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‘Freedom From Want’

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‘Freedom From Want’

The image above, created by artists Hank Willis Thomas and Emily Shur, is a reimagining of Norman Rockwell’s 1943 painting, “Freedom From Want.”

Comparing this image to Rockwell’s original piece, what message do you think the artists are trying to convey? How does the photo relate to or comment on the societies of 1943 and today? What is your opinion of its message?

Tell us in the comments, then read the related article to find out what the artists want to say.

Find many more ways to use our Picture Prompt feature in this lesson plan.

Word + Quiz: portend

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Word + Quiz: portend

: indicate by signs

_________

The word portend has appeared in 99 articles on NYTimes.com in the past year, including on Jan. 5 in “Apple’s Biggest Problem? My Mom” by Kevin Roose:

When Apple lost more than $75 billion in market value this past week after a surprise announcement that it is expecting lower iPhone sales than originally projected, the company put most of the blame for its troubles on China, where a slowing economy and the trade war with the United States have hurt sales.

… Most of the journalists who write about tech for a living (including me) are early adopters — power users who like having the latest gadgets, and who are willing to fork over money for a slightly better experience. For some of these people, Apple’s announcement has come as a shock that portends potential disaster for the company.

_________

Weekly News Quiz for Students: Plane Crash, Border Crisis, Medical Breakthrough

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Weekly News Quiz for Students: Plane Crash, Border Crisis, Medical Breakthrough

A resolution condemning , which stemmed from an uproar that erupted after Representative Ilhan Omar of Minnesota, a freshman Democrat, insinuated that backers of Israel exhibit dual loyalty, passed the House by an overwhelming 407-to-23 vote on March 7.

How Praveen became a Data Scientist by using Coursera

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How Praveen became a Data Scientist by using Coursera

2013 B.C. (Before Coursera)

When I graduated from college, I joined iNautix in September 2013. During my time there, I trained on mainframes. I’m passionate about programming and loved working on hands-on projects, but I was not completely satisfied in my role.

I kept reading about data science — one thing led to another. I found my new love: machine learning. I decided to enroll in the Machine Learning Specialization from the University of Washington and shortly thereafter, I added the Deep Learning Specialization from deeplearning.ai.

Machine Learning: What’s That?

I really didn’t have a clue about machine learning before I started these two Specializations. They helped me understand core machine learning concepts, and now I’m able to pick up new algorithms easily and apply them to the real-world problems. Completing both Specializations helped me stand out from the crowd of eager new data scientists like myself.

Here’s my course list:

A Door Opens

After a year and a half of learning on Coursera, I felt confident enough in my abilities to start applying for jobs. I reached out to many senior members of HSBC, mentioning that I’m an aspiring data scientist that wants to practice machine learning. I also highlighted all the certifications and courses that I completed. A few of them replied back quickly, and others connected me with more relevant contacts within the company. I never thought I’d get that kind of response from so many high-level people at a company like HSBC. Eventually, a promising door opened for me: Virendrasinh Gohil, Head of Big Data in the India office and leader of India’s Innovation Lab, asked me to meet him the very next day after I contacted him. This represented a turning point for my career.

Fortunately, a data science role opened up in December 2016, and Virendrasinh recommended me. After I interviewed, I was put on the project. I’ve been a data scientist working in Global Markets at HSBC ever since. I’m pretty sure that without Coursera, I wouldn’t be where I am today.

Interested in becoming a data scientist, or learning more about what it takes to succeed in the field? Find your path with the Coursera Data Science Academy.

FutureLearn’s Unlimited: One Price for the Entire Catalog

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FutureLearn’s Unlimited: One Price for the Entire Catalog

FutureLearn, the UK-based MOOC provider, just launched a new plan that lets learners pay a single price to access most of FutureLearn’s catalog. Unlimited costs $269 and gives users access to the catalog for a single year and does not automatically roll over. For now Unlimited is available for a discounted rate of $199 (until May 11, 2019).

The only other MOOC provider that offers a single subscription price for its entire catalog is Kadenze, and its price is $20/month. Coursera also tested a single subscription price of $49/month for its entire catalog at the end of 2017, but unfortunately decided against it after the test. Now Coursera learners need to sign up for a subscription per Specialization, which can cost anywhere between $39–79.

A drawback of the subscription model can often be that once you stop paying, you lose access to the course materials. But to FutureLearn’s credit with their new model, if you finish a course then you retain access to the course materials as long as the course stills exist on the platform.

Most of FutureLearn’s catalog is still free to access, but it’s not as free as it was before. Back in April 2017,  FutureLearn introduced a new pricing model that restricted access to the free content. Prior to this change, FutureLearn used to charge only for certificates (digital and print). The course content and the exams were completely free.

