From March 5 to March 28, as it typically does three times a year, Mercury, the closest planet to the sun, appears to move backward across the sky — from east to west rather than from west to east.
Have you seen any negative effects in your life? Have you been more cautious about your actions? Or do you think fear or caution, based on astrological events, is nonsense?
1. What do astrologists warn us about while Mercury is in retrograde?
2. While Mercury may appear to be moving backward, what, scientifically, is taking place between Mercury and Earth’s orbit?
3. What evidence does the author present of astrology’s growing presence online? Which online communities represent the most significant growth?
4. Why are millennials increasingly drawn to online astrology?
5. How do some practitioners of astrology respond to skeptics and critics who call it “pseudoscience”? What do astrology defenders believe to be the positive benefits?
6. Chani Nicholas, an astrologer, says:
My main concern is that everyone has access to the therapies and practices that are healing to them. And if astrology is not that for you, then great, move on.
What do you think of her statement? Should people who don’t believe in astrology just move on? Do you think there is any value in pushing back against astrology? Do you think there is anything harmful about astrology?
Finally, tell us more about what you think:
— Do you believe in astrology? Or do you think it is harmless fiction? Or alternatively, do you think it’s actually harmful in any way — a dangerous pseudoscience or a hoax?
— Do you think the alignment of planets or stars has any influence on human life? Does the article make you more or less interested in what Amanda Hess calls the “mystical internet?” Will you change your behavior while Mercury is in retrograde?
— Do you regularly read your horoscope or discuss your astrological sign? If so, why? If not, what do you think of those who do? Have you ever visited an astrology or so-called mystical internet site? If yes, why? Does it provide you with a sense of spirituality? Community? Self-understanding?
— Why do you think astrology is on the rise? Is it a good thing? What evidence or personal experience do you have to support your opinion?
Blockchain is poised to reshape nearly every industry as companies work to increase security and anonymity, drive efficiencies, lower costs, and more. In fact, Gartner forecasts the market opportunity for blockchain to exceed $3.1 trillion between now and 2030, creating considerable demand for blockchain talent. But we don’t just need technical developers who can help build this technology — it’s also crucial that business professionals understand the fundamentals of blockchain and its applications. This applies to executive levels as well as operational roles within an organization.
Today, we’re excited to partner with INSEAD, one of the world’s top business schools to launch ‘Blockchain Revolution for the Enterprise,’ a new Specialization taught by Don Tapscott, adjunct professor at INSEAD and co-founder of the Blockchain Research Institute (BRI). Tapscott is one of the world’s leading authorities on blockchain and the role technology plays in business and society. With content derived from the BRI’s multi-million dollar research program, the four-course Specialization is designed to introduce business professionals to the world of blockchain and teach them how to strategically leverage the technology to create scalable business opportunities.
“Educating leaders is one of the keys to accelerating the widespread adoption of blockchain,” said Tapscott. “Working with INSEAD and Coursera, we’ve been able to develop the world’s definitive online course for blockchain in business. This will be invaluable to learners around the world.”
With no programming background necessary, Blockchain Revolution for the Enterprise enables mid-career and senior business professionals to:
Understand a high-level view of blockchain and how they can position themselves within this ecosystem
Critically evaluate various claims, applications, use-cases, and implementation challenges of blockchain technology using seven design principles
Produce a Blockchain Opportunity Analysis to assess the feasibility of a blockchain application within their own industry or organization
“Blockchain, a technology predicted to disrupt many industries, is being viewed both as a threat and an opportunity,” said Peter Zemsky, Deputy Dean and Dean of Innovation at INSEAD. “It is important for INSEAD to lead the way to offer a universally accessible learning program to help demystify this technology and to prepare future leaders for this transformation.”
The first course in the Blockchain Revolution for the Enterprise Specialization is available today, with additional courses rolling out through next week. Coursera and INSEAD will also be launching a number of vertical-focused Specializations that adapt this content for specific application in industries like healthcare, finance, and supply chain.
As blockchain presents another wave of technological disruption, we believe learners need flexible, affordable ways to keep up. We are humbled to collaborate with industry leaders like Don Tapscott and INSEAD to provide an accessible pathway that empowers learners with skills that can transform their careers and their businesses.
In hopes of pursuing a career in Public Policy, I’ve spent the past year reorienting my programming towards Data Science. Naturally, doing so required significant practice finding, reading, visualizing and analyzing data. This experience has impressed upon me the importance of having a framework in mind when tackling a big data project.
