To Code or Bust
Our laboratory wanted to design a new animal behavioral experiment.
This required using animal behavior recording chambers, which necessitated learning and
mastering the MedStateCode programming language independently. Overcoming this challenge is
when I initially discovered the power of coding and programming.
On to Python
Empowered by my initial success with programming, I decided to learn the versatile Python language.
Eager to apply this tool, I explored how it could advance our scientific goals. Before long,
I was providing solutions to previously intractable problems.
The Program Stands Alone
Wanting to share these solutions presented the challenge of enabling colleagues to use these
programs for their projects. This led me to learn how to construct and design GUI applications.
My efforts culminated in creating many PySimpleGUI and Tkinter standalone executable programs,
complete with intuitive user interfaces for non-technical lab members.
Putting the Squeeze on Data with Python
Recognizing Python's potential for task automation, I set out to address the tedious data collection and
entry issues plaguing our data-dependent lab. This led to the creation of numerous automation programs,
saving countless hours of data aggregation and processing time.
Snakes on a Phone!
In an effort to provide mobile solutions for lab members, I tackled a new challenge.
Many routine lab tasks require precise calculations and are prone to errors,
such as drug dosing or converting between different chemical solution percentages.
To address these issues, I created sharable mobile applications using the Kivy framework,
allowing lab members to perform these calculations in the field, ensuring speedy and accurate results.
Bio Science to Data Science
Although automating data collection and calculations saved much time,
there was another side of Python uniquely suited to scientific endeavors:
statistical analysis and data visualization. To harness Python's strength in this realm,
I began learning critical frameworks such as Pandas, Numpy, and Matplotlib.
As I became proficient in data manipulation and visualization,
I unlocked new insights and efficiencies for our lab.
Shedding the Self-Taught
While my initial Python learning was self-directed,
I recognized the value of structured learning and formal recognition.
I began supplementing my independent study with online courses and
pursuing relevant certifications to solidify my knowledge and demonstrate my skills.
This included learning from platforms like FreeCodeCamp, Codecademy, and Coursera.
More Tools More Problems Solved
Expanding my data science skill set, I've continued learning visualization tools
such as Tableau, Streamlit, and R. Additionally,
I've begun exploring machine learning theory and building prediction models.
Is Your Organization Next!?
I am eager to bring my technical expertise, scientific mindset,
and curiosity to the problems your team is aiming to solve.