Under the new pricing model, the exams are behind a paywall (similar to Coursera). The course content is still free to access, but it’s only available for the duration of the course and two weeks after it ends. For continual access to the course (caveat: as long as it exists on FutureLearn), learners need to Upgrade.

The single course Upgrade option still exists for learners who want access to only one course. Unlimited is an additional tier on top of this.

Unlimited also frees FutureLearn learners from worrying about course start and end dates, because the entire catalog is available to them as long their subscription is active.

Effectively speaking, FutureLearn is drastically slashing their prices with this newest model. For the same price of maybe 3–4 course upgrades, you can get access to FutureLearn’s catalog of around 1,000 courses.

You can learn more about Unlimited here.

Climbing the Ladder: Investigating the “American Dream” with Python

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Climbing the Ladder: Investigating the “American Dream” with Python

College was everything to me.

Until the day I got my first acceptance letter, every grade, project and passion was pursued with college in mind. I knew since I was 5 that I needed a scholarship—that my future rested on money that I didn’t have.

My parents, teachers and counselors worked hard to make it happen, and due in no small part to the combination of their efforts and sheer good luck, I was able to graduate from an amazing school. But I was lucky. I have known many people throughout my life who shared my drive but not my fortune.

As a lifelong observer, explorer, and beneficiary of the American higher education system, I want to understand it. I want to know its history, its benefits, and the role it plays in intergenerational mobility. I want to know whether there is more than just luck that separates attainment and success from rejection or failure. This article represents my best attempt to do so.

A brief history of American Education

American schools, as it turns out, have always been very different.

In the late 18th century, American colonies varied drastically in their education systems. Geography, immigration, religious sectarianism and the rise of enlightenment thinking combined to create a patchwork of different educational philosophies. The privatized, economically stratified elementary schools in the South formed a different education ecosystem to the governmental and religious institutions in the North.

Prior to 1747, Harvard, Yale and William and Mary were the only colleges within the United States. But the religious fervor inspired by the Great Awakening caused various Christian sects to open schools, tripling the number of colonial colleges, and setting the groundwork for higher education in the country.

Harvard_Old_College

The original Harvard College building (1638–1670)

Americans became far more educated throughout the 19th century, as the embrace of public funding and a decentralized education system saw the United States become one of the best-educated countries in the world. American colleges, initially considered inferior to their European counterparts, would benefit massively from public commitment and investment.

The 20th century greatly accelerated the transition to higher education, particularly so after World War II. The GI Bill covered college tuition and expenses for World War II and Korean veterans, encouraging many to pursue a degree after returning to the states. The SAT attempted to democratize the admissions process. Low-cost community colleges and two-year schools began popping up throughout the country. Most importantly, the percentage of high school graduates went from 25% in 1940 to nearly 80% by 2000.

Likewise, college graduation rates have exploded, with the U.S. Census Bureau’s data showing that the percentage of college graduates in the general population has doubled in the past 40 years:

1

Via U.S Census Bureau.

However, this growth has not been even. The geographic, fiscal, and, above all, racial composition of different localities has led to a wide range of different outcomes for different people.

The combination of exploding demand for higher education and variable local factors has resulted in wide diversity among colleges and their student bodies. These differences are key to understanding the role higher education plays in intergenerational mobility. By analyzing how wealth, location and collegiate selectivity affect student outcomes, we can better understand the role higher education plays in achieving the “American Dream.”

College and outcome

As the American workforce has adapted to an increasingly automated world, demand for a college degree has drastically risen. Per the Georgetown Public Policy Institute, we know that degree holders have seen the highest job growth for the past 20 years:

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Via Georgetown Public Policy Institute.

We also know that degree holders have higher wages:

3

Via Bureau of Labor Statistics.

Lower rates of unemployment:

4

Via Statista.

And have benefitted massively from increased educational requirements over the past 45 years:

5

Via Georgetown Public Policy Institute.

While the U.S. population has made huge strides in college participation, the distribution of these gains has been uneven at the local level. One example of this is the disconnect between increasing high school and college graduation rates.

The Hamilton Project, an economic research group within the Brookings Institution, found that counties with the lowest percentage of high school graduates in 1980 had the highest gains in high school graduation rates by 2016:

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Via The Hamilton Project.

The opposite is true for college graduates. Counties with lower percentages of college graduates in 1980 saw the smallest increases, while counties with higher percentages of college graduates in 1980 saw the largest gains:

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Via The Hamilton Project.

To understand the ramifications of this trend, we can use the Hamilton Project’s Vitality Index. This index assigns each county in the United States a composite score according to factors like household income, poverty rate, life expectancy and other measurements of the county’s health.

Comparing county vitality rate, we can see that this lack of collegiate participation has had dire consequences for many counties. Counties with low college attainment had significantly worse vitality scores in 1980 and 2016.

8

Via The Hamilton Project.

So, what does this tell us?

