How to Build the Perfect Data Science Team

Although the fields of statistics, data analysis, and computer programming have been around for decades, the use of the term “data science” to describe the intersection of these disciplines has only become popular within the last few years.

The rise of this new specialty — which the Data Science Association defines as “the scientific study of the creation, validation and transformation of data to create meaning” — has been accompanied by a number of heated debates, including discussions about its role in business, the validity of specific tools and techniques, and whether or not it should even be considered a science. For those convinced of its significance, however, the most important deliberations revolve around finding people with the right skills to do the job.

On one side of this debate there are those purists who insist that data scientists are nothing more than statisticians with fancy new job titles. These folks are concerned that people without proper statistics training are trying to horn in on a rather lucrative gig without getting the necessary training. Their solution is to simply ignore the data science buzzword and hire a proper statistician.

At the other end of the spectrum are people who are convinced that making sense out of large data sets requires more than just number-crunching skills, it also requires the ability to manipulate the data and communicate insights to others. This view is perhaps best represented by Drew Conway’s data science venn diagram and Mike Driscoll’s blog post on the three “sexy skills” of the data scientist. In Conway’s case, the components are computer programming (hacking), math and statistics, and specific domain expertise. With Driscoll, the key areas are statistics, data transformation — what he calls “data munging” — and data visualization.

The main problem with this multi-pronged approach is that finding a single individual with all of the right skills is nearly impossible. One solution to this dilemma is to create teams of two or three people that can collectively cover all of the necessary areas of expertise. However, this only leads to the next question, which is: What roles provide the best coverage?

In order to address this question, I decided to start with a more detailed definition of the process of finding meaning in data. In his PhD dissertation and later publication, Visualizing Data, Ben Fry broke down the process of understanding data into seven basic steps:

  1. Acquire – Find or obtain the data.
  2. Parse – Provide some structure or meaning to the data (e.g. ordering it into categories).
  3. Filter – Remove extraneous data and focus on key data elements.
  4. Mine – Use statistical methods or data mining techniques to find patterns or place the data in a mathematical context.
  5. Represent – Decide how to display the data effectively.
  6. Refine – Make the basic data representations clearer and more visually engaging.
  7. Interact – Add methods for manipulating the data so users can explore the results.

These steps can be roughly grouped into four broad areas: computer science (acquire and parse data); mathematics, statistics, and data mining (filter and mine); graphic design (represent and refine); and information visualization and human-computer interaction (interaction).

In order to translate these skills into jobs, I started by selecting a set of occupations from the Occupational Information Network (O*NET) that I thought were strong in at least one or two of the areas in Ben Fry’s outline. I then evaluated a subset of skills and abilities for each of these occupations using the O*NET Content Model, which allows you to compare different jobs based on their key attributes and characteristics. I mapped several O*NET skills to each of Fry’s seven steps (details below).

ONET Skills, Knowledge, and Abilities Associated with Ben Fry’s 7 Areas of Focus

Acquire (Computer Science)

  • Learning Strategies – Selecting and using training/instructional methods and procedures appropriate for the situation when learning or teaching new things.
  • Active Listening – Giving full attention to what other people are saying, taking time to understand the points being made, asking questions as appropriate, and not interrupting at inappropriate times.
  • Written Comprehension – The ability to read and understand information and ideas presented in writing.
  • Systems Evaluation – Identifying measures or indicators of system performance and the actions needed to improve or correct performance, relative to the goals of the system.
  • Selective Attention – The ability to concentrate on a task over a period of time without being distracted.
  • Memorization – The ability to remember information such as words, numbers, pictures, and procedures.
  • Oral Comprehension – The ability to listen to and understand information and ideas presented through spoken words and sentences.
  • Technology Design – Generating or adapting equipment and technology to serve user needs.

