills and connections you need to launch your career as a business intelligence analyst.

Tips and strategies for taking your analytical thinking skills to the next level

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The ability to analyze and comprehend data has become an increasingly vital skill in a world where practically every organization relies significantly on it. Business analysts play an important role in assisting businesses in making informed decisions by finding patterns and trends in data and providing insights and recommendations. However, to be an effective business analyst, you must cultivate a mindset that is focused on data-driven decision-making and analysis.

So, here are some tips for developing analytical thinking skills.

Develop a curious mindset
One of the most important qualities of an effective analyst is curiosity. Curiosity drives an analyst to ask questions, explore new ideas, and discover new insights. To develop a curious mindset, ask questions, read books and articles outside of your comfort zone, and look for opportunities to learn new things.

Practice critical thinking
Critical thinking is the ability to analyze information objectively and make evidence-based decisions. To develop critical thinking skills, evaluate information from multiple sources, question assumptions, and test hypotheses.

Improve your problem-solving skills
Analytical thinking involves breaking down complex problems into smaller, more manageable components. To improve your problem-solving skills, practice breaking down problems into smaller pieces and solving them step-by-step.

Learn to communicate effectively
Effective communication is essential for an analyst to convey complex information in a clear and simple manner. Practice writing and presenting your findings to different audiences to develop your communication skills.

Develop your technical skills
Technical skills are essential for an analyst, and developing them is crucial for success. Learn to use analytical tools and software such as Excel, SQL, Power BI, or Tableau and stay up-to-date with the latest trends in the field.

Analytical thinking skills are essential for success in the field of business analysis. Developing a curious mindset, practicing critical thinking, honing your problem-solving skills, learning to communicate effectively, and developing your technical skills are all important strategies for unlocking the mind of an analyst. By following these tips, you can take your analytical thinking skills to the next level and become a valuable business analyst.

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.

New York taxi weekly forecast & comparison overview

This week I analyzed a HUGE data set provided by Maven Analytics, which contained records of 28 million Green Taxi trips in New York City. It was perfect for data wrangling and visualization task. I picked up my favorite BI tool Power BI to complete this entire project.


Build a dashboard to assist the Lead Dispatcher with weekly planning and logistics based on Green Taxi Trips in New York City (Year 2017-2020).

Answering the following questions:

  • What’s the average number of trips we can expect this week?
  • What’s the average fare per trip we expect to collect?
  • What’s the average distance traveled per trip?
  • How do we expect trip volume to change, relative to last week?
  • Which days of the week and times of the day will be busiest?
  • What will likely be the most popular pick-up and drop-off locations?


Provided dataset contained records of 28 million Green Taxi trips in New York City for four fiscal years 2017 to 2020. The raw data has some issues, so it required the following adjustments and assumptions to clean and prepare the data for further analysis.

  • Included trips that were NOT sent via “store and forward”
  • Included street-hailed trips paid by card or cash, with a standard rate
  • Removed any trips with dates before 2017 or after 2020, along with any trips with pickups or drop-offs into unknown zones
  • Assumed any trips with no recorded passengers had 1 passenger
  • Swapped dates If a pickup date/time is AFTER the drop-off date/time
  • Removed trips lasting longer than a day, and any trips which show both a distance and a fare amount of 0
  • Changed any records where the fare, taxes, and surcharges are ALL negative into positive value
  • Calculated the distance for any trips that had a fare amount but a trip distance of 0, using this formula: (Fare amount – 2.5) / 2.5
  • Calculated the fare for any trips that had a trip distance but a fare amount of 0, using this formula: 2.5 + (trip distance x 2.5)

Data Cleaning and Preparation task completed using Power Query
Final Dashboard

Check out an interactive report here:

Exploratory Data Analysis of the Titanic Dataset

I have participated in the DataDNA October Challenge the Titanic Kaggle dataset. Analyzing the Titanic Kaggle dataset to identify which class was most likely to survive the Titanic disaster.


Provided data is a themed dataset and may not be accurate against the actual events of the Titanic disaster. The titanic sample dataset records only 418 passengers’ data.


Identify which class was most likely to survive.

My Process

In order to know how well each characteristic correlates with survival, I decided to approach the problem based on the characteristics available in the dataset.

Analyzed the given dataset for errors or possibly inaccurate values within characteristics and tried to correct those values or excluded the samples containing the errors.

Age data in this dataset is incomplete. It contains 86 null values which will not be included while measuring age correlation with survival rate. The cabin and ticket features are dropped during the analysis because of fewer data points.

Classifying available characteristics in the given dataset would facilitate further analysis.


Sex – Gender of passenger: male or female

Class – 1 = First Class, 2 = Second Class, 3 = Third Class

Point of embarkation – C = Cherbourg, Q = Queenstown, S = Southampton

Survived – 0 = Dead, 1 = Alive


Age – Age of passenger

Fare – Fare paid for a ticket

SblSp – Number of traveling with sibling or spouse

Parch – Number of traveling with parent or children


Passenger’s Gender

Out of 418 total passengers, only 152 (36%) survived and 266 (64%) died. This includes 100% of females who were rescued, compared to 100% of males who died. Most females (72) traveled in 3rd Class. This reflects that gender influences the number of survivors because women from all classes were rescued with priority over men.

