It all seems so perfect, naturally, in the artist’s rendering of the Oakland Athletics’ proposed new stadium. Fans of all kinds flock to the ballpark by Howard Terminal — young, old, black, white, one in a wheelchair, one walking a dog. The distinctive silver cargo loaders on the bay loom to the left, while a military flyover roars overhead. The marquee declares this the 2023 World Series.
That would mark 33 years since the Athletics’ last appearance in the World Series, a stretch that would nearly rival the franchise’s chasm between pennants from 1931 to 1972. Those fruitless decades were eventful, at least, as the franchise migrated from Philadelphia to Kansas City to the Oakland-Alameda County Coliseum, where it has remained.
Like so many listed, I have to admit that I am guilty of this. I think that most apologies that are delivered digitally aren’t done so because they aren’t genuine, but because it’s so much easier to ask for forgiveness from the comfort of your couch, hiding behind a screen. When you’re face-to-face with that person, you either get too embarrassed to admit your mistake, or you’re too scared that it won’t be accepted. Somehow, whether it be to an offer of forgiveness or a dating request, people fear a corporal rejection more than a digital one, which encourages them to send these messages from a screen.
“Sorry.” Receiving that from a bubble over text has never relieved me from whatever situation led to it. I’ve always preferred an in-person apology, but I VERY rarely get those now. Living in the social media age has taken such a toll on the quality of our conversations that many are uncomfortable having in-person conversations. These awkward feelings towards face-to-face convos cause us to resort to typed characters on a screen to let out our thoughts and feelings. However, this method of communication is very dangerous …
Our minds receiving that text can pick apart every detail. “Why did he capitalize that?” “She put a period … she’s obviously mad!” While the person on the other end may have had no intentions to send off “negative vibes.” Then, the argument begins: “Why are you mad at me?”So many arguments can be prevented with a phone call. Talking face-to-face allows for optimum communication. If I ever got into a situation with a person that ended in them having to apologize, I would 100% prefer a phone call or to be apologized to in person because one, it’s way more personal, and two, it gives me the peace of mind that they are being genuine.
The only struggle with apologizing face to face is sometimes it can be very nerve racking, and for some people who are not very social it can seem almost impossible to do. I think that is why so many apologies are sent through text. But I also think that for some people it is tough to face the truth. Most of the kids in our generation now do everything through their phone so that they can hide. They don’t want to feel the agony of being wrong so they apologize through text or phone call. But like I said earlier, the most genuine and most heartfelt way to apologize in my opinion is in person, and then after just shoot them a text just to tell them how sorry you are.
I think admitting that you made a mistake and then explaining what you are going to do to not make it again, really leaves no room for the apologizee (forgive my phrasing) to be mad, at least at the apology.In short, if you want an effective apology, start by acknowledging what you did wrong, explain what you will do to NEVER do this again, and finish by passive-aggressively insulting the one you are apologizing to for being so sensitive. The last part has … mixed results.
I think that the best apologies are when the offender realizes what they have said has caused offense and they are genuinely sorry for being insensitive. With the observance of their offence, they now know not to do or say whatever it is they did that caused a negative reaction. A lot of people don’t care if they offend people, their apologies are in genuine and they continue offending others, making no changes.
An apology should consist of recognition of wrongdoing, and an expression of remorse for the event. Bad apologies are not sincere and often take the approach of blaming anything but self. Many times the wrongdoer will even guilt the recipient to make themselves feel better …Apologies are of importance because they can build and strengthen a relationship when an individual can take responsibility for their shortcoming or mistakes. It shows the love and respect one has for another when they can admit to their flaws, and hopefully aim to improve themselves. It can be corrosive, though, if one is only giving excuses or continuously having the same problem, promise to change, and producing no growth.
Codecademy’s very own Nick Duckwiler (left) and Ryan Tuck from Warby Parker (right) in our office. (📷: Mitch Boyer)
Last month, Codecademy and Warby Parker came together to work on a special Learn SQL from Scratch Capstone Project. It was during this time when I met Ryan Tuck, a Data Engineer at Warby, who played a major part in this partnership. So when he decided to drop by our office for the final QA round, I had to break out my notebook and ask some questions. Enjoy.
Hey Ryan, let’s start off with a question I’ve had for a while — what is a Data Engineer? (Is it similar to a Data Analyst or a Software Engineer?)