To demonstrate this, I will use my most recent data science project, which you can read more about here. Hopefully, the steps suggested below aide you in creating a framework for your own projects.
Step One: Familiarize yourself with your area of interest.
Context is key when analyzing any dataset, and unfortunately, many otherwise impressive works have been ruined by the author’s lack of familiarity with the subject matter. This doesn’t mean that you’ll have to know every surrounding detail. Working with large datasets would be impossible if that was the case. However, if you’re going to write about wage growth, you’ll need to know the difference between real and nominal wages. If you want to analyze migration patterns to the United States, you should probably read about the 1965 Immigration Act.
A key part of my piece was learning about the history of American colleges—where they initially formed, the circumstances that caused college enrollment to accelerate in the 20th century, and how students with degrees compare to students without degrees. Equally important was familiarizing myself with current research regarding intergenerational mobility. Without the background knowledge I gained from doing this, I would have struggled mightily in finding a dataset suited to answering my question.
You will inevitably make mistakes, and that’s why peer review has its own step. But some background reading will save you a good bit of time and prevent a truly erroneous error from getting through. If you’re lucky, step two will be completed in the midst of step 1.
Step Two: Determine your question
What do you want to know?
Unfortunately, this step doesn’t end with simple curiosity. When deciding what your question should be, you need to consider a number of complementary ones.
How likely is it that the information you are looking for exists?
Has this question been answered before? If so, what is distinct about your approach?
Are you comfortable enough with the math and/or logic necessary to answer this question?
Is this question interesting enough to you to sustain your frustration when you inevitably encounter difficulty?
My research went a long way in explaining the difference in collegiate and non collegiate incomes, while also exploring the divergent prospects between high education countries and low education counties. What was missing was the difference in outcomes between different colleges. This inspired me to explore mobility from the standpoint of the colleges themselves, and that led me to discovering Raj Chetty’s “Mobility Report Cards”.
The length of this step can vary drastically. Sometimes, what you need can be found on the Census or Bureau of Labor Statistics. These are accurate, trustworthy databases that, barring a few edge cases, will give you a fairly comprehensive picture of what you’re trying to discover. Needless to say, it’s not always that easy. It can take hours, if not days, to find the dataset you need.
As mentioned earlier, my background research allowed me to quickly identify the right dataset for analyzing college mobility rates. Chetty’s “Mobility Report Cards” had substantial information concerning student school selectivity and income for multiple decades. The data they used was provided by the Department of Education and the Internal Revenue Service, and it was clearly documented and laid out.
This documentation made the whole process exponentially easier. I couldn’t have asked for a better dataset. Unfortunately, expecting this for every project is unrealistic, and likely to severely hamper your motivation. It can take a long time to find the right datasets, and sometimes you’re only able to find data that is auxiliary to your main question. In these situations, my advice would be twofold:
Reach out to others and see if someone more familiar with the subject knows where to locate the correct data.
Consider modifying your question according to what you have and repeating step two.
This can be a long, painful process. While plowing through the mud is oftentimes necessary, don’t ever be afraid to adjust your parameters based on what you have.
Step 4: Familiarize yourself with the dataset
You will likely be creating several visualizations from this data, and cross checking it throughout the project. Before you start, take an hour or so to explore it. Create simple graphs, find the maxima/minima, visualize the distribution. Most importantly, see if you can isolate trends within your data.
This step, along with step 1, is a great way of preemptively identifying trends in your dataset. The graph above is a scatter plot of college tiers vs the percentage of parents at the top of the national income quartile. Drawing the regression line showed me what to expect when plotting the differences in top quartile representation by college tier. The same was also true for median income.
This proved immensely valuable, as knowing the trend in the data allowed me to quickly identify when I had made a mistake in creating the graphs I ended up using for the final piece. Simple plotting can be an invaluable tool when preparing yourself for a more thorough analysis.
Conclusion
Learn the subject, find your question, choose your dataset, and familiarize with it. Hopefully, these steps aide you in creating your own work. Analyzing and visualizing data is frustrating, often monotonous work, especially when you’re still learning the ropes as I am. However, the power that comes from being able to make unique observations and contribute to the historical conversation is well worth the effort.
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:
From Sydney to Seoul and Cape Town to New York, children skipped school en masse on March 15 to demand action on climate change.
What issues in your community, your country or the world do you think deserve more attention? What is one that you would take a stand on, like the students pictured above took a stand on climate change? Why does it matter to you?