  • An increasing number of jobs require a college education
  • College graduates tend to be significantly better off than non-graduates
  • An increasing percentage of the population are getting a college education
  • Collegiate educational gains have been smallest in counties with fewer graduates
  • The presence of college graduates has significant ramifications for community well-being.

Altogether, this gives us a detailed picture of outcomesw, but economic mobility is a different beast. To understand how colleges impact mobility, we need to understand the opportunities colleges provide for those on the bottom end of the national income distribution. We can’t just know that colleges are associated with better circumstances—we need to know how they affect those who come from worse circumstances.

Determining Mobility

So which colleges provide such opportunities for low-income students? Where are these colleges located?

To answer this question, we’ll need some data. Thankfully, a group of researchers at Opportunity Insights have done the legwork for us. Using IRS tax records and Department of Education data, this team has released various data sets concerning student outcomes for children who were born between 1980 and 1982. With Pandas, we can visualize this data, and in the process paint a clearer picture of economic mobility in the United States.

Let’s start with a head count.

Attendance

image--3-

There are two main takeaways from this data:

  • Most children attended some type of college, but a sizable minority (averaging a bit under 1.4 million per year) didn’t.
  • College graduation is primarily driven by Selective and Two-year colleges

Remember this when considering the data below. While a non-trivial number of students attend Elite, Highly Selective, and Non-Selective colleges, the schools most representative of the student experience are Selective and Two-year institutions. Other schools types, while enrolling millions of students, represent only a fraction of national student body.

Median Parental Household Income

As mobility describes upward economic movement, we need to know these students’ “starting position”. To do this, we can compare the range of parental income by college type:

10

Unsurprisingly, median parental household income moves upward by tier. Not a single Elite college had a median parental income below a hundred thousand, while the opposite was true for for-profit colleges. The income gap between college tiers increases the more rightward you go in the graph, ranging from median incomes between 20 and 30k to median incomes above 220k.

Individual Income

The pattern observed with parental income persists with student income:

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Again, we see a clear rightward shift in the income distribution by college tier. This tells us that:

  • Individual and parental income generally increases according to college tier.
  • There is a wide gap between the incomes for the lowest tier (under two years/for profits) and the highest tier(most selective schools)

Bottom to Top

We can see that there is a significant wealthy disparity according to college type. But how does the distribution of wealth look if we focus on lower income students? Are the colleges with the wealthiest students on average seeing the greatest gains for students from poor families?

To determine this, we can use what I will henceforth call “Bottom to top mobility,” the percentage of students from families in the bottom 20% of the national income distribution who rise to the top quintile of the individual national income distribution.

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The answer is a resounding yes. Like the income distributions, we see a clear upward trajectory by college tier. Bottom quintile students show more upward progression at higher tier colleges, with a majority at most elite schools reaching the top individual quintile. In general, poorer students seem to do considerably better after graduating from top schools.

Mobility Rates

So if more selective schools lead to higher income for lower income students, and they admit fewer low income students, how can we assess these schools’ overall impact on mobility? We can take those “mobile students”, the students from low income families who made it into the top quintile, and divide the number of them at a given school by the total number of students at that school. Doing so gives us these colleges’ “Mobility Rates”:

(Note: The following graph is “zoomed in” for clarity. 7 colleges with Mobility Rates above 8.5 are not shown.)

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Mobility Rates don’t diverge noticeably by selectivity. While there is variation in the distributions, there isn’t a clear “upward path” between college tier and Mobility Rates. Selective schools do better for poor students, yet this seems to be “balanced out” by more selective colleges having fewer poor students in the first place.

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So while poorer students tend to do better at more selective schools, the lower percentage of poor students at these schools means that these institutions have a smaller footprint on economic mobility.

We can graph this footprint geographically. If we multiply the mobility rate by the number of students, then group these students together by their colleges counties, we can see the locations in the United States that play the biggest role in driving economic mobility.

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County Count

Naturally, our largest urban centers had the most representation. Los Angeles; San Diego; Miami-Dade; Cook County, Illinois; Harris County, Texas; Prince George’s County, Maryland; and the New York City boroughs were responsible for a substantial portion of mobile students. This is in line with general trends in college enrollment:

“Across the nation, college graduates are overwhelmingly concentrated in urban areas. Almost 90 percent of college grads live in urban counties, with more than 60 percent of them in large metros with over one million people. Just a bit more than one in ten college graduates reside in rural communities.”
–Richard Florida, Citylab

Finally, we can split these mobile students by college type.

image--4-

The majority of “mobile” students come from selective and two year colleges. While more selective schools might be more conducive to economic mobility, they represent a smaller fraction of overall college attendance, and percentage-wise they admit far fewer lower income students.