At Warby Parker, data engineers are responsible for creating and maintaining the plumbing required to support the data and reporting needs of the business. We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.
What languages/frameworks do you use at Warby?
On data engineering, we use Python as our general purpose programming language, as do most of the other teams in our Technology department. When it comes to databases, we use PostgreSQL for the majority of our SQL needs, and are beginning to use Amazon Athena and Google BigQuery for some of our larger datasets. We use Looker as our exclusive business intelligence entry point to all of this data.
What are some of the projects you worked on?
I’ve had the privilege of working with a lot of of smart people in every department at our company to help them solve their varied data needs, from reconciling financial data with the Accounting team to automating and modeling standardized performance metrics for our team of over 200 customer experience advisors.
As part of a team of five supporting the data needs of a rapidly growing company, I’ve tried where possible to focus on helping our analysts solve their own problems. This includes helping people learn Python and commit to our codebase, guiding the creation of data models in SQL, and encouraging people to submit pull requests to add features in Looker, our BI tool.
Seeing dozens of otherwise “non-technical” colleagues opening up PRs on a daily basis, and consequently being part of the democratization of tech that we value at Warby Parker, is probably the most rewarding “project” I’ve been a part of.
One project finished recently during our first annual “Hackweek” is called Pipes, which allows anyone at the company to easily move large amounts of data from wherever to wherever (Looker, Google Sheets, PostgreSQL, BigQuery, etc) on a regular cadence, or manually through a simple one-line chatbot interface. The adoption has been overwhelmingly positive and we’re looking to grow this sort of tooling out even more.
“We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.”
What got you into the data field?
I’ve always been drawn to analytical fields like math, and became pretty proficient in Excel during some internships in college. Once I had learned to program and learned more about data science and its applications in artificial intelligence, I knew that anything I could do to immerse myself in the world of data would be a step in the right direction.
Three and a half years ago, I landed a job as a junior software engineer at Warby Parker not fully knowing what I was in for, but am so glad I got the opportunity to help build tools to support an interesting and ever-changing data-driven culture here.
Where did you learn SQL and Python?
I had a background in C++, and was exposed to Python through an Intro to Data Science course. When Warby Parker hired me onto the Data team in 2015, I had never written a SQL query in my life, but picked it up quickly and within a few months started up internal SQL training classes, which I still teach on a monthly basis.
What does your tattoo say?
The ultimate cheatsheet.
This is Bayes’ Theorem, which is an equation that describes how to update probabilities given new evidence. Two summers ago I worked on building a tool to help predict weekly fantasy football performance. Some colleagues suggested a Bayesian approach would be appropriate, since there aren’t really enough data points in an NFL season to be able to use statistical approaches that require larger datasets, and I’d want to regularly update my predictions after each player’s latest performance.
I did a deep dive into understanding the (simple) math underlying Bayes’ Theorem and came out of that experience with a whole new worldview, understanding my entire knowledge of the world as a big and intricate probabilistic model that I was continuously updating with every experience I ever have. It was pretty transformative, and I figured that was worth a tattoo.
What is a concept in SQL/Python that’s essential to your work?
Donald Knuth said, “Premature optimization is the root of all evil.” I’ve generally found this to be true, and try to live by it in my work. For example, I’ll generally prefer to keep a data model simple by rebuilding it for all time on a daily basis using a single SQL query instead of making a more complicated model that requires iteratively adding to a table, keeping track of state, updated timestamps, when something last ran, etc.
A wise man once said, “Duplicating data makes things go fast,” but databases are already impressively fast to begin with, without implementing anything to improve performance. Ultimately, I almost always approach a problem thinking about optimizing for my time over machine time, for readability over performance, and for introducing as little cognitive overhead as is required by the problem at hand. Only once performance issues or readability issues present themselves will some code be worth a rewrite.
Last question! Since you wrote Warby Parker’s internal SQL training courses, I know there gotta be some inner Curriculum Developer in you. Can you teach a SQL concept in 2 minutes?
Sure! Have you ever written a query that yields some result set and you think, “I’d love to query the stuff I just produced like it was a table?” Enter the WITH clause.
Suppose I have a mega query that gives the transaction summaries:
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
Using WITH, I can create a temporary table within my query that I can SELECT from and treat it just like a regular old table.