Tell us in the comments, then read the related article to see more photos from the student protests around the world.
Find many more ways to use our Picture Prompt feature in this lesson plan.
The seven stories presented here reveal Parker’s manifold strengths. We see her as a chronicler of contemporary New York, in both its manic, flapper highs and its bread-line lows. Her playwright’s ear for the nuances of dialogue is discernible in stories like “Diary of a New York Lady.” Whether set in Downtown speakeasies, tawdry hotel rooms or Fifth Avenue ballrooms, all have heart — at times almost too much. Even at its most amusing (and it can, of course, be riotously, incisively, funny), Parker’s universe is fundamentally cruel, and stories like “The Garter” and “Mr. Durant” look on compassionately as characters are rocked by a hard-boiled world …
4. Why were officers able to arrive on the scenes of the attacks more quickly on that particular day? The article goes on to say, “Still, it was not fast enough.” What details develop this point?
5. Who is the suspect? What appears to be his motive? How long did it take police officers to apprehend him?
Finally, tell us more about what you think:
The related Opinion essay “The Roots of the Christchurch Massacre” argues that “All those who have helped to spread the worldwide myth that Muslims are a threat have blood on their hands.” Wajahat Ali writes:
Upon learning about the massacre in Christchurch, a Muslim friend messaged me, “How will we keep our kids safe?”
I didn’t have a good answer. But I know the threat we’re facing isn’t just individual terrorists. It’s the global ideology of white nationalism and white supremacy. We have to take it seriously, and call out politicians, academics and media personalities who give it a platform under the guise of exploring both sides, fostering debate or avoiding political correctness.
The cost is too great. Just look to Christchurch and the 49 worshipers who should have returned home to their families and community instead of attending their last prayer.
Do you share Mr. Ali’s stance that attacks like those that took place at two mosques in Christchurch are not caused just by individual terrorists carrying out the attacks but also the “global ideology of white nationalism and white supremacy” to which they subscribe?
Elsewhere in his essay, Mr. Ali incorporates in his argument President Trump’s rhetoric about immigration in the United States and in Europe:
If the idea that Muslims are a threat sounds familiar, it’s in part because it was used by President Trump to argue for a wall to protect America from a “caravan” of Central American migrants seeking asylum. He asserted that “Middle Easterners” were in the caravan, a claim he admitted he could not back up. During a summer trip to England, Mr. Trump warned that Britain was losing its “culture” and that immigration had “changed the fabric of Europe — and unless you act very quickly, it’s never going to be what it was.”
Arguing for his travel ban aimed at mostly Muslim countries, Mr. Trump said, “I think Islam hates us,” lied about seeing Muslims celebrate the Sept. 11 attacks, and retweeted a fringe anti-Muslim group’s fake videos of Muslim refugees committing violence. No wonder the Christchurch manifesto praised Mr. Trump “as a symbol of renewed white identity and common purpose.”
It’s clear that the dangers of white nationalism aren’t limited to the United States. This attack is a reminder that this dangerous ideology also threatens immigrant communities worldwide, and that it’s fueled by leaders around the world.
What is your reaction to these ideas? Do you think world leaders like Mr. Trump can or should be held responsible for the spread of anti-immigrant ideology? Why or why not? How has New Zealand’s prime minister, Jacinda Ardern, responded to the attacks? Do you think she can use her position and words to help heal her nation?
Finally, what role does social media play in fomenting hatred of certain groups? It is known that the shooter in Christchurch streamed part of the attacks on Facebook. Do social media companies have a responsibility to block extremists from using their platforms to spread hate and inspire violence? Explain your stance.
1. After looking closely at the image above (or at the full-size image), think about these three questions:
• What is going on in this picture?
• What do you see that makes you say that?
• What more can you find?
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.)
3. After you have posted, try reading back to see what others have said, then respond to someone else by posting another comment. Use the “Reply” button or the @ symbol to address that student directly.
Each Monday, our collaborator, Visual Thinking Strategies, will facilitate a discussion from 9 a.m. to 2 p.m. Eastern Time by paraphrasing comments and linking to responses to help students’ understanding go deeper. You might use their responses as models for your own.
At Coursera, we’re always working on making your learning experience more rewarding than ever. Our latest updates are designed to provide you with tools that help you make progress on-the-go and stay on track with your courses.
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We hope that you enjoy these updates to your learning experience on Coursera!
