Last year on my birthday I was between jobs. I spent half the day in interviews before heading back home for a sushi dinner with friends and loved ones.
The year before that, I had a job I wasn’t sure about, and I spent half the day taking the GRE. The proctor called me crazy for signing up to take a test on my birthday. I think I had sushi on that day, too.
This year on my birthday, I seem to have reaped the benefits of the efforts of my past two birthdays. Continue reading
Since starting my General Assembly Data Science course, I’d gotten swept away with a million different things – working on my project, tentatively picking up a writing project I’ve had on hold FOREVER (more info on that coming soon), being asked to be a part-time Teaching Assistant for similar data bootcamp, of which I promptly had to leave for an exciting new full time job in data science, and then prepping to moving to a new apartment – that I just couldn’t keep the promise of a weekly update for the goings-on of my class.
As I’m writing this, while I should be putting some finishing touches on my final paper for my course, I wanted to do a quick recap of the last several weeks of my class.
Week 4 was the first week back after winter break, so it was a bit hard to get back into the swing of things, even though I had been diligently (mostly) working on my project.
On Monday, we were introduced to Logistic Regression, or ‘hipster regression’ as our instructor put it. Not exactly sure why it’s called that…
Anyway, logistic regression is a method of machine learning that’s basically used everywhere. From fraud detection to medical diagnoses to customer churn. I was already familiar with the method before the lesson, and it’s one that I plan to implement for my current project, but I was learning it formally for the first time. Discovering the math behind logistic regression was incredibly helpful in understanding its pros and cons in prediction.
An illustration of the logistic function from class
On Wednesday we discussed another method called Naive Bayes Classification. Completely new material to me and I’m honestly still daunted by the math and all the definitions for “prior probability”, “posterior probability”, and why it’s even called Naive in the first place (because it assumes independence of features, which may not be true.)
The instructor seemed to sense that many of us were stumped and reminded us that it’s okay!
Inspirational slide from class 🙂
Well, I’m definitely outside of my comfort zone now. Homework this week was challenging, but I’m excited about what I’ve learned and how I could apply it. I just hope I’m doing it correctly… time to go to office hours.
Despite fighting a cold, lacking practice, and having any actual recording equipment, I made an amateur recording of Chris Hadfield’s cover of David Bowie’s ‘Space Oddity’ as a loved one’s Christmas present. I even took the creative liberty of adding ambulance sirens in the background 🙂
I decided to share the recording with the rest of the world following David Bowie’s recent passing.
So here it is, my Take 1 of Space Oddity:
Week 3 was jam-packed with material as we prepared for the two week holiday break.
On Monday we did a quick exercise with Pandas which I found extremely useful. I’ve gotten too accustomed to working with R, so Pandas’ dataframes and Python’s superior efficiency are a joy to work with. I’ll definitely be using it more for my future data projects, particularly the upcoming one for this course.
We also talked about model evaluation on Monday: how to avoid overfitting a model to old data so that predictions for new data are accurate.
That fit well with Wednesday’s discussion of bias and variance, for which I have this lovely illustration to do the explaining of both for me.
We then talked about using regularization to balance the bias-variance tradeoffs. I’m still wrapping my head around it, so thankfully I have a couple weeks to carefully review.
I’m also hoping to spend the next weeks off gathering and cleaning data for my aforementioned final project!
What’s my project about? Well, I did mention in my Week 1 post about building off the work I’ve done for the Simple Word Count Tracker and my NaNoWriMo research efforts. I hope to build an analytical model that can predict whether or not a writer will win the NaNoWriMo challenge of writing 50,000 words in November.
My biggest first obstacle is acquiring the rest of the data I will need. I have a lot of word count data, obviously, but just for the most recent NaNoWriMo. I also want to acquire data besides word counts, such as whether a user was a donor to NaNoWriMo, who and what are their favorite authors and books, the year they joined NaNoWriMo, where they are located, and any other variable that might be an indicator of their likeliness of “winning”. I’ve realized the best way to get this data that isn’t just emailing the website organizers and asking for it (believe me, I tried) is to write a script to scrape the html of user profiles on the website.
Thankfully my instructor and TA have pointed me in the direction of a lot of good resources for me to start this. Bring it on.
Here’s to a fun and productive break!
The pace of week 1 felt slow for me; week 2 went a bit faster.