I will put everything from the previous query in a parentheses and use WITH to give it the name transaction_summaries.
Then I’ll apply the date and customer filtering down below for a more readable query, to separate out all the JOIN logic from the actual WHERE filters that I want to apply on that data.
with transaction_summaries as (
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
)
select
*
from
transaction_summaries
where
first_name = 'beyonce'
and
transaction_date > '2018–01–01'
order by
total_cost desc
limit
5
If you’re familiar with subqueries, this does a similar thing but makes the SQL far more readable, even if your query isn’t quite as performant as it would have been. This is essentially an implementation of the mantra “Don’t Repeat Yourself” that’s common in the world of programming.
Incredible. And love the SQL styling! 😍
Huge shout out to Ryan and the whole Warby Parker team for making this partnership happen. Special hat tips for behind-the-scenes support from:
A Day in the Life of a Software Engineer (via Life of Luba)
With Airbnb came a revolution of sorts in the world of vacation travel and culture. We sat down with Luba Yudasina, a YouTuber, an opera singer, and a Software Engineer on the Airbnb’s Homes Platform team, to discuss software engineering and her programming journey—from Codecademy to Airbnb!
Hey Luba, let’s start with the basics! What does a Software Engineer on the Platform team do at Airbnb?
Homes Platform’s mission is to create the building blocks to power all Homes categories. Any project undertaken by our team should be reusable and extensible in some way. This means that as a backend engineer, I have a lot of opportunities to work on impactful technical projects that create systems and services to support Homes, as well as collaborate across teams to come up with the best architectural decisions and designs.
Recently, our team wrote a blog post on classifying Room Types into categories using Machine Learning and computer vision. The room-type classification problem largely resembles the ImageNet classification problem, except our team’s model outcomes are customized room-types.
After a few experiments with various models, the team chose ResNet50 due to its good balance between model performance and computation time. To make it compatible with our use case, we added two extra fully connected layers and a Softmax activation in the end.
Let’s rewind a little bit. Coming from a chemical engineering background in college, how did you make the switch into programming?
I went to the University of Waterloo in Canada—a university with the biggest co-op program in the world. Co-op means that to obtain a bachelor’s degree you must complete a certain number of internships. If you are in Engineering at Waterloo, you must complete 5 internships to graduate.
In my first and second years, I interned at chemical engineering companies and afterwards I couldn’t see myself working in the field full-time. That’s why I’m particularly grateful that I studied at Waterloo: if not for co-op, I probably would not have realized I didn’t want to work in chemical engineering until getting a full-time job after graduation.
I happened to have a lot of friends in Computer Science and Software Engineering right when I realized Chem Eng wasn’t for me. They really encouraged me to try coding, and when I decided to follow their lead I never looked back! My first online programming course was Web Development on Codecademy 🙂
“It’s a really cool time to be a software engineer and even cooler to be a female software engineer, because this is the time when women start to embrace their own unique identities and be ok with not being ‘one of the dudes.'” -Luba Yudasina
When I decided I wanted to learn computer science on my own, my goal was to get an internship in the field because working as a software engineer at a tech company would be the best test to really know if it was for me.
I happened to be in Munich, Germany on academic exchange for a whole year when I was learning how to code, so I hustled as much as I could while being there to get experience to learn quicker and have something to put down on my tech resume.
Almost immediately after arriving in Germany, I got a part time job as a developer at a game publishing company. I had a good friend in Computer Science at my German university: her and I ended up working on an Android app as a side project, etc. When I was ready, I started preparing for technical interviews. I then leveraged my network to refer me to companies and do mock technical interviews with me.
Yelp was really random though—a Yelp recruiter looked at my LinkedIn profile and didn’t even message me, but I messaged them anyway asking about internship opportunities, and that’s how I got my interview there!
Airbnb HQ in San Franciso
What is an essential app/item in your day-to-day?
Code searching! A lot of software engineering is problem solving and a lot of it is understanding other people’s code and the reasoning behind writing it a certain way. Searching through the codebases is almost essential to my day to day. Whenever I build something new or build on top of already existing tech, I need to understand how it works and is written, and code search is vital to this.
At Airbnb we use Google’s Codesearch for these purposes, but developers (myself included) also frequently use their IDEs to search for relevant code. I mostly use RubyMine or IntelliJ (depending on the codebase I’m working with).