Looking Ahead

James Tuslow Adams defined the American Dream as:

“…(not) a dream of motor cars and high wages merely, but a dream of social order in which each man and each woman shall be able to attain to the fullest stature of which they are innately capable, and be recognized by others for what they are, regardless of the fortuitous circumstances of birth or position.”

Economic mobility lies at the heart of the American Dream, and education has long served as it’s ladder. College, once available to only a select few, has become an essential component of the American workforce and economy.

These longstanding trends in mobility won’t reverse themselves, and a college degree will doubtlessly become even more essential to employment prospects in the future. However, I hope recognizing the colleges that drive mobility can help us determine the avenues by which we can increase the availability of opportunity and elevate those for whom the opportunities are slipping further from reach.

Time’s arrow always marches forward, but it is worth considering how we might aid those left in its trail.

Ask a Data Engineer: Warby Parker Edition 👓

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Ask a Data Engineer: Warby Parker Edition 👓

wp-header

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:

  • Lon Binder, Chief Technology Officer, Warby Parker
  • Maddie Tierney, Executive Assistant, Warby Parker
  • Kayla Robbins, Executive Assistant, Warby Parker
  • Kaki Read, Senior Communications Manager, Warby Parker
  • Isabel Seely, Senior Brand Manager, Warby Parker

It’s been an absolute pleasure. And of course, the fam at Codecademy. You know who you are. Couldn’t do it without you.

Learning With: ‘U.S. Women’s Soccer Team Sues U.S. Soccer for Gender Discrimination’

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Learning With: ‘U.S. Women’s Soccer Team Sues U.S. Soccer for Gender Discrimination’

4. According to the article, the team has been “a leading force in women’s sports for more than a generation.” What evidence does the author give to support this claim? How have the team’s activism and outspokenness inspired other women’s teams around the world?

5. How does the pay between players on the men’s and women’s national soccer teams differ? Why are direct comparisons in compensation complicated?

6. The article concludes with a quotation by Neena Chaudhry, the general counsel of the National Women’s Law Center in Washington:

These are the same kinds of arguments and claims that we still see at every level of education for women and girls, from K through 12 to college. It’s unfortunately a sad continuation of the way that women and girls in sports are treated in the U.S.

Do you agree? Do you think the United States soccer team’s case is part of a larger system of inequity?

Finally, tell us more about what you think:

— What is your reaction to the lawsuit? Do you think the team will win its case and why?

— Why does this lawsuit matter? Is this case just about soccer or is it bigger? Why should nonsports fans care? How will it make a difference to you?

— Should professional female athletes be paid the same as men? Does it matter if the ticket sales or television ratings in a given sport are higher for men than for women?

— Do people have a responsibility to watch and attend more women’s sporting events if they want women’s athletics to thrive? What suggestions do you have to make sure that female athletes get the respect they deserve in our society? Will you watch the United States women’s soccer team compete this summer in the World Cup?

— In a related article, “8 Times Women in Sports Fought for Equality,” Sarah Mervosh and Christina Caron profile other examples of women fighting for equality in sports. Look through the article and select one historical example you find most interesting or significant. Compare this example to the current efforts by the United States women’s soccer team. How are they similar and different? What lessons can we learn from these female athletes’ struggles?

— What are girls’ and boys’ athletics like at your school? Do female athletes receive equal treatment? What recommendations do you have to improve female athletic programs at your school?

Related Resources:

In Fight for Equality, U.S. Women’s Soccer Team Leads the Way

Pay Disparity in U.S. Soccer? It’s Complicated

Women’s History Month

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Women’s History Month

March is Women’s History Month and March 8 was International Women’s Day. What do these celebrations mean to you? How do you think we should observe them?

Do you think it’s meaningful to recognize and honor women with a special history month? Why or why not?

Tell us in the comments, then read the related satirical essay from Opinion to see what one writer has to say.

Or, find more ideas in our collection on Teaching and Learning About Women’s History With The New York Times

Word + Quiz: calumny

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Word + Quiz: calumny

1. a false accusation of an offense or a malicious misrepresentation of someone’s words or actions

2. an abusive attack on a person’s character or reputation

_________

The word calumny has appeared in 16 articles on NYTimes.com in the past year, including on Nov. 12 in the Sports of the Times column “Does Calgary Really Want the Winter Olympics? Does Anyone?” by Michael Powell:

I attended the 2016 Summer Games in Rio de Janeiro and wrote of the corruption and violence that had fallen on the poorest in the beautiful city, and the schools and hospitals starved for funds as the government spent grandly on its Olympic Taj Mahal.

Rio’s mayor at the time, Eduardo Paes, wrote a letter complaining of my calumnies and took offense at talk of corruption. Another year passed, and Brazilian prosecutors found Olympic corruption everywhere. An executive from a big construction firm testified that he had given $5 million in bribes to Paes. The former mayor denies those charges.

_________