Monday involved a quick overview of Python syntax. I’ve used Python before, but not extensively, so I found the in-class exercise a bit challenging. Thankfully, a quick review before the next class prepared me for the tougher exercise on Wednesday. Yay studying! It works, kids!
Wednesday’s exercise was on the K-Nearest Neighbors Algorithm. I talk more about this in more technical detail on my data blog. It was a fun first glance into the world of Machine Learning Algorithms. Can’t wait to learn more complex predictive analytics as the class continues!
You may have gotten a hint of it from reading a few of my posts here, but if you follow me elsewhere say, on this blog, you’ll know that I’m passionate about data science. I decided to enroll in General Assembly’s Data Science part time, semiweekly course. 11 weeks of programming, statistics, and sifting through data. If you’re a geek like me, that translates to approximately 11 weeks of fun!
The class is set up to focus on developing ‘Type A’ data scientists (analyst leaning) rather than ‘Type B’ data scientists (programmer leaning), which I am a little disappointed about. While I do need a strong refresher in my statistics, I definitely wanted to ramp up my coding abilities more. Still, I’m hoping to get a lot from this course, especially in regards to learning complex machine learning algorithms and better Python programming practices. Above all, it will serve as a good stepping stone for finding a new role in data science, or for preparing myself for further studies in data science.
The first week so far has just been introductions. My classmates are so diverse in personal, professional, and academic backgrounds. I’m certain I’m one of the youngest in the class, if not the youngest. Some have had a lot of experience working with data as analysts with no programming background, and others have done a ton of coding, but lack a strong statistics foundation. I feel like I’m somewhere in the middle, but we’ll see.
In the Monday class, after introductions and orientation, we learned some command line basics. Easy stuff for me, as I’d been using it extensively in school and in work, but I recognize it’s very new and cryptic to others. I finished my exercises early and helped the people sitting next to me with theirs. (I felt really cool about that.)
On Wednesday, some alums from the previous class visited to present their class projects. One student created a model to predict GDP from data collected from the CIA website. Another built a recommendation system for meal recipes based on inputted ingredients. I felt both excited and scared about my own project. The instructor told us to expect putting in 200 to 300 hours of work! Still, I’m stoked that in just a few weeks I’ll know enough to start putting together insightful analyses and predictive models of my own. Can’t wait! I hope I can use the data I collected from the Simple Word Count Tracker I created for NaNoWriMo. I think it would be awesome to be able to use data to predict if someone could win NaNoWriMo before NaNoWriMo even began. More on that as class goes on, I guess.
Wednesday was also supposed to be the day we go over some Python basics, however, we got a little behind during the lesson on Git. I’m familiar with both Python and Git, so that lesson felt pretty slow for me even though it was good review. Hopefully things will pick up in next class. Looking forward to it.
In the meantime, here’s a quick visualization (created in Tableau) of something I learned this week about the Data Science Workflow:
It’s that time again! Life’s been crazy busy and I haven’t posted any new content in a while. I’m hoping that will change soon, and that this month’s writing frenzy will force me back into the habit again.
I’m writing under Nicaless as usual 🙂
You can also keep track of my progress, as well as the progress of the rest of NaNoWriMo, with this Simple Word Count Tracker shiny web app I quickly spun up in R.
I was inspired by the NaNoWriMo official word count tracker and wanted to come up with my own way of tracking my writing progress even when it’s not NaNoWriMo.
Take a look! It’s still a work in progress, so any feedback or suggestions are most welcome!
Ok. Back to writing. Good luck with NaNoWriMo everyone!
Due to a flight delay, Iggy didn’t make it in time to their mother’s birthday party, so Terra invited his long-time-no-see brother for a chat at the same cafe he conducted the interview with Airlie.
“I gave her a call of course,” a jet-lagged Iggy sipped his red eye while Terra grimaced at the imagined bitter taste, “She said ‘I’m so glad you’re here! Why such a late flight? You know it would be so much easier for you to visit if you found a position at the hospital nearby…'”
“Of course.”
“I promised her dinner tonight. You can come with. The patronizing would be divided up evenly. Easier to bear.”
“I’ll think about it. I’ve still got work to do. Lots of emails to reply, too.” Airlie was expecting an answer soon. “Some code to push…”
“Sleeping well?”
“Not really. Mom asked me that, too.”
“Same. And I had the same answer.” Iggy took a moment to admire the cafe’s view of the lake, the geese and the dense algae floating upon it.