In your videos, you’ve mentioned the intersection of gender and technology. Can you speak a bit more about that?
It’s a really cool time to be a software engineer and even cooler to be a female software engineer, because this is the time when women start to embrace their own unique identities and be ok with not being “one of the dudes.”
I think it’s particularly important to redefine the stereotypes, and I hope that with my own example I can show young girls and women interested in the field that you don’t have to give up your feminine side to be a software engineer and still be into fashion, or makeup, or art (I personally sing opera) and have other interests outside of coding and be successful in the field.
Before we wrap up, do you have anything else you would like to say to our learners?
Don’t be discouraged, learn and absorb as much as you can! If you don’t understand a concept or can’t build a project right away, know that with practice, perseverance and concentration you will get there!
Take advantage of such amazing tools as Codecademy that are there for you to take and learn. Learning anything new can be frustrating, but knowing that you can do it, staying curious, asking questions and not losing your motivation is the key to success.
Huge shoutout to Luba for this insightful interview. It’s always incredibly moving to see a Codecademy learner go on to do bigger things. Go subscribe to her YouTube channel, Life of Luba.
And thank you to the whole Homes Platform/Engineering team at Airbnb for the support. Check out their wonderful open source projects on airbnb.io.
Codecademy’s very own Nick Duckwiler (left) and Ryan Tuck from Warby Parker (right) in our office. (📷: Mitch Boyer)
Last month, Codecademy and Warby Parker came together to work on a special Learn SQL from Scratch Capstone Project. It was during this time when I met Ryan Tuck, a Data Engineer at Warby, who played a major part in this partnership. So when he decided to drop by our office for the final QA round, I had to break out my notebook and ask some questions. Enjoy.
Hey Ryan, let’s start off with a question I’ve had for a while — what is a Data Engineer? (Is it similar to a Data Analyst or a Software Engineer?)
At Warby Parker, data engineers are responsible for creating and maintaining the plumbing required to support the data and reporting needs of the business. We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.
What languages/frameworks do you use at Warby?
On data engineering, we use Python as our general purpose programming language, as do most of the other teams in our Technology department. When it comes to databases, we use PostgreSQL for the majority of our SQL needs, and are beginning to use Amazon Athena and Google BigQuery for some of our larger datasets. We use Looker as our exclusive business intelligence entry point to all of this data.
What are some of the projects you worked on?
I’ve had the privilege of working with a lot of of smart people in every department at our company to help them solve their varied data needs, from reconciling financial data with the Accounting team to automating and modeling standardized performance metrics for our team of over 200 customer experience advisors.
As part of a team of five supporting the data needs of a rapidly growing company, I’ve tried where possible to focus on helping our analysts solve their own problems. This includes helping people learn Python and commit to our codebase, guiding the creation of data models in SQL, and encouraging people to submit pull requests to add features in Looker, our BI tool.
Seeing dozens of otherwise “non-technical” colleagues opening up PRs on a daily basis, and consequently being part of the democratization of tech that we value at Warby Parker, is probably the most rewarding “project” I’ve been a part of.
One project finished recently during our first annual “Hackweek” is called Pipes, which allows anyone at the company to easily move large amounts of data from wherever to wherever (Looker, Google Sheets, PostgreSQL, BigQuery, etc) on a regular cadence, or manually through a simple one-line chatbot interface. The adoption has been overwhelmingly positive and we’re looking to grow this sort of tooling out even more.
“We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.”
What got you into the data field?
I’ve always been drawn to analytical fields like math, and became pretty proficient in Excel during some internships in college. Once I had learned to program and learned more about data science and its applications in artificial intelligence, I knew that anything I could do to immerse myself in the world of data would be a step in the right direction.
Three and a half years ago, I landed a job as a junior software engineer at Warby Parker not fully knowing what I was in for, but am so glad I got the opportunity to help build tools to support an interesting and ever-changing data-driven culture here.
Where did you learn SQL and Python?
I had a background in C++, and was exposed to Python through an Intro to Data Science course. When Warby Parker hired me onto the Data team in 2015, I had never written a SQL query in my life, but picked it up quickly and within a few months started up internal SQL training classes, which I still teach on a monthly basis.
What does your tattoo say?
The ultimate cheatsheet.