Parse (Computer Science)

  • Reading Comprehension – Understanding written sentences and paragraphs in work related documents.
  • Category Flexibility – The ability to generate or use different sets of rules for combining or grouping things in different ways.
  • Troubleshooting – Determining causes of operating errors and deciding what to do about it.
  • English Language – Knowledge of the structure and content of the English language including the meaning and spelling of words, rules of composition, and grammar.
  • Programming – Writing computer programs for various purposes.

Filter (Mathematics, Statistics, and Data Mining)

  • Flexibility of Closure – The ability to identify or detect a known pattern (a figure, object, word, or sound) that is hidden in other distracting material.
  • Judgment and Decision Making – Considering the relative costs and benefits of potential actions to choose the most appropriate one.
  • Critical Thinking – Using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems.
  • Active Learning – Understanding the implications of new information for both current and future problem-solving and decision-making.
  • Problem Sensitivity – The ability to tell when something is wrong or is likely to go wrong. It does not involve solving the problem, only recognizing there is a problem.
  • Deductive Reasoning – The ability to apply general rules to specific problems to produce answers that make sense.
  • Perceptual Speed – The ability to quickly and accurately compare similarities and differences among sets of letters, numbers, objects, pictures, or patterns. The things to be compared may be presented at the same time or one after the other. This ability also includes comparing a presented object with a remembered object.

Mine (Mathematics, Statistics, and Data Mining)

  • Mathematical Reasoning – The ability to choose the right mathematical methods or formulas to solve a problem.
  • Complex Problem Solving – Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.
  • Mathematics – Using mathematics to solve problems.
  • Inductive Reasoning – The ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events).
  • Science – Using scientific rules and methods to solve problems.
  • Mathematics – Knowledge of arithmetic, algebra, geometry, calculus, statistics, and their applications.

Represent (Graphic Design)

  • Design – Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models.
  • Visualization – The ability to imagine how something will look after it is moved around or when its parts are moved or rearranged.
  • Visual Color Discrimination – The ability to match or detect differences between colors, including shades of color and brightness.
  • Speed of Closure – The ability to quickly make sense of, combine, and organize information into meaningful patterns.

Refine (Graphic Design)

  • Fluency of Ideas – The ability to come up with a number of ideas about a topic (the number of ideas is important, not their quality, correctness, or creativity).
  • Information Ordering – The ability to arrange things or actions in a certain order or pattern according to a specific rule or set of rules (e.g., patterns of numbers, letters, words, pictures, mathematical operations).
  • Communications and Media – Knowledge of media production, communication, and dissemination techniques and methods. This includes alternative ways to inform and entertain via written, oral, and visual media.
  • Originality – The ability to come up with unusual or clever ideas about a given topic or situation, or to develop creative ways to solve a problem.

Interact (Information Visualization and Human-Computer Interaction)

  • Engineering and Technology – Knowledge of the practical application of engineering science and technology. This includes applying principles, techniques, procedures, and equipment to the design and production of various goods and services.
  • Education and Training – Knowledge of principles and methods for curriculum and training design, teaching and instruction for individuals and groups, and the measurement of training effects.
  • Operations Analysis – Analyzing needs and product requirements to create a design.
  • Psychology – Knowledge of human behavior and performance; individual differences in ability, personality, and interests; learning and motivation; psychological research methods; and the assessment and treatment of behavioral and affective disorders.

Using occupational scores for these individual ONET skills and abilities, I was able to assign a weighted value to each of Ben Fry’s categories for several sample occupations. Visualizing these skills in a radar graph shows how different jobs (identified using standard SOC or ONET codes) place different emphasis on the various skills. The three jobs below have strengths that could be cultivated and combined to meet the needs of a data science team.

Another example includes occupations that fall outside of the usual sources of data science talent. You can see how — taken together — these non-traditional jobs can combine to address each of Fry’s steps.

According to a recent study by McKinsey, the U.S. “faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions” based on data. Instead of fighting over these scarce resources, companies would do well to think outside of the box and build their data science teams from unique individuals in other fields. While such teams may require additional training, they bring a set of skills to the table that can boost creativity and spark innovative thinking — just the sort of edge companies need when trying to pull meaning from their data.