“Guess the grass really isn’t greener on the other side, huh?”
“Always seems that way, though,” Iggy gulped the rest of the coffee. The last drop down his throat, he promptly brought the cardboard cup down on the table with a loud thunk. “But that doesn’t mean you should move every time your lawn starts looking a little brown. Just water the fucking thing.” Terra jumped in his seat at the outburst. “Well, unless California’s still in a draught. Then maybe you should move. Or plant some fucking cacti. Your lawn would look so fucking cool with cacti.”
“Ig, are you okay?”
Iggy blinked rapidly a few times, first at Terra, then out the window again. “This is a great cafe. You work out of here often?”
“Not really.”
“You should. The view is great. The coffee is strong. If you can’t find the energy here to get your startup off the ground,” Terra rolled his eyes, “At least you could pretend you have a really cool front yard for your office.”
“Sounds like a plan.”
A kind of sequel to Interview.
At least the view was nice.
It wasn’t the most conventional setting for an interview – a bit noisy, cramped, and crowded – but Terra was more annoyed by the timing of the meeting, rather than the locale. In fact, the cafe was only a five minute drive away from his next appointment. That helped a little with the timing bit. Terra checked his watch. His interviewee was almost ten minutes late. That didn’t help.
Terra formulated a number of reasonable but likely to be rejected anyway excuses for being late when Airlie finally walked in.
Airlie looked like any typical fresh-out-of-college, desperate-for-work grad.
“Sorry I’m late. Haven’t yet shook the habit of Berkeley time!” the kid joked.
And he had the attitude of any typical millennial.
“Love the venue by the way. I came here all the time to study. The lake is beautiful, isn’t it? So green and alive! I’d meditate over the peace and serenity of it, but I’m usually too hyped up on the caffeine from their bomb iced mochas!” Interesting guy. And full of jokes it seemed.
“Let’s get started shall we?” Terra dove right into the questions. “Why do you want to work for Futuru?”
“Well, since I majored in Computer Science…” As Airlie went on to explain how his dime a dozen degree got him interested data science and the crazy world of Silicon Valley startups, a familiar, nagging voice in Terra’s head provided a different answer.
Why not get a more stable job, hon? One that pays the bills? That way, you can do all the fun stuff you’re passionate about without working yourself to the bone, staying up late, getting no reward for it…
“… but most of all I want to make a difference in the world, and I can see myself doing that at Futuru.” Airlie finished.
“Good answer.” Terra hadn’t heard a single word. The voice in his head continued.
Change the world? Hah, a noble cause, that’s for sure. Most people who say that are actually just in it for the fame and fortune. I hope that’s not you, sweetie. Have you really thought through what you’re doing with this company?
“And, what do you think you can contribute to the team at Futuru that’s unique?
Airlie flashed a confident grin. “I like to consider myself a jack of all trades. I figure that’s always a useful person to have in any team…”
Jack of all trades and master of none, they say. You’re going to have to settle on something eventually. You can’t be starting random companies every couple years for the rest of your life. Variety doesn’t pay for retirement as much as focus and single-minded commitment do. See, your father agrees with me.
“What are you hoping to get out of this position?” This was the last question. Terra checked his watch again. He hoped he wasn’t appearing to be rude, but given the tardiness and the air-headed responses to his questions, Terra didn’t feel the kid would be a good fit. Going any further would be a waste of time.
Airlie paused before giving his reply. “Well… I can continue saying the usual bullshit…” The change in tone caught Terra off guard, “Really, I do want to learn and be part of a cool team, but look at me. I’m sure you’ve put it together by now. I just graduated from Cal and I need to get my shit together. Most of all I need money. That’s what I’m hoping to get out of this. I’m also hoping I can prove that I can hustle, so that when I’m done with this soul searching business and finally figure out what I actually want to do, I have some more believable answers to these stupid questions everyone tries to ask me.”
Terra was stunned. And then he felt bad for the kid. These stupid questions? They’ll never stop asking them. “That’s a bold answer.”
“Yup,” the confident grin returned. “I’m trying to stand out. Is it working?”
“It definitely is. We’ll keep in touch.” Terra smiled and held out his hand. They shook and said their polite goodbyes.
Terra rushed out of the cafe to his car, double-checked that his mother’s birthday gift was securely fastened in his passenger seat, and drove to her party.