This is Bayes’ Theorem, which is an equation that describes how to update probabilities given new evidence. Two summers ago I worked on building a tool to help predict weekly fantasy football performance. Some colleagues suggested a Bayesian approach would be appropriate, since there aren’t really enough data points in an NFL season to be able to use statistical approaches that require larger datasets, and I’d want to regularly update my predictions after each player’s latest performance.
I did a deep dive into understanding the (simple) math underlying Bayes’ Theorem and came out of that experience with a whole new worldview, understanding my entire knowledge of the world as a big and intricate probabilistic model that I was continuously updating with every experience I ever have. It was pretty transformative, and I figured that was worth a tattoo.
What is a concept in SQL/Python that’s essential to your work?
Donald Knuth said, “Premature optimization is the root of all evil.” I’ve generally found this to be true, and try to live by it in my work. For example, I’ll generally prefer to keep a data model simple by rebuilding it for all time on a daily basis using a single SQL query instead of making a more complicated model that requires iteratively adding to a table, keeping track of state, updated timestamps, when something last ran, etc.
A wise man once said, “Duplicating data makes things go fast,” but databases are already impressively fast to begin with, without implementing anything to improve performance. Ultimately, I almost always approach a problem thinking about optimizing for my time over machine time, for readability over performance, and for introducing as little cognitive overhead as is required by the problem at hand. Only once performance issues or readability issues present themselves will some code be worth a rewrite.
Last question! Since you wrote Warby Parker’s internal SQL training courses, I know there gotta be some inner Curriculum Developer in you. Can you teach a SQL concept in 2 minutes?
Sure! Have you ever written a query that yields some result set and you think, “I’d love to query the stuff I just produced like it was a table?” Enter the WITH clause.
Suppose I have a mega query that gives the transaction summaries:
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
Using WITH, I can create a temporary table within my query that I can SELECT from and treat it just like a regular old table.
I will put everything from the previous query in a parentheses and use WITH to give it the name transaction_summaries.
Then I’ll apply the date and customer filtering down below for a more readable query, to separate out all the JOIN logic from the actual WHERE filters that I want to apply on that data.
with transaction_summaries as (
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
)
select
*
from
transaction_summaries
where
first_name = 'beyonce'
and
transaction_date > '2018–01–01'
order by
total_cost desc
limit
5
If you’re familiar with subqueries, this does a similar thing but makes the SQL far more readable, even if your query isn’t quite as performant as it would have been. This is essentially an implementation of the mantra “Don’t Repeat Yourself” that’s common in the world of programming.
Incredible. And love the SQL styling! 😍
Huge shout out to Ryan and the whole Warby Parker team for making this partnership happen. Special hat tips for behind-the-scenes support from:
A Day in the Life of a Software Engineer (via Life of Luba)
With Airbnb came a revolution of sorts in the world of vacation travel and culture. We sat down with Luba Yudasina, a YouTuber, an opera singer, and a Software Engineer on the Airbnb’s Homes Platform team, to discuss software engineering and her programming journey—from Codecademy to Airbnb!
Hey Luba, let’s start with the basics! What does a Software Engineer on the Platform team do at Airbnb?
Homes Platform’s mission is to create the building blocks to power all Homes categories. Any project undertaken by our team should be reusable and extensible in some way. This means that as a backend engineer, I have a lot of opportunities to work on impactful technical projects that create systems and services to support Homes, as well as collaborate across teams to come up with the best architectural decisions and designs.
Recently, our team wrote a blog post on classifying Room Types into categories using Machine Learning and computer vision. The room-type classification problem largely resembles the ImageNet classification problem, except our team’s model outcomes are customized room-types.
After a few experiments with various models, the team chose ResNet50 due to its good balance between model performance and computation time. To make it compatible with our use case, we added two extra fully connected layers and a Softmax activation in the end.
Let’s rewind a little bit. Coming from a chemical engineering background in college, how did you make the switch into programming?
I went to the University of Waterloo in Canada—a university with the biggest co-op program in the world. Co-op means that to obtain a bachelor’s degree you must complete a certain number of internships. If you are in Engineering at Waterloo, you must complete 5 internships to graduate.
In my first and second years, I interned at chemical engineering companies and afterwards I couldn’t see myself working in the field full-time. That’s why I’m particularly grateful that I studied at Waterloo: if not for co-op, I probably would not have realized I didn’t want to work in chemical engineering until getting a full-time job after graduation.
