Episode 09 of the ‘Becoming a Data Scientist’ Podcast: Unveiling the Journey of Justin Kiggins

Episode 09 of the ‘Becoming a Data Scientist’ Podcast: Unveiling the Journey of Justin Kiggins

Introduction:

In this episode of the Becoming a Data Scientist Podcast, Renee interviews Justin Kiggins, a “full stack neuroscientist” with a unique background. Justin initially pursued a career in music therapy as a musician, but later shifted to mechanical engineering and eventually found his passion in biomedical engineering and neuroscience. Currently, he focuses on studying auditory perception and the brains of communicating birds. The podcast provides valuable insights into Justin’s journey and the fascinating field of neuroscience. Whether you are a data scientist or simply curious about the complexities of the human brain, this interview is sure to captivate and inspire you.

Full Article: Episode 09 of the ‘Becoming a Data Scientist’ Podcast: Unveiling the Journey of Justin Kiggins

Justin Kiggins: The “Full Stack Neuroscientist”

In a recent interview, Justin Kiggins, self-proclaimed “full stack neuroscientist,” spoke about his unique journey from a musician majoring in music therapy to studying auditory perception and the brains of communicating birds. Kiggins’ career path took him through various fields, including mechanical engineering, biomedical engineering, and neuroscience. Let’s dive into his fascinating story.

The Beginnings: From Musician to Engineer

Kiggins initially pursued a career in music therapy, majoring in music as an undergraduate student. However, his interests soon shifted, and he transitioned to mechanical engineering. This move introduced him to a whole new world that would eventually lead him to neuroscience.

Biomedical Engineering and Neuroscience: A Natural Progression

After completing his degree in mechanical engineering, Kiggins developed an interest in biomedical engineering, which paved the way for his eventual exploration of neuroscience. This discipline allowed him to delve deeper into the study of auditory perception and the intricacies of bird communication.

Studying Auditory Perception and Bird Brains

Kiggins’ research primarily focuses on auditory perception and studying the brains of communicating birds. By understanding how birds perceive and produce sounds, scientists can gain valuable insights into human auditory processing and communication. Kiggins’ work sheds light on the complex mechanisms underlying these abilities.

Podcast Episode and Video Links

Listeners interested in learning more about Kiggins’ journey can tune into the podcast episode featuring his interview. The episode is available on various platforms, including the podcast’s RSS feed for podcast subscription apps, Stitcher, and iTunes. For those who prefer video content, there is also a YouTube playlist featuring interview videos.

Additional Resources Mentioned by Justin

Throughout the interview, Kiggins references several resources that have been influential in his career. These resources include:

1. MatLab: A software platform used for numerical computation and visualization.
2. Echo Localization: The process by which animals detect the location of a sound source using echoes.
3. Convolution: A mathematical operation used to combine two functions and produce a third function.
4. Fulbright Scholar: A prestigious scholarship program that supports international educational exchange.
5. Ushahidi: A non-profit organization that develops open-source software for data collection and visualization.
6. HarassMap: An initiative that aims to end sexual harassment through technology and community engagement.
7. European Starling: A bird species known for its distinctive song and communication abilities.
8. Bird Song Recursive Syntactic Structure: A scientific study investigating the structural complexity of bird songs.
9. Zebra Finch Song: Another bird species that has been extensively studied due to its complex vocalizations.
10. Spectral Analysis: A technique used to analyze the frequency components of a signal or function.
11. Neuron: A specialized cell that transmits electrical and chemical signals in the nervous system.
12. Brain Electrodes: Devices used to record electrical activity from the brain.
13. Numpy, Spark, Thunder, and Pandas: Software libraries commonly used in data analysis and manipulation.
14. Birth Doula: A trained professional who provides emotional and physical support during childbirth.
15. Allen Institute: A research institute dedicated to advancing scientific understanding of the brain.

Closing Thoughts

Justin Kiggins’ unique journey from musician to “full stack neuroscientist” is a testament to the diverse paths one can take in pursuit of scientific knowledge. His research on auditory perception and bird communication offers valuable insights into human cognition and communication. As Kiggins continues his work, he will undoubtedly contribute to our understanding of the intricate workings of the brain.

Summary: Episode 09 of the ‘Becoming a Data Scientist’ Podcast: Unveiling the Journey of Justin Kiggins

In this episode of the Becoming a Data Scientist Podcast, Renee interviews Justin Kiggins, a “full stack neuroscientist” who has studied auditory perception and the brains of communicating birds. Justin shares his journey from being a musician majoring in music therapy, to switching to mechanical engineering, and eventually finding his way to biomedical engineering and neuroscience. During the interview, Justin mentions various topics such as echo localization, convolution, Fulbright scholar program, Ushahidi, and HarassMap. He also discusses his research on bird song and neural activity, and the tools he uses including MatLab, numpy, spark, thunder, and pandas. Overall, this episode provides insights into the fascinating world of neuroscience and data science.

Frequently Asked Questions:

Q1: What is data science, and what does it entail?
A1: Data science is an interdisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract knowledge and insights from data. It involves using various tools, techniques, and algorithms to interpret and uncover meaningful patterns, trends, and correlations in datasets.

Q2: How is data science relevant to different industries?
A2: Data science has become increasingly essential in nearly all industries. It enables businesses and organizations to make data-driven decisions, enhance operational efficiency, optimize marketing strategies, improve customer experience, detect fraud, predict market trends, and much more. From healthcare and finance to retail and manufacturing, data science is revolutionizing the way businesses operate.

Q3: What are the key skills required to become a data scientist?
A3: To excel in data science, individuals need a strong foundation in mathematics and statistics, as well as programming skills. Other crucial skills include data visualization, data cleaning, data engineering, machine learning, and domain knowledge. Additionally, data scientists should possess critical thinking abilities, communication skills, and a curiosity to explore and analyze complex datasets.

Q4: Can you explain the data science lifecycle?
A4: The data science lifecycle typically involves several stages, starting with problem formulation and data collection. After acquiring the necessary datasets, the data is preprocessed through cleaning, transformation, and feature engineering. Next, exploratory data analysis and visualization help to gain initial insights. The modeling phase involves applying various algorithms to build and train the models. Finally, model evaluation, deployment, and maintenance complete the cycle.

Q5: What are some ethical considerations in data science?
A5: Data science involves handling vast amounts of data, often containing sensitive or personal information. Therefore, it is important to uphold ethical standards. Issues such as privacy, bias, fairness, and transparency need to be carefully considered. Data scientists must ensure that data is collected and used responsibly, with proper consent, while avoiding discriminatory practices and ensuring the results are communicated transparently.