Updates:

May 2, 2014 – The folks over at DarkHorse Analytics put together a list of the “five faces” of analytics. Great article.

  1. Data Steward – Manages the data and uses tools like SQL Server, MySQL, Oracle, and maybe some more rarified tools.
  2. Analytic Explorer – Explores the data using math, statistics, and modeling.
  3. Information Artist – Organizes and presents data in order to sell the results of data exploration to decision-makers.
  4. Automator – Puts the work of the Explorer and Visualizer into production.
  5. The Champion – Helps put all of the pieces in place to support an analytics environment.

D3 Notes:

Spelling and National Security

A former co-worker of mine always used to joke about our company’s customer database by posing the deceptively simple question: “How many ways can you spell ‘IBM’?” In fact, the number of unique entries for that particular client was in the dozens. Here is a sample of possible iterations, with abbreviations alone counting for several of them:

  • IBM
  • I B M
  • I.B.M.
  • I. B. M.
  • IBM CORP
  • IBM CORPORATION
  • INTL BUS MACHINES
  • INTERNATION BUSINESS MACHINES
  • INTERNATIONAL BUSINESS MACHINES
  • INTERNATIONAL BUSINESS MA

I thought of this anecdote recently while I was reading an article about the government’s Terrorist Identities Datamart Environment list (TIDE), an attempt to consolidate the terrorist watch lists of various intelligence organizations (CIA, FBI, NSA, etc.) into a single, centralized database. TIDE was coming under scrutiny because it had failed to flag Tamerlan Tsarnaev (the elder suspect in the Boston Marathon bombings) as a threat when he re-entered the country in July 2012 after a six-month trip to Russia. It turns out that Tsarnaev’s TIDE entry didn’t register with U.S. customs officials because his name was misspelled and his date of birth was incorrect.

These types of data entry errors are incredibly common. I keep a running list of direct marketer’s misspellings of my own last name and it currently stands at 22 variations. In the data world, these variation can be described by their “edit distance” or Levenshtein distance — the number of single character changes, deletions, or insertions required to correct the entry.

Actual Name Phonetic Misspellings Dropped Letters Inserted Letters Converted Letters
Kinde Kindy Kine Kiinde Kinoe
Kindee Inde Kinder Kimbe
Kindle Kimde
Kindde Isinde
Kindke Pindy
Kindl
Kinds
Kinge
Kinele
Winde
Kinae
Kincius
Jindy

Many of these typographical mistakes are the result of my own poor handwriting, which I admit can be difficult to transcribe. However, if marketers have this much trouble with a basic, five-letter last name, you can imagine the problems the feds might have with a longer foreign name with extra vowels, umlauts, accents, and other flourishes thrown in for good measure. Add in a first name and a middle initial and the list of possible permutations grows quite large … and this doesn’t even begin to address the issue of people with the same or similar names. (My own sister gets pulled out of airport security lines on a regular basis because her name doppelgänger has caught the attention of the feds.)

The standard solutions for these types of problems typically involve techniques like fuzzy matching algorithms and other programmatic methods for eliminating duplicates and automatically merging associated records. The problem with this approach is that it either ignores or downplays the human element in developing and maintaining such databases.

My personal experience suggests that most people view data and databases as an advanced technological domain that is the exclusive purview of programmers, developers, and other IT professionals. In reality, the “high tech” aspect of data is limited to its storage and manipulation. The actual content of databases — the data itself — is most decidedly low tech … words and numbers. By focusing popular attention almost exclusively on the machinery and software involved in data processing, we miss the points in the data life-cycle where most errors start to creep in: the people who enter information and the people who interpret it.