I happened to have a lot of friends in Computer Science and Software Engineering right when I realized Chem Eng wasn’t for me. They really encouraged me to try coding, and when I decided to follow their lead I never looked back! My first online programming course was Web Development on Codecademy 🙂
“It’s a really cool time to be a software engineer and even cooler to be a female software engineer, because this is the time when women start to embrace their own unique identities and be ok with not being ‘one of the dudes.'” -Luba Yudasina
When I decided I wanted to learn computer science on my own, my goal was to get an internship in the field because working as a software engineer at a tech company would be the best test to really know if it was for me.
I happened to be in Munich, Germany on academic exchange for a whole year when I was learning how to code, so I hustled as much as I could while being there to get experience to learn quicker and have something to put down on my tech resume.
Almost immediately after arriving in Germany, I got a part time job as a developer at a game publishing company. I had a good friend in Computer Science at my German university: her and I ended up working on an Android app as a side project, etc. When I was ready, I started preparing for technical interviews. I then leveraged my network to refer me to companies and do mock technical interviews with me.
Yelp was really random though—a Yelp recruiter looked at my LinkedIn profile and didn’t even message me, but I messaged them anyway asking about internship opportunities, and that’s how I got my interview there!
Airbnb HQ in San Franciso
What is an essential app/item in your day-to-day?
Code searching! A lot of software engineering is problem solving and a lot of it is understanding other people’s code and the reasoning behind writing it a certain way. Searching through the codebases is almost essential to my day to day. Whenever I build something new or build on top of already existing tech, I need to understand how it works and is written, and code search is vital to this.
At Airbnb we use Google’s Codesearch for these purposes, but developers (myself included) also frequently use their IDEs to search for relevant code. I mostly use RubyMine or IntelliJ (depending on the codebase I’m working with).
In your videos, you’ve mentioned the intersection of gender and technology. Can you speak a bit more about that?
It’s a really cool time to be a software engineer and even cooler to be a female software engineer, because this is the time when women start to embrace their own unique identities and be ok with not being “one of the dudes.”
I think it’s particularly important to redefine the stereotypes, and I hope that with my own example I can show young girls and women interested in the field that you don’t have to give up your feminine side to be a software engineer and still be into fashion, or makeup, or art (I personally sing opera) and have other interests outside of coding and be successful in the field.
Before we wrap up, do you have anything else you would like to say to our learners?
Don’t be discouraged, learn and absorb as much as you can! If you don’t understand a concept or can’t build a project right away, know that with practice, perseverance and concentration you will get there!
Take advantage of such amazing tools as Codecademy that are there for you to take and learn. Learning anything new can be frustrating, but knowing that you can do it, staying curious, asking questions and not losing your motivation is the key to success.
Huge shoutout to Luba for this insightful interview. It’s always incredibly moving to see a Codecademy learner go on to do bigger things. Go subscribe to her YouTube channel, Life of Luba.
And thank you to the whole Homes Platform/Engineering team at Airbnb for the support. Check out their wonderful open source projects on airbnb.io.
Included in the free download are two desktop options – one with the calendar and one without the calendar. The others include one wallpaper per device.
In the screen shot below, you can see a student, Madison from New Jersey, responding to Christian in Pittsburgh. One of the questions Madison asks is, “Are these the effects of naturally increasing temperatures, or the doing of man made emissions?”
4. What’s going on in this graph?
Just like photographs, graphs tell stories. After students have “noticed and wondered,” we ask them: What’s going on in this graph? What story can it tell?
Here are some examples of the comments students made about the above graph about winter temperatures.
This graph shows that although the average winter temperature in the US is not increasing at a steady rate, overall the temperatures are getting warmer with time. This is in favour of the discussion about climate change causing global warming. — Tatyana from New Zealand
This graph depicts how the winter temperatures from the 1900s differ from the 2000s. And how the temperatures are gradually getting warmer. This could be happening due to global warming or maybe how they say the sun is gradually getting closer and closer to our planet. — Matthew Laing from Philadelphia
From brief observations, I can conclude that this is a graph depicting the differences between the average winter temperature in 1900 and the present average winter temperature at the time. Because of this, I’m curious if an increase in air pollution, carbon dioxide, and deforestation is responsible for the overall increase of winter temperatures on Earth. This graph could capture how pollution and an increase of carbon dioxide within the atmosphere because the gas prevents small bits of heat from the sun from escaping the Earth’s atmosphere, forcing it to bounce back to the Earth’s surface as more thermal energy from the sun reaches our planet. A similar phenomenon has been present on Venus, which, due to the gases within Venus’ atmosphere capturing the majority of thermal energy from the sun, has made Venus the hottest planet in the Solar System. — Ben S. from Allen Tex.
