No programming just yet; this is mainly a history and background lesson.
At some point in the last few years, you've most likely stumbled across at least one of the many memes surrounding "big data" and "data science."
The level of ubiquity with which these terms have thoroughly saturated the tech sector's vernacular has rendered these terms almost meaningless. Indeed, many have argued that data science may very well not be anything new, but rather a rehashing and rebranding of ideas that did not gain traction previously, for whatever reason.
Before differentiating data science as a field unto itself and justifying its existence, I'll first offer a working definition. In my opinion, the most cogent and concise definition of data science is encapsulated in the following Venn diagram:
Data Science as a proper field of study is the confluence of three major aspects:
1: Hacking skills: the ability to code, and knowledge of available tools.
2: Math and statistics: strong quantitative skills that can be implemented in code.
3: Substantive expertise: some specialized area of emphasis.
The third point is crucial to the overall definition of data science, and is also the reason that defining data science as a distinct field is so controversial. After all, isn't a data scientist whose substantive experience is biology just a quantitative biologist with strong programming skills?
I'll instantiate the answer to this question with a somewhat snarky data science definition I've stumbled across before:
Data Scientist (n.): Person who is better at statistics than any software engineer, and better at software engineering than any statistician.
The best answer here is "it depends." This being a college course, however, we'll need to be more rigorous than that if we expect to get any credit.
A big part of the ascendence of data science is, essentially, Moore's Law.
Where once upon a time, digital storage was a luxury and available processing power required hours to perform the most basic computations, we're now right smack in the middle of an exponential expansion of digitized data creation and enough processing power to crunch a significant portion of it.
In effect, data science is not "new", but rather the result of technologies that have made such theoretical advancements plausible in practice.
There is, nevertheless, a lot of overlap between this new field of "data science" and those that many regard as the progenitors: statistics and machine learning.
Many data scientists are from academia, with Ph.D.s! (e.g. machine learning, statistics)
Many other data scientists have stronger software engineering backgrounds.
To quote Joel Grus in his book, Data Science from Scratch:
In short, pretty much no matter how you define data science, you'll find practitioners for whom the definition is totally, absolutely wrong.
So we fall back to: "a professional who uses scientific methods to liberate and create meaning from raw data"
Unfortunately, this is too overly broad to be useful; applied statisticians would claim this definition is, almost word-for-word, what they've been doing for centuries.
Is there any hope?!
(spoiler alert: yes)
While the popular media tropes of "big data," "skills," and "jobs" don't inherently justify the spawn of a new field, there is nonetheless still a solid case to be made for a data science entity.
It's important to note first: data science did not develop overnight.
Despite the seemingly rapid rise of Data Science as a field, "big data" as a meme, and the data scientist as an industry gold standard, this was something envisioned over 50 years ago by John Turkey in his book, The Future of Data Analytics (1962!)
Presented broad concepts of
as their own field, not just branches or extensions of math and stats.
There is legitimate value in training people in the practice of extracting information from data.
from "getting acquainted" with the data
to delivering major conclusions based on it
This is the field of "Greater Data Science", or GDS.
Ever heard this?
"A data scientist spends 80% of their time cleaning and rearranging their data."
It's a little rhetorical (what about cat videos?)
But data are messy, often missing certain values or corrupted in other ways that would break naive attempts at analysis.
Furthermore, there's something to be said for exploring data beforehand, "getting a feel for it."
No such thing as an assumption-free algorithm that will "just work" for any data!
Understanding the data, really intuitively getting it, will dramatically improve our lives (of trying to analyze it).
There are countless data formats, with more appearing all the time:
Some examples:
More complicated examples:
Computing tools change; computer science fundamentals don't.
...nevertheless, you'll need to be well-versed in a broad range of tools for reading, analyzing, and interpreting data at each stage of the data science pipeline.
"If you can't write about it, you can't prove you know or understand it."
Not a lot of opportunity for written answers in this course...
...but the art of visualization is a close proxy!
Visualization is rguably one of the most important aspects of data science, as it is one of (if not the) primary ways in which analysis results are conveyed.
Broadly speaking, there are two modeling cultures:
Data scientists should be familiar with and competent in both modeling paradigms.
Always need a healthy dose of meta!
GDS should be able to evaluate itself.
Oh, and: there is a lot of money in data science. A lot.
As our society relies ever more heavily on digitized data collection and automated decision making, those with the right skill sets to help shape these new infrastructures will continue to be in demand for awhile.
Everybody's building a data science program these days
...and UGA is no exception!
Not just tech companies! Any company that deals with any kind of data will likely have a need for highly trained data scientists.
We live in an era where
have combined to create a perfect storm for those with the skills to ingest, structure, and analyze all the data that is being generated.
1: Why is the combination of hacking skills and substantive expertise, devoid of math and statistics knowledge, considered the "danger zone"?
2: What is the difference between machine learning and artificial intelligence?
3: Of the six divisions of "GDS," which one do you think is most prevalent in mainstream media? Why?
4: What's wrong with saying that data science is just statistics, but with computers?
5: Think of a company you wouldn't normally consider a "tech company," and contemplate how they might use a data science division to improve their bottom line.
Lectures: Mondays, Wednesdays, and Fridays.
Programming Assignments: 10 of them (not including "Assignment 0"), all to be done on JupyterHub. Released every Tuesday & Thursday.
Exams: 2 of them!
Slack: Dedicated Slack team, https://eds-uga-csci1360.slack.com, to ask and answer questions.
Web portal through which all the programming assignments will be done!
Your initial username/password are BOTH your UGA MyID. So if your UGA MyID is "ugadawgs", you would log in with username ugadawgs and password ugadawgs.
It is NOT TIED into CAS, so don't use your "usual" MyID password!
We'll go over how to change your JupyterHub password, but to repeat: your initial JupyterHub password is NOT YOUR CURRENT UGA PASSWORD. Your initial JupyterHub username and password are BOTH your MyID username.
Ample opportunities for extra credit on assignments and exams.
Don't ask about doing "extra" extra credit. Just don't. kthx bai
Please don't copy code.
This is where all course communication will take place.
You're still welcome to send me emails (put "CSCI 1360E" in the subject), but I'll probably just answer the questions in Slack anyway.