I once worked at a company where we introduced a crude quality check to a manual double-entry process. If two pieces of information didn’t match, the program paused to let the person correct their mistake. The data entry folk were incensed! The automatic checks were bogging down the process and hurting their productivity. Never mind that the quality of the data had improved … what really mattered was speed!

On the other hand, I’ve also seen situations where perfectly capable people had difficulty pulling basic trends from their Business Intelligence (BI) software. The reporting deployments were so intimidating that people would often end up moving their data over to a copy of Microsoft Excel so they could work with a more familiar tool.

In both cases, the problem wasn’t the technology per se, but the way in which humans interacted with the technology. People make mistakes and take shortcuts … it is a natural part of our creativity and self-expression. We’re just not cut out to follow the exacting standards of some of these computerized environments.

In the case of databases like TIDE, as long as the focus remains on finding technical solutions to data problems, we miss out on what I think is the real opportunity — human solutions that focus on usability, making intuitive connections, and the ease of interpretation.

Update:

  • July 7, 2013 – In a similar database failure, Interpol refused to issue a worldwide “Red Notice” for Edward Snowden recently because the U.S. paperwork didn’t include his passport number and listed his middle name incorrectly.
  • January 2, 2014 – For a great article on fuzzy matching, check out the following: http://marketing.profisee.com/acton/attachment/2329/f-015e/1/-/-/-/-/file.pdf.

2012: The Year of the Amateur

One of the topics that seemed to keep cropping up in the news this year was the growing power of the amateur in public life. This trend is not necessarily new but it has been gaining momentum as modern technologies make it easier for the average person to create things (i.e. books, music, videos or physical products) and deliver them to a wider audience. Combine this with an anemic economic recovery and you have the perfect environment for people striking out on their own.

American history is full of passionate amateurs who ignored societal rules or overcame an entrenched bureaucracy to introduce new and exciting ideas to our culture. We admire the business entrepreneur, the garage band, and the inventor working out of his basement. They are some of our most cherished icons and they speak to our desire to make it big on our own terms. This attitude finds its purest expression in the Do-It-Yourself (DIY) ethic, which encourages individuals to bypass specialists altogether and seek out knowledge and expertise on their own.

There are some problems with this relentless individualism, however. Taken to the extreme this skeptical attitude toward the professional “elite” can lead to the distrust — and perhaps even disdain — of true experts. People now diagnose their own medical conditions, create their own legal documents, homeschool their own children, and regularly deny the validity of scientifically accepted facts. In an article which discusses recent changes in the distribution of information, Larry Sanger talks about how the aggregation of public opinion on the Internet (what he calls the “politics of knowledge”) has eroded our very understanding and respect for reliable information:

“With the rejection of professionalism has come a widespread rejection of expertise — of the proper role in society of people who make it their life’s work to know stuff.”

Everybody’s an expert now, in the sense that we can all do our own research online and come to our own conclusions about any topic under the sun. It’s the perfect democratization of knowledge … except most of us aren’t really experts in the traditional sense. Experts typically possess a very deep understanding of a subject and are aware of its subtleties and nuances. The average person may only scratch the surface of a topic and can miss important details because they literally don’t know what they don’t know. Nobody’s seriously going to call in an amateur cardiac surgeon if they’ve got a heart problem, so why is it so easy to dismiss the work of professionals in other fields?

Before I’m accused of being elitist, let’s lay down a framework for discussing the differences between amateurs, experts, and professionals. In an article published by Wharton, Kendall Whitehouse draws the distinction between “knowledgeable enthusiasts” (amateurs) and professionals based on the editorial process (this is in a journalistic context):

“Carefully checked sources and consistent editorial guidelines are key differences between most professional and amateur content … The latter brings quickness and a personal viewpoint and the former provides analysis and consistent quality.”

While I certainly agree that results are important, there are plenty of situations where amateurs deliver results that are as good as those of professionals. In fact, the DIY community frequently uses the term amateur expert and notes that the word “amateur” stands in contrast to the commercial motivation (i.e. financial reward) of the professional, not their level of skill. Following this reasoning, a professional is not necessarily an expert, they are simply someone who happens to get paid for what they do. An amateur can still be an expert based on their skills and abilities, they just don’t get paid.