5. Come up with a catchy headline.
This past summer Robert Lochel, a math teacher in the Hatboro-Horsham School District, mentioned to us that he always asked students to write a catchy headline after they were done noticing and wondering. We liked the idea so much, we added it to our weekly protocol.
Here are a few examples of students’ headlines about the graph above.
• “You Thought This Winter Was Cold? Check This Graph,” by Nathan of Virginia
• “Is Earth on the Hot Seat?,” by Kero K. and Jon I. from Hampton High School
• “Dreaming of a Green Christmas,” by Isaac from Hampton High School
• “Doomsday Deviation,” by Michael, Harper, Joseph and Owen from Hampton High School
_________
Part II: A Collection of Climate Change-Related Graphs by The Times
1. This graph shows the consumption of tobacco products by students, with e-cigarettes broken out separately, from 2011 to 2018. The graph originally appeared elsewhere on NYTimes.com.
After looking closely at the graph above (or at this full-size image), think about these three questions:
• What do you notice? • What do you wonder? What are you curious about that comes from what you notice in the graph? • What might be going on in this graph? Write a catchy headline that captures the graph’s main idea. If your headline makes a claim, tell us what you noticed that supports your claim.
The questions are intended to build on one another, so try to answer them in order. Start with “I notice,” then “I wonder,” and end with “The story this graph is telling is ….” and a catchy headline.
2. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment, although teachers of younger students are welcome to post what their students have to say or they can have their students use this same activity on Desmos.)
3. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button or the @ symbol to address that student directly.
On Wednesday, March 6, our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern Time to help students’ understanding go deeper. You might use their responses as models for your own.
4. On the afternoon of Thursday, March 7, we will reveal more information about the graph at the bottom of this post. Students, we encourage you to post an additional comment after reading the reveal. How does the original New York Times article and the moderators’ comments help you see the graph differently? Try to incorporate the statistical terms defined in the Stat Nuggets in your response.
Codecademy’s very own Nick Duckwiler (left) and Ryan Tuck from Warby Parker (right) in our office. (📷: Mitch Boyer)
Last month, Codecademy and Warby Parker came together to work on a special Learn SQL from Scratch Capstone Project. It was during this time when I met Ryan Tuck, a Data Engineer at Warby, who played a major part in this partnership. So when he decided to drop by our office for the final QA round, I had to break out my notebook and ask some questions. Enjoy.
Hey Ryan, let’s start off with a question I’ve had for a while — what is a Data Engineer? (Is it similar to a Data Analyst or a Software Engineer?)
At Warby Parker, data engineers are responsible for creating and maintaining the plumbing required to support the data and reporting needs of the business. We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.
What languages/frameworks do you use at Warby?
On data engineering, we use Python as our general purpose programming language, as do most of the other teams in our Technology department. When it comes to databases, we use PostgreSQL for the majority of our SQL needs, and are beginning to use Amazon Athena and Google BigQuery for some of our larger datasets. We use Looker as our exclusive business intelligence entry point to all of this data.
What are some of the projects you worked on?
I’ve had the privilege of working with a lot of of smart people in every department at our company to help them solve their varied data needs, from reconciling financial data with the Accounting team to automating and modeling standardized performance metrics for our team of over 200 customer experience advisors.
As part of a team of five supporting the data needs of a rapidly growing company, I’ve tried where possible to focus on helping our analysts solve their own problems. This includes helping people learn Python and commit to our codebase, guiding the creation of data models in SQL, and encouraging people to submit pull requests to add features in Looker, our BI tool.
Seeing dozens of otherwise “non-technical” colleagues opening up PRs on a daily basis, and consequently being part of the democratization of tech that we value at Warby Parker, is probably the most rewarding “project” I’ve been a part of.