Passenger’s Age

Children had the highest survival rate of 46% considering they were accompanied by their mothers. Young adults around the age of 20 to 30 had the lowest survival rate of 37%. Most of them traveled in the 3rd class.
Note – Age data in this dataset is incomplete. The age characteristic contains 86 null values, which are not included in the above chart.

Traveling with Siblings or Spouse

Passengers traveling with siblings or spouses had higher survival rates than those traveling alone.

Traveling with Parents or Children

Passengers who lost the most traveled alone in comparison to passengers with parents or children. Most of those families traveled in the 1st class while most solo passengers traveled in the 3rd class.

Port of Embarkation

(65%) passengers embarked from Southampton port, (24%) from Cherbourg port, and (11%) from Queenstown port. Passengers who boarded from Queenstown had the highest (52%) survival rate compared to Cherbourg (39%) and Southampton (33%).

Class of Travel

The survival rate was highest in the First class (47%), followed by the third class (33%) and second class (32%). This reflects the fact that the highest fare-paid passengers had the highest survival rate.


Overall, several characteristics were observed that correlated with the rate of survival of the Titanic Disaster.
Gender: Female passengers from all classes were rescued with priority.
Family: Passengers who traveled with family members had the highest survival rate. Most of them traveled in the 1st class and unaccompanied passengers who lost the most were in the 3rd class.
Age: Young adults around the age of 20 to 30 had the lowest survival rate since most of them traveled in the 3rd class.
1st class passengers were given priority as they paid the highest fare compared to other classes.
Highest number of passengers who embarked from Southampton and traveled in 3rd class had the lowest rate of survival.

Data Visualization

Operational performance analysis to identify opportunities to improve productivity and increase revenue.

This is my submission for Maven Analytics’ Pizza Challenge October 2022. This report summarizes Plato’s Pizza restaurant’s annual performance of the year 2015. Additionally, it makes recommendations to identify opportunities to increase sales and perform more efficiently.

Data Set

Plato’s Pizza is a fictitious Greek-inspired pizza place in New Jersey. The dataset provided in the Data Playground contains one year of sales data. It contains four tables in CSV format.


Create data models and use your data analysis & visualization skills to bring the data to life. Analyze data and create a report to help Plato’s Pizza find opportunities to drive more sales and work more efficiently.

Also, answer the following questions.

  1. What days and times do we tend to be busiest?
  2. How many pizzas are we making during peak periods?
  3. What are our best and worst selling pizzas?
  4. What’s our average order value?
  5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)
My Process

Plato’s Pizza has already defined the goals of this analysis, so I mainly focused on answering all the questions using Power BI. Created some Dax measures and calculated columns.

Visualization is my favorite part. An important component of being a data analyst is the ability to communicate your findings in a way that will appeal to your audience. We are always tempted to show every metric possible. But more is not always good. Since too many elements can be distracting I decided to include graphs that answer Plato’s questions.

1. What days and times do we tend to be busiest?

2. How many pizzas are we making during peak periods?

3. What are our best and worst selling pizzas?

4. What’s our average order value?

Average Daily Revenue = 
    VALUES(orders[date]),[Total Revenue]

Average Daily Orders = 
    VALUES(orders[date]), [Total Orders]
Average Daily Pizzas Sold = 
    VALUES(orders[date]), [Total Quantity Sold]
Average Pizzas Per Order = 
DIVIDE([Total Quantity Sold],[Total Orders]
Average Order Value = 
DIVIDE([Total Revenue], [Total Orders]

5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)

  • The most popular large size pizzas are generating highest (45.89%) revenue $375.32K whereas XXL size pizzas are generating least (0.12%) revenue $1.01K. Therefore, XXL size can be removed from the menu.
  • Changing restaurant opening time to 11:00 to 22:00 hours can reduce operational cost.
  • Replacing 50% seating arrangement with 2-seater tables will maximize utilization of seating capacity.
  • To drive more sale on Sunday and during post lunch off hours, Plato’s should run promotions / special offers.

Data Visualization

Check out an Interactive Version.

Professional end to end business intelligence solution using Microsoft Power BI

This is my personal project. Spirit Sport, is a fictional global manufacturing company. As a Business Intelligence Analyst, my task is to design and deliver professional and end to end business intelligence solution using Microsoft Power BI. The raw data is provided in CSV files. (Data Source: Maven Analytics).
I have followed a steady and systematic process through the Power BI workflow and divided this project into four steps.

Step 1: Connecting and transforming the raw data

I received the raw data in an unformatted structure. Therefore, after importing all CSV files into Power BI, I had to transform them into a suitable format and extract meaningful insights using the power query editor.

Step 2: Build a relational data model

In this project, the model consists of six lookup tables: Customer, Calendar, Territories, Product, Product Category, and Product Sub Category.

And two fact tables: Sales and Returns. I have used the snowflake schema and all relationships are one-to-many.

Step 3: Adding calculated fields using DAX

I have added multiple calculated columns to filter the data. Instead of creating new numeric values, I created new fields and used them to create those filters. Wrote DAX measures for aggregation formulas and computed numerical values to use in the visualization.

Step 4: Designing interactive Power BI reports

In this step, I have designed three different reports as follows,

Executive Summary

Product Details
Customer Details