If the amateur/professional word pairing makes sense, we still need an antonym of “expert” to refer to deficiencies in skills. In this case, I would suggest the term “novice,” which is defined as someone who has very little training or experience. Essentially this means that a thorough discussion of experts and amateurs needs to account for both a financial dimension (amateur vs. professional) and a skill or experience dimension (novice vs. expert). I’ve created a quick quad chart to visualize these relationships:

If we return to our previous discussion, we can now see that the rejection of expertise does not necessarily represent support for the plucky amateur, it represents a shift toward glorification of the naive. Sure, there are times when novices can bring a fresh perspective to established practices (punk rockers and other creative outsiders come to mind). But in 2012, the growing regularity of this superficial approach led to a few very interesting — and very public — failures.

The first example is the unauthorized attempt by an elderly parishioner to restore a painting in a Spanish church over the summer. The tragi-comic results of Cecilia Gimenez’s fresco fiasco were all over the news in August and it was pretty clear to everyone that her work was a massive failure. Using our new definition, she is clearly a novice (unskilled) amateur (unpaid).

Ms. Gimenez later complained that, with all the attention that her botched restoration of Ecce Homo had gotten, she should have received some compensation for her work. This would have made her a quasi-professional, I guess, but I don’t suppose there are a lot of museums out there who’d be willing to hand over their cultural treasures to her care.

(To create your own Ecce Homo restoration, check out this site.)

The second example was the National Football League’s use of replacement referees during the early part of the 2012 season. With the regular officials locked out due to contracts negotiations, NFL management brought in referees from semi-professional football leagues, lower college divisions and even high schools in hopes that nobody would notice the difference. They noticed.

Throughout the preseason, a series of bad penalties, missed calls, and even blown coin tosses made it clear that the new guys were not ready for prime time. As the regular season progressed and the mistakes accumulated, demands for the return of the regular refs grew louder. Finally, two days after the outcome of a game between the Green Bay Packers and the Seattle Seahawks was decided by a controversial call, an agreement between the NFL and NFL Referees Association was reached. (Photo below from the Washington Post.)

NFL management clearly misjudged the level of skill needed officiate a pro football game and how quickly the replacement refs would be exposed for what they were: novice professionals. This isn’t to say that some of these guys couldn’t have developed into perfectly good officials over time. But such a high-profile occupation doesn’t really lend itself to on-the-job training.

Not all skilled workers are lucky enough to have their expertise hit the bottom line so obviously. Writing in an article about the NFL lockout, Paul Weber noted that”

“Attitudes about expertise can … make it a risky hand to play in a negotiation, depending on who’s on the other side of the table. The idea that no one is irreplaceable and there’s always a guy next in line willing to do the job run deep in America. Professing expertise can also bring on suspicions of elitism and scratch an itch to knock someone down a peg.”

This inclination can be seen clearly in my third example of the year, which involves several high-profile political pundits who insisted that Mitt Romney would win the 2012 Presidential election. When statistician Nate Silver of the New York Times began predicting an Obama victory back in June, many conservative commentators questioned both his methodology and his masculinity (offending comments have since been removed).

Despite Silver’s clear statements regarding the laws of probability, conservatives just could not get past the fact that most of their favored polls (University of Colorado, Rasmussen) showed a neck-and-neck race. In the end, the elections validated the statistical approach that Silver used and forced many people to rethink their reliance on ‘unskewed’ polls or Karl Rove’s math skills.

Although the animosity toward Silver subsided after the election, I have my doubts that his success will lead to a sudden surge in respect for professional experts. There seems to be a natural tendency in our culture to distrust anyone who stakes a claim to the truth — especially if we don’t like what they’re saying.

