WASHINGTON — The Food and Drug Administration is accusing Juul and Altria of reneging on promises they made to the government to keep e-cigarettes away from minors.
Dr. Scott Gottlieb, the agency’s commissioner, is drafting letters to both companies that will criticize them for publicly pledging to remove nicotine flavor pods from store shelves, while secretly negotiating a financial partnership that seems to do the opposite. He plans to summon top executives of the companies to F.D.A. headquarters to explain how they will stick to their agreements given their new arrangement.
… The contretemps with the F.D.A. is only the latest headache in Washington for Juul, a company that has had much more success capturing market share in an emerging industry than in navigating the government scrutiny that comes with it.
For the first time, one of the top prizes in has been given to a woman. On March 19, the Norwegian Academy of Science and Letters announced it has awarded this year’s Abel Prize — an award modeled on the Nobel Prizes — to Karen Uhlenbeck, an emeritus professor at the University of Texas at Austin.
If you’ve ever put off an important task to, say, alphabetize your spice drawer, you know it wouldn’t be fair to describe yourself as lazy.
After all, alphabetizing requires focus and effort — and hey, maybe you even went the extra mile to wipe down each bottle before putting it back. And it’s not like you’re hanging out with friends or watching Netflix. You’re cleaning — something your parents would be proud of! This isn’t laziness or bad time management. This is procrastination.
If procrastination isn’t about laziness, then what is it about?
Etymologically, “procrastination” is derived from the Latin verb procrastinare — to put off until tomorrow. But it’s more than just voluntarily delaying. Procrastination is also derived from the ancient Greek word akrasia — doing something against our better judgment.
“It’s self-harm,” said Dr. Piers Steel, a professor of motivational psychology at the University of Calgary and the author of “The Procrastination Equation: How to Stop Putting Things Off and Start Getting Stuff Done.”
That self-awareness is a key part of why procrastinating makes us feel so rotten. When we procrastinate, we’re not only aware that we’re avoiding the task in question, but also that doing so is probably a bad idea. And yet, we do it anyway.
“This is why we say that procrastination is essentially irrational,” said Dr. Fuschia Sirois, professor of psychology at the University of Sheffield. “It doesn’t make sense to do something you know is going to have negative consequences.”
She added: “People engage in this irrational cycle of chronic procrastination because of an inability to manage negative moods around a task.”
Wait. We procrastinate because of bad moods?
In short: yes.
Procrastination isn’t a unique character flaw or a mysterious curse on your ability to manage time, but a way of coping with challenging emotions and negative moods induced by certain tasks — boredom, anxiety, insecurity, frustration, resentment, self-doubt and beyond.
“Procrastination is an emotion regulation problem, not a time management problem,” said Dr. Tim Pychyl, professor of psychology and member of the Procrastination Research Group at Carleton University in Ottawa.
In a 2013 study, Dr. Pychyl and Dr. Sirois found that procrastination can be understood as “the primacy of short-term mood repair … over the longer-term pursuit of intended actions.” Put simply, procrastination is about being more focused on “the immediate urgency of managing negative moods” than getting on with the task, Dr. Sirois said.
The particular nature of our aversion depends on the given task or situation. It may be due to something inherently unpleasant about the task itself — having to clean a dirty bathroom or organizing a long, boring spreadsheet for your boss. But it might also result from deeper feelings related to the task, such as self-doubt, low self-esteem, anxiety or insecurity. Staring at a blank document, you might be thinking, I’m not smart enough to write this. Even if I am, what will people think of it? Writing is so hard. What if I do a bad job?
All of this can lead us to think that putting the document aside and cleaning that spice drawer instead is a pretty good idea.
Ms. Lieberman tells us there are no easy solutions like downloading a scheduling app because overcoming chronic procrastination is about managing our emotions in a new way, not better time management or self-control.
She goes on to provide several techniques such as self-forgiveness, self-compassion and making your temptations more inconvenient.
Students, read the entire article, then tell us:
— Are you a procrastinator? If yes, how does procrastination affect your life and performance in school? Tell us about a specific time you kept putting off a task and what you think was the source of your procrastination?
— How big a problem is procrastination in your life? What have you tried to do to overcome this problem? If you are not a procrastinator, tell us what strategies you use to get things done on time.
— What do you think of Ms. Lieberman’s argument that procrastination is not about being lazy or bad time management but about managing our emotions? Can you give any examples that support or contradict this viewpoint? What do you think are the root causes of your own procrastination?
— What do you think about the author’s advice? Which of the techniques that she recommends are you most likely to try and why? Are there any other strategies you have used in your life that have been successful?
— Do you think procrastinating can ever be productive? Why or why not? Can you give an example of a time when putting off a task or project until the last minute made your work better?
How many minds it changes is another matter. Opinions have hardened over time, with many Americans already convinced they knew the answers before Mr. Mueller submitted his conclusions. Some believe that the special counsel’s previous indictments, twinned with voluminous news reporting, have already shown a conspiracy between the Trump campaign and the Kremlin. Some believe that the investigation is, as Mr. Trump has long described it, a “witch hunt.”
What impact do you think the report will have on the country? Do you think it will change minds?
Finally, tell us more about what you think:
— What is your reaction to the Mueller report and Mr. Barr’s summary? Do they change your view of Mr. Trump and his presidency? Do you find Mr. Barr’s interpretation of the Mueller report to be satisfying?
