Alliance Health

Data Analyst II

Dentalle Inc.

Compiled data and reviewed information for accuracy prior to input, which resulted in a better organizational data structure 

Produced and analyzed weekly and monthly reports using advanced Excel spreadsheet functions, showing how our data changed throughout time 

Verified accuracy of data and corrected any errors before transcribing to ensure reliability of analysis 

Organized, sorted and checked input data against original documents

Data Specialist

Career Pause due to COVID-19
Obtained training through courses in advanced
Microsoft Excel.

Career pause

HEALTHY FAMILY DENTISTRY PA

Dental Hygiene combined with Office Management

Created spreadsheets using Microsoft Excel for daily, weekly, and monthly 
financial reporting, which resulted in achievement of monthly goals

Participated in team-building activities to enhance working relationships
 
Tracked patient’s oral health by collecting, monitoring, and updating data
 
Actively used the collected data in 
storytelling, resulting in patient’s oral heath improvement 

Excellent oral communication, using inter-personal skills to build 
relationships with patients and team

Certified Registered Dental Hygienist

Data Analyst with deep knowledge in Excel, SQL and Data Visualization 

My background is in Clinical Healthcare, which enabled me to practice and refine hard skills, such as Data Collection and Analysis; and also soft skills, such as communication, critical thinking and building relationships. My heart was in business, so I found Data Analytics to be right field to be at, in order to solve everyday problems using data-driven solutions. 

Here is my data portfolio: 

https://thaiscooke.github.io/

I am so excited to announce that the topic of my LinkedIn Learning course will be on my love-at-first-sight-data-analytics-tool: SQL 🤩 

And since I am always preaching that tools are not useful without a context behind it,  I will be showing how to make use of SQL skills in the healthcare space. 

If you are either a healthcare professional that wants to transition into data analytics, or a data professional who wants to understand how data can be used in healthcare, this course is for you. 

Are there any questions that you would like me to cover throughout the course? Share in the comments below ☺️

Thais Cooke

Cleaning data is like cleaning my house: when I think I am done, there is always something else to clean…

Just like using specific cleaning tools for the house, we also have our favorite data-cleaning tools. I love using SQL, Power Query, and Python to clean data (although I am still learning, and just using it in very specific cases)

What are your favorite data cleaning tools and techniques? Share in the comments below ☺️

Before becoming a data analyst, I was a dental hygienist. 

The healthcare industry is a tough one to be in. During my (very rare) downtime, I would be brainstorming solutions to common problems:

“This patient was waiting for so long! How to reduce the wait time in our waiting area?”

“This is going to take longer than what I thought. How to allocate the right amount of time for each procedure?”

“Am I going to need extra help today?”

“Am I even going to have time for a lunch break today?” 😅 

In the past, the answer to those questions would be guided by “intuition” or “gut feeling”. Today, we have data at our disposal to figure out concrete solutions to daily issues. The use of SQL and other tools can help tremendously to achieve solutions for those problems. 

How do you use data in your industry?

If I could pick something that took me some time to understand in SQL, I would say that window functions certainly makes the list. And I still struggle with those from time to time. 

For window functions, think of them as virtual "windows" through which you can view and analyze your data. Here is an analogy: 

Imagine you're watching a parade from a window on a building. You can see the entire parade route, but your view is limited to what's in front of you at that moment. 

Now, let's apply this concept in SQL: each participant in the whole parade represents a row in your SQL dataset. Each row contains different attributes, just like how each parade participant has unique features.

The window in our analogy represents the subset of rows that the window function operates on. It's like adjusting your view to focus on a specific section of the parade. Let’s say that each time, you want your window to only capture the participants that are wearing the same costume color, and you want to order the participants from tallest to shortest in each costume color group. 

You could use a window function to assign a row number to each participant based on their height, and the comparison would be done only among the participants who are in that window. The participant with a row number of 1 would be the tallest within that specific range, while 2 would be the second highest and so on. 

Your query would look like this:

SELECT 
    Participant_ID,
    Height,
    Costume_color,
    ROW_NUMBER() OVER (PARTITION BY Costume_Color ORDER BY Height DESC) AS Height_per_costume_color
FROM Parade_Participants 

My brain works through analogies, so hopefully this is helpful 😅 

How do you use window functions at your work?

I remember when I started working with “real-life” data, and what a difference it was from the perfectly structured data found in courses. 

I understand why the data in courses is so clean and perfect: the goal is to have the student grasp the logic of the syntaxes. If you are a beginner, going straight to messy data can make the teaching more confusing. 

Once you are past this stage, however, and feel confident in your abilities, I strongly encourage you to get your hands dirty with data. Here are the differences between both types of datasets: 

Perfectly Clean Dataset:
  ⭐️ Data is well-organized, structured, and free from errors or inconsistencies.
  ⭐️ Analysis tends to be straightforward, with clear patterns.
  ⭐️ Results often lack the complexities and nuances found in messy data.
  ⭐️ There is little room for unexpected variables or outliers.

Real Life Dataset:
  ⭐️ Data is messy, with missing values, outliers, and inconsistencies present.
  ⭐️ Requires extensive cleaning, preprocessing, and validation before analysis can begin.
  ⭐️ Analysis is more challenging, as patterns may be obscured by noise or hidden amidst the chaos.
  ⭐️ Often requires a combination of technical expertise and domain knowledge to extract meaningful insights.

How was your experience when you first started diving into “real-life” data? Share in the comments below ☺️

Taplio

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