The most vociferous of these battles are those fought between journalists and bloggers but there are plenty of other amateur/professional pairings that set off fireworks. In a recent book review on Slate, professional writer Doree Shafrir openly wonders why anyone would be satisfied with being an amateur. To her, the only path to gratification and validation is through professional success:

“The idea of being an office drone by day and by night being, say, an amateur astronomer is completely bizarre to me. Why wouldn’t you just be an astronomer?”

To which a wise reader responds:

“The sad fact is that many of us simply aren’t good enough at what we really love to do it for a living … Or we were good, but unlucky. Or unwilling to sacrifice our families. Or we’re still living down the consequences of a previous failure.”

Amateur interests are a way for someone to gain new skills, test drive a new career, or just participate in a community despite the fact that they aren’t collecting a paycheck. The amateur/professional spectrum doesn’t just exist at the endpoints, it runs the gamut from hobbyists and tinkerers to semi-professionals and professionals. Back in 2004, a report titled The Pro-Am Revolution by Charles Leadbeater (a frequent contributor to TED), suggested that improved tools and new methods of collaboration are helping to create a breed of amateurs that hold themselves to professional standards and can even produce significant discoveries.

In the field of astronomy, these “demi-experts” had an amazing year. Recent developments in computer technology and digital imaging have allowed amateur astronomers to explore regions of the universe never before seen by non-scientists. Plus, the sky is so vast (and observation time so restricted) that serious amateurs can help professional astronomers simply by observing unrecorded (or underrecorded) stellar objects. Significant amateur finds in 2012 included: new comets; new exoplanets; explosions on Jupiter; a planet with four suns; a detailed map of Ganymede; mysterious clouds on Mars; and even previously undiscovered photos from the Hubble telescope.

While these examples make it clear that amateurs can contribute meaningfully to many fields, it is less obvious how society can avoid the pitfalls associated with the well-intended novice. The key, I think, is for everyone — from novice to expert, amateur to professional — to recognize their own limitations. Businesses want expertise but they don’t always want to pay for it. People want to do what they love but they don’t always have the time or skills to make it their career. A novice who tries to recreate the work of an expert will almost certainly fail but an amateur with passion and drive can spur innovations beyond the abilities of entrenched professionals.

These labels are fluid. All experts were once beginners and all professionals were once unpaid. People progress from novice to expert in distinct stages but they can also move from expert to novice if they change careers. In today’s job market, it even seems possible that some of us could apply all of these labels to ourselves at once. To paraphrase author Richard Bach, a professional is simply an amateur who didn’t quit.

Further Reading:

Infographics and Data Visualization (Week 5/6)

I took part in a brief discussion on the student forum after the Week 4 project and it made me realize that I’d been spending so much time trying to create a functional interactive graphic in Tableau that I was missing out on practicing some of the basic techniques of the class. When you combine that with the fact that my favorite attempt was a sketch I laid out in PowerPoint, I decided that I should try to focus on the structure and design of the graphic to see what I could come up with.

The topic I picked was based on some data that I’d pulled back in May/June that I’d never had a chance to use. This data covered all of the various U.S. breweries and the variety of beers they made. I did some additional research to add some information on beer ingredients (especially water, barley and hops) as well as some interesting stats on beer consumption based on a few fun maps done at FloatingSheep.

I spent a good deal of time coming up with the basic grid of the graphic, which ended up having a static left hand column for the introduction to each topic and then an interactive map of the U.S. on the right. The interactive portion consists of tabbed sections that allow you to navigate through several subtopics.

The flow of the of the series starts with an overview of beer production in the U.S., moves to a section on the ingredients of beer, and ends with information on American beer consumption. (I also thought about including some local beer stats for the great State of Wisconsin but that may have to wait.)

