These accounts are chock-full of interesting tidbits that make them ideal for playing with mixed media, color, texture and layering in a collage. A high school student, Annabeth Tao, experimented with these concepts to incorporate details like “Italian singer,” “jazz and pop,” “exciting performance,” “fashion dressing style,” and “aerobics promoter” from Lara Saint Paul’s obituary into her piece:
I bought a lot of luxurious materials like golden wires, fabric, beans and glitter in the secondhand store to make Lara Saint Paul look as fabulous as she was. I accidentally saw two large records lying on the table in the secondhand store, and successfully got the idea to use them as my collage base.
I learned to connect the key words and materials that I can use for the collage and freely explored the way to present the obituary in a very interesting way so that the audience could notice her characteristics at the first glance.
With thousands of obituaries to choose from and the freedom to use any materials they want, these mixed media collages are a great way to spark student creativity and ingenuity.
Supplies:
Students will need to refer to the Obituaries section to find their subjects. For the collage, they can experiment with a vast range of materials — magazines, text, fabric, found objects, stamps, photographs, wire, beans, glitter, newspaper, original drawings — anything goes! You can find more ideas for collage supplies here.
2. Read it and, as you read, brainstorm images that are associated with the person in the obituary by creating a mind map. Keep in mind that the images do not have to be of the person.
3. Find and experiment with a vast range of materials to create the collage. Magazines, text, fabric, found objects, stamps, photographs, wire, beans, glitter, newspaper, original drawings — anything goes!
4. Assemble your final collage. Layer the materials to make a cohesive composition.
According to the Coursera Global Skills Index, technology, business, and data skills are in high demand across every industry. To expand access to career-relevant learning, Coursera is offering a limited number of scholarships for tech, data, and business courses to learners in India and Mexico. These scholarships will allow learners to take a course aligned with lagging or emerging skills in India and Mexico and earn a Course Certificate.
Who is eligible for a scholarship? Learners in India and Mexico who can’t afford to pay for a Course Certificate can apply for a scholarship.
Which courses are eligible for a scholarship? As part of the Global Skills Index we’ve identified specific courses that align with lagging and emerging skills in India and Mexico. You can find the eligible courses in India hereand Mexico here.
What do I receive with a scholarship? Once approved for a scholarship, you’ll be able to view all course content for one course and complete all assignments required to earn a Course Certificate. The scholarship is valid for 180 days after your application is approved.
What does the application involve? You’ll need to include information about your educational background, career goals, and financial circumstances. You’ll also need to commit to abiding by our Honor Code and our Code of Conduct. Make sure your application is over 150 words. Applications below this minimum will not be accepted. Please note that you can only apply for a scholarship via our website on a computer. You cannot apply using our mobile app.
When will my application be reviewed? The review process may take up to 15 days. While you wait for your application to be reviewed, you can begin the course immediately in audit mode or free mode. When your application is reviewed, you’ll get an email letting you know whether it’s been approved or denied. Unfortunately, Coursera is unable to provide exceptions or rush individual applications.
Can I transfer my scholarship to a different course? Unfortunately, Coursera cannot transfer payments or scholarships across different courses.
Can I start a free trial while I have a scholarship? Your scholarship application will be cancelled if you start a free trial while it is being reviewed. If you’ve already started a free trial and still want your scholarship application to be reviewed for approval, you should cancel your subscription and re-apply.
By Emily Glassberg Sands, Head of Data Science, Coursera
Today marks the release of the inaugural Coursera Global Skills Index (GSI), an in-depth look at skill trends and performance around the world, made possible by the millions of learners who come to Coursera to learn and grow.
Skills are the foundation of human productivity and economic success. Yet the skills landscape as we know it is under transition. Technology is advancing faster than humans, and the skills that once fueled our economy will no longer suffice. Companies, countries, and individuals are grappling with a serious skills shortage, in addition to uncertainty on which skills they need to succeed in the changing economy.
Urgency around upskilling is a global one, but there is no one-size-fits-all solution. Each country has a unique set of circumstances – economic, political, social – that shape their skills landscape today. Industries, too, face challenges unique to their verticals that require tailored talent strategies. This first edition of the Coursera Global Skills Index looks closely at these trends, benchmarking 60 countries and 10 industries across Business, Technology, and Data Science skills. The findings draw from an innovative data methodology that uses machine learning to map skills to the content that teaches them and then robustly measure skill proficiencies based on the assessment performance of the millions of learners on Coursera.
We are excited to reveal these rich skills insights, but this is just the beginning. Looking ahead, we will continue to partner with governments, businesses, and individuals around the world to put this data into action, and turn the shifting tides of the skills landscape into an opportunity to train, innovate, and compete.
Here are some of the key findings:
Two-thirds of the world’s population is falling behind in critical skills, including 90% of developing economies. Countries that rank in the lagging or emerging categories (the bottom two quartiles) in at least one domain make up 66% of the world’s population, indicating a critical need to upskill the global workforce. Many countries with developing economies — and with less to invest in education — see larger skill deficiencies, with 90% ranking in the lagging or emerging categories.