— Has the investigation restored your confidence in the American system of law or in Mr. Trump and his presidency? Or has deepened your distrust?
— The investigation took over 22 months and cost over $25 million. Was it worth it? Why or why not? What, if anything, would you change about the proceedings? Do you agree that the full Mueller report should be made public?
— Should the nation now move on, or should Democrats continue their investigations into the issue of collusion?
— President Trump claims the Mueller report is a “complete and total exoneration.” Do you agree? Is he correct to have called the investigation a “witch hunt”?
… while Mr. Mueller may not have found sufficient evidence of a criminal conspiracy, let’s not lose sight of what we already know, both from his investigation and from news reports over the past two years.
We know that the Russian government interfered repeatedly in the 2016 presidential election, by hacking into computer servers of the Democratic National Committee and the Clinton campaign. We know that it did this with the goals of dividing Americans and helping Donald Trump win the presidency. We know that when top members of the Trump campaign learned about this interference, they didn’t just fail to report it to the F.B.I. They welcomed it. They encouraged it. They made jokes about it. On the same day that Mr. Trump publicly urged the Russians to hack into Hillary Clinton’s emails, they began to do just that. And we know that when questioned by federal authorities, many of Mr. Trump’s top associates lied, sometimes repeatedly, about their communications with Russians. None of this is in dispute.
That Mr. Mueller couldn’t find sufficient evidence that Mr. Trump or anyone involved in his campaign had coordinated directly with the Russians may be explained by the fact that they didn’t need to. They were already getting that help.
We also know that what began as a counterintelligence investigation quickly turned into a criminal investigation, in large part because Mr. Trump surrounded himself with criminals. To date, his campaign chairman, Paul Manafort; his deputy campaign chairman, Rick Gates; his national security adviser, Michael Flynn; his campaign foreign policy adviser, George Papadopoulos; and his personal lawyer and fixer, Michael Cohen, have all pleaded guilty or been convicted of federal crimes. In January, Mr. Mueller charged Roger Stone, Mr. Trump’s longtime aide, with multiple counts of witness tampering, obstructing justice, and making false statements.
Imagine if we’d learned all of this just Sunday, in one fell swoop, rather than in a trickle of indictments and prosecutions over the last 18 months.
— Do you agree with the editorial board’s interpretation of the report? Which points do you find to be most persuasive? Which the least?
— In “Right and Left React to the Mueller Report,” Sarah Mervosh surveys the reaction to the report from across the political spectrum. Read through the short quotations provided and choose one article to read in its entirety. What different facts and perspective did this additional article provide?
— What do you think about the media coverage of the investigation? Do you think it was fair? If you feel it was not, in what ways do you think the media coverage was flawed? Why?
Today, Imperial College London announced it will be launching an online master’s degree in machine learning. The program will be offered on Coursera and will welcome its first learners cohort in fall 2020.
Imperial is ranked among the world’s top ten universities by QS and Times Higher Education. The degree will be the second offered by Imperial on Coursera: a year ago, they announced an online master’s degree in public health that is set to start in fall 2019.
The program will be the first MOOC-based degree fully dedicated to machine learning. Its closest competitor is Georgia Tech’s OMSCS, which leads to a degree in computer science but allows learners to specialize in machine learning.
The goal of Imperial’s degree will be to train engineers and researchers to meet the growing demand for machine learning specialists in fields ranging from analytics to bioinformatics.
The curriculum will cover fundamentals, such as computing and math; machine learning concepts, such as deep learning and statistical modeling; and tools, such as PySpark and PyTorch. The program will also delve into the ethics of machine learning applications.
Specific courses haven’t been revealed yet. But Imperial offers three Specializations on Coursera. Two cover public health and are part of their upcoming public health degree. And the third covers machine learning. So it’ll likely be part of their machine learning degree.
To be admitted, applicants will need a bachelor’s degree in computer science, math, physics, or economics. They’ll also likely have to submit academic transcripts, recommendations, and English proficiency scores.
Imperial will allow learners to complete relevant MOOCs on Coursera then transfer them for credit if they later join the program. This will likely apply to their existing Specialization, but it may also extend to other, yet-to-be-launched online courses or microcredentials.
Applications start dates haven’t been announced yet. But Imperial’s machine learning degree seems to follow the same schedule as their public health degree, just one year later:
Public Health MS
Machine Learning MS
Announcement
March 2018
March 2019
Applications Open
January 2019
TBA
Launch
September 2019
September 2020
So assuming this one-year lag is maintained, Imperial should start accepting applications for its machine learning degree around January 2020.
Fees haven’t been disclosed either. But, again, assuming Imperial prices its second degree like the first, it should cost around $15K in the UK and EU, and $25K elsewhere.
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:
For years, insomniacs have been lulled to sleep by the dulcet voice of Bob Ross, the bushy-haired painter whose PBS show, “The Joy of Painting,” rose to popularity in the 1990s and has lately enjoyed a second life on YouTube. Now, the maker of a popular meditation app hopes Mr. Ross will put everyone else to sleep, too.
Calm.com, which produces meditation products, is recasting classic episodes of “The Joy of Painting” into “Sleep Stories,” an audio series designed for restless adults to ease the burden of slumber. It is the first time the company that manages Mr. Ross’s estate has agreed to license audio of the show that turned Mr. Ross into a celebrity and, after his death in 1995, a pop culture favorite.