Due to time constraints, these mockups contain sample maps from other sources (here. and here):

Infographics and Data Visualization (Week 4)

The assignment for Week 4 is the based on data used in a recent Guardian article on U.S. unemployment. Having used Bureau of Labor Statistics (BLS) data for many years at my previous job, I am far more familiar with this topic than I was with the data we used for last week’s assignment. In fact, I have already written several blog posts dealing with general employment statistics so it will be challenging to come up with something fresh.

The Guardian article includes an interactive map that highlights the lower 48 states (Hawaii and Alaska are off screen) and allows the user to select one of eight different employment metrics. A five-color scale defines the range of each metric while clicking on an individual state brings up a bar chart displaying a few data points and some additional text.

One problem I have with this map is that I think the states are too large to tell a detailed story about how unemployment affects different areas of the country. Maps at the county level (like this one from the BLS or this gorgeous D3 example posted on GitHub) show far more interesting regional employment patterns and help create a more compelling story. (Alberto Cairo talks about the importance of enumeration unit size in this week’s reading assignment.)

Another criticism is that the map only uses a fraction of the employment/unemployment information available from the BLS. This data is relatively easy to download and so there’s no real reason not to include a richer dataset in the graphic. Additional data would allow more detailed monthly trends and more meaningful comparisons to the National rate and/or the rates of other states.

Finally, I think the color scheme used on this map is hard to interpret. The color categories are not easily distinguished from one another and they don’t relate to any natural scale that the user could use to detect patterns. Creating more categories might also help with interpretation of the data.

The range and structure of the data suggests that there is a good story to be found looking at unemployment before, during and after Obama’s first term. There were certainly some unusual statistics associated with the 2007-2009 recession (as defined by the National Bureau of Economic Research).  It was the worst period of economic performance in the U.S. since the Great Depression and the pace of the recovery is one of the slowest on record.

In fact, until President Obama was re-elected a few weeks ago, no sitting president since World War II had been returned to office with an unemployment rate above 7.2%. This metric was such a sacred cow that conservative pundits accused the BLS of bias when data more favorable to the President was released in the run-up to the election. So, how did Obama earn a second term fighting these headwinds?

My first set of charts presents an overview of unemployment in the U.S. over the past twelve years. I wanted to show both the long-term trend in unemployment as well as a side-by-side comparison of the three most recent presidential terms. I’ve included a shaded area for each of the past two recessions on the first chart to show the effect of the two recessions.

The first thing I noticed by looking at these charts is that, over the past twelve years, the U.S. unemployment rate has never been lower than it was during George W. Bush’s first month in office. The rate got pretty close to that mark in the final months of Bush’s second term but it never quite made it. The second thing I noticed was that the drop in unemployment during the months following the Great Recession was slightly faster than it was during the recovery period following the 2001 recession.

My second chart shows the unemployment rate for each state over the course of Obama’s first term. It also includes a ranking of states by total unemployment and colors each chart using the results of the 2012 election.

A Thanksgiving Meal Preparation Timeline

The art of timing the preparation of Thanksgiving dishes takes years of experience and perhaps more than a few hard lessons in the kitchen (ever have anyone de-bone a turkey?). For those less experienced chefs, I’ve always felt that a good inforgraphic might help organize the work so that all the dishes are ready at the proper time.

I didn’t have the time to document my own family’s meal this year but I noticed that L.V. Anderson over at Slate wrote a great piece on her attempt to organize a full dinner. She sums up the issues nicely:

Cooking a Thanksgiving meal is a somewhat masochistic enterprise. It’s rewarding, for sure, and fun if you like cooking. But perfectly coordinating the timing of several dishes—nearly all of which taste best hot, many of which require oven time, and some of which begin deteriorating in quality shortly after you finish cooking them—is, well, impossible.

I’ve taken her instructions and organized them into a timeline with a target mealtime of 3:00 PM. Each box in the chart represents a 15-minute interval and clicking on it describes the task and provides a link to the recipe. Here it is … posted just under the wire:

It still needs some work so I’ll be making a few changes over the weekend. Meanwhile, Happy Thanksgiving!