Europe is the global skills leader. European countries make up over 80% of the cutting-edge category (top quartile globally) across Business, Technology, and Data Science. Finland, Switzerland, Austria, Sweden, Germany, Belgium, Norway, and the Netherlands are consistently cutting-edge in all three domains. This advanced skill level is likely a result of Europe’s heavy institutional investment in education via workforce development and public education initiatives.
Asia Pacific, the Middle East and Africa, and Latin America have high skill inequality. Consistent with the vast economic and cultural diversity that characterizes each region, Asia Pacific, Middle East and Africa, and Latin America have the greatest within-region skill variance. Asia Pacific is at the extremes of the global Business rankings with New Zealand (#6) and Australia (#9) approaching the very top, while Pakistan (#57) and Bangladesh (#59) land near the bottom. In the Middle East and Africa, Israel is a leader in each of the three domains and #1 in Data Science, while Nigeria lags near the bottom of the rankings across domains, and is last in Data Science. In Latin America, Argentina’s #1 ranking in Technology is in stark contrast to Mexico’s (#43) and Colombia’s (#49) lower proficiencies in the field.
The United States must upskill while minding regional differences. Although known as a business leader for innovation, the U.S. hovers around the middle of the global rankings and is not cutting-edge in any of the three domains. Within the U.S., skill proficiency is distributed non-uniformly while the West ranks ahead of other regions in Technology and Data Science, the Midwest shines in Business.
In addition to benchmarking countries, we also evaluated trending skills globally and skill proficiencies across 10 major industry verticals:
Demand for Technology and Data Science skills is growing, while demand Business skills is shrinking. Across the board, enrollment numbers highlight fast-growing demand for Technology and Data Science skills from individuals and companies alike. While Technology enrollments increased by 13%, for example, Business enrollments decreased by 11%.
Technology industry professionals lack strong business skills. Technology ranks 5th in Business out of the ten industries in our analysis.
Manufacturing shows skills resilience in the digital era. Manufacturing ranks #1 in both Business and Technology and demonstrates an aptitude for tackling change successfully.
Telecommunications consistently ranks near the top. Telecommunications is the only industry to rank consistently in the top three across Business, Technology, and Data Science (#3 in each).
Finance surprises with below-average skills performance. Despite its pursuit of digital transformation, Finance ranks second to last in Business (#9) and Data Science (#9), and hovers near the middle in Technology (#5).
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:
Have you ever altered or changed your name for any reason? Has anyone else? For instance, maybe you go by a nickname more often than the name on your birth certificate. Which of the names you are called do you like best? Why?
If you are an immigrant, is your name one that is easily understood, pronounced and spelled in the place you live now? If not, what has that been like for you?
Tell us in the comments, then read a related essay from the Opinion section to learn more.
Find many more ways to use our Picture Prompt feature in this lesson plan.
Have you ever seen an online video of grapes exploding in a microwave oven? In these popular videos, people microwave two grape halves connected by a bridge of skin to see the explosive results.
Watch the first 20 seconds in the above ScienceTake video, “Why Microwaved Grapes Explode.”
• What did you notice when the grapes were microwaved? Describe what you observed in as much detail as you can.
• What’s your explanation of what happened?
Spend several minutes developing your hypothesis — feel free to pause or replay the video as needed.
Now watch the rest of the video and answer the following questions:
• What is the common amateur explanation for these explosions?
• What did scientists discover is the real explanation?
Please, do not try this experiment at home! (Or school either.)
1. The article begins: “If you haven’t seen what happens to grapes in a microwave oven, you haven’t spent enough time in the richly nerdy corner of the internet that specializes in strange, everyday phenomena with fascinating, plausible scientific explanations.” How would you describe the writer’s tone in this opening sentence? Why does James Gorman, the author, use the words “richly” and “nerdy” together? And why does he say “plausible” scientific explanations? What do you think is Mr. Gorman’s view on the veracity of the scientific explanations one can find on YouTube videos?
2. What is plasma? How is plasma relevant to the grape experiment?
3. Why did Aaron D. Slepkov, a physicist, decide to study the phenomenon of exploding grapes?
4. In your own words, explain why grapes explode. Be sure to include the role of electrons, radiation wavelengths and resonance.
5. What makes grapes a particularly good subject for the microwave experiment? Although Dr. Slepkov discovered that grapes do not need to be cut in half, why do they need to be touching each other for the experiment to work?
6. What is the most exciting finding for Dr. Slepkov? What are the implications of this discovery for future scientific research?
Finally, tell us more about what you think:
— Return to your hypothesis from above. How did it compare to the scientific explanation discovered by Dr. Slepkov?
— What was the most surprising or intriguing thing you learned from the article? What questions about the nature of scientific inquiry does the article raise for you?
— The article concludes: “For party goers, the message is clear: Never underestimate a parlor trick, particularly if a microwave is involved.” What do you think is the message of this research? How useful or important is the research on exploding grapes?
— Have you watched other homemade science experiments online? Which is your favorite and why? Are there any videos you would you like to see explained by scientists?
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.
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.
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.
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 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.
