The 5 W’s of My “Distilling Data Science” Blog
Hello, dear reader. My name is Paul Ables, a budding *insert buzzword* “data scientist” with a Medium blog page. That is a dangerous recipe, one which I hope to cook into a delectable data-driven feast.
What is an aspiring data science student to write about, and why should that matter to you? Better stated by Simon Sinek,
“Why did you get out of bed this morning, and why should anyone care?”
I suppose all things serve a purpose, even this meager corner of the web. Laying the groundwork for what content lies ahead seems wise, so we can better understand each other as author and reader.
Without further ado, here are the 5 W’s (and 1 H) of my “Distilling Data Science” blog:
1. Who
I am Paul, as previously mentioned. “Kentuckiana” is home, being born and residing in Louisville while also having Hoosier roots. I am a newly enrolled graduate student at Ball State University pursuing an M.S. in Data Science. My profession is working for Louisville Gas & Electric as a Business Analyst.
Based on the above info, you have possibly (and accurately) surmised that:
- I know my fair share of accounting and computer languages
- My love for Microsoft Excel is rooted in nostalgia more than its present-day usefulness… yet I cherish it regardless
- I create more spreadsheets “for fun” than I should admit
That is enough about me. How about you? By stumbling across this blog entry, perhaps you:
- Are my family or friend (if so, thank you)
- Are interested in data science, either as a working professional or as a curious learner (if so, this site may be helpful)
- Have strayed too deep into a random Google rabbit hole (if so, welcome!)
2. What
This blog is called “Distilling Data Science”. Its’ primary purposes are to:
- Share my journey through a graduate data science program, from the Data Mines of Moria to the fires of Mt. Python, there and back again…
- Enhance the analysis skillset by writing about the exciting parts of these courses: statistical concepts, code/software tips, probability problems, machine learning tools, visualization theory, and more. Hopefully, you learn a thing or two along the way!
- Practice consistent data collection with fun and interesting datasets, sharing them and their sources with you
3. When
I will publish content throughout the 2.5-year duration of coursework and beyond, as worthy content presents itself and time permits. My goal is to post here weekly or bi-weekly.
If a lull in writing happens, blame the professors for assigning too much busy work. Except for Dr. Li, she is great!
4. Where
Medium.com blog site: https://medium.com/@DistillingDataScience
Twitter: https://twitter.com/Distilling_Data
YouTube: No channel yet, but it may happen if the need arises…
5. Why
Data science is an exciting field of study that has exploded in popularity, yet remains shrouded in mystery. The profession has quickly caught on as companies launch newly-minted analytics departments. Former financial analysts are being rebranded as data analysts. Wannabe IT programmers are diverting their careers to become business intelligence engineers, all in the name of entering the “data science” game.
But why?
What does a data science career look like? What qualifications are needed? Is a master’s degree required, or are free online courses from EdX and Coursera sufficient?
Speaking of which, what the heck is the difference in these online offerings from CodeCademy, Udemy, EdX, Coursera, etc. Which should I choose and why?
Do you need to pour hours of your life into perfecting Jupyter Notebooks? What is a Kaggle? Is Tableau fading away or the future?
As a professional, what insights should I be seeking? Why? Do we need more metrics? Is this dashboard sufficiently or confusingly visualizing data? What is the difference between data wrangling, mining, and cleaning? Is our company’s data all of these things, and how do I know?
So. Many. Questions.
These seemingly infinite inquiries are why this website exists. There is so much I want and need to know about this field. The same goes for co-workers who recently asked me for the best “intro to data science” courses, or fellow students who share a weekly discussion board digesting class content.
Where and how do we find answers? Read on to the final section of this post…
6. How
Being a boy from Bardstown, KY means that I know my bourbon. Waking up on Sundays smelling sour mash in the air teaches you a few things about the finest export of My Old Kentucky Home.
One of those lessons is distillation. Bourbon is distilled from fermented grain, yeast, and water mash. Going through multiple rounds of this process clears the mash of impurities (and boosts alcohol content!).
“Distilling” is also crucial to successful interaction and analysis of data, as you can deduce from reading its Oxford definition:
“the extraction of the essential meaning or most important aspects of something”
I aim to publish content that keeps this definition front and center, extracting essential meaning from this data-filled journey I have embarked on.
Data science has been hailed as an exciting career path to enter. Yet it’s relatively new and lacks the longevity-bolstered foundation of law, medical, or other historical academic disciplines. Hopefully, this blog’s content helps us find answers to our queries. With each distillation and insight gained, we can continually solidify the data science foundation under our feet.