One project finished recently during our first annual “Hackweek” is called Pipes, which allows anyone at the company to easily move large amounts of data from wherever to wherever (Looker, Google Sheets, PostgreSQL, BigQuery, etc) on a regular cadence, or manually through a simple one-line chatbot interface. The adoption has been overwhelmingly positive and we’re looking to grow this sort of tooling out even more.
“We use software engineering practices to automate the work of data cleaning, normalizing, and model building so that data is always ready to be consumed by data analysts in every department.”
What got you into the data field?
I’ve always been drawn to analytical fields like math, and became pretty proficient in Excel during some internships in college. Once I had learned to program and learned more about data science and its applications in artificial intelligence, I knew that anything I could do to immerse myself in the world of data would be a step in the right direction.
Three and a half years ago, I landed a job as a junior software engineer at Warby Parker not fully knowing what I was in for, but am so glad I got the opportunity to help build tools to support an interesting and ever-changing data-driven culture here.
Where did you learn SQL and Python?
I had a background in C++, and was exposed to Python through an Intro to Data Science course. When Warby Parker hired me onto the Data team in 2015, I had never written a SQL query in my life, but picked it up quickly and within a few months started up internal SQL training classes, which I still teach on a monthly basis.
What does your tattoo say?
The ultimate cheatsheet.
This is Bayes’ Theorem, which is an equation that describes how to update probabilities given new evidence. Two summers ago I worked on building a tool to help predict weekly fantasy football performance. Some colleagues suggested a Bayesian approach would be appropriate, since there aren’t really enough data points in an NFL season to be able to use statistical approaches that require larger datasets, and I’d want to regularly update my predictions after each player’s latest performance.
I did a deep dive into understanding the (simple) math underlying Bayes’ Theorem and came out of that experience with a whole new worldview, understanding my entire knowledge of the world as a big and intricate probabilistic model that I was continuously updating with every experience I ever have. It was pretty transformative, and I figured that was worth a tattoo.
What is a concept in SQL/Python that’s essential to your work?
Donald Knuth said, “Premature optimization is the root of all evil.” I’ve generally found this to be true, and try to live by it in my work. For example, I’ll generally prefer to keep a data model simple by rebuilding it for all time on a daily basis using a single SQL query instead of making a more complicated model that requires iteratively adding to a table, keeping track of state, updated timestamps, when something last ran, etc.
A wise man once said, “Duplicating data makes things go fast,” but databases are already impressively fast to begin with, without implementing anything to improve performance. Ultimately, I almost always approach a problem thinking about optimizing for my time over machine time, for readability over performance, and for introducing as little cognitive overhead as is required by the problem at hand. Only once performance issues or readability issues present themselves will some code be worth a rewrite.
Last question! Since you wrote Warby Parker’s internal SQL training courses, I know there gotta be some inner Curriculum Developer in you. Can you teach a SQL concept in 2 minutes?
Sure! Have you ever written a query that yields some result set and you think, “I’d love to query the stuff I just produced like it was a table?” Enter the WITH clause.
Suppose I have a mega query that gives the transaction summaries:
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
Using WITH, I can create a temporary table within my query that I can SELECT from and treat it just like a regular old table.
I will put everything from the previous query in a parentheses and use WITH to give it the name transaction_summaries.
Then I’ll apply the date and customer filtering down below for a more readable query, to separate out all the JOIN logic from the actual WHERE filters that I want to apply on that data.
with transaction_summaries as (
select
transactions.date as transaction_date,
sum(items.price) as total_cost,
count(*) as number_of_items
from
transactions
inner join
customers
on
customers.id = transactions.customer_id
inner join
transaction_items
on
transactions.id = transaction_items.transaction_id
inner join
items
on
items.id = transaction_items.item_id
)
select
*
from
transaction_summaries
where
first_name = 'beyonce'
and
transaction_date > '2018–01–01'
order by
total_cost desc
limit
5
If you’re familiar with subqueries, this does a similar thing but makes the SQL far more readable, even if your query isn’t quite as performant as it would have been. This is essentially an implementation of the mantra “Don’t Repeat Yourself” that’s common in the world of programming.
Incredible. And love the SQL styling! 😍
Huge shout out to Ryan and the whole Warby Parker team for making this partnership happen. Special hat tips for behind-the-scenes support from:
