The Love Lab: Using Data Science to Predict the Likelihood of Divorce

The Love Lab

The Love Lab at the University of Washington Professor founded by Dr. John Gottman uses predictive analytics to assess the likelihood of newlyweds having a stable and happy marriage based on observations of the couple taken during a 15 minute conversation on a persistent marital conflict. Gottman’s team then extract information from each second of the video by evaluating emotions and expressions along with physiological data (e.g. changes in heart rate). Emotions are characterized using the SPAFF (Specific Affect Coding System ) code, developed from Ekman & Freisen’s Facial Affect Coding System,  which applies weighting to positive and negative emotions. The weighting scheme for SPAFF codes is provided below.


Weighting Scheme for the SPAFF Codes (Source)

Researchers compile the second by second SPAFF data into a time series and use complex algorithms to project the likelihood of marital success. Dr. Gottman & his team have found that they can predict a couple’s marriage will last longer than 15 years with 90% accuracy after analyzing only a 15 minute conversation. Even more astounding, Love Lab researchers have found that they can be nearly as accurate with their projections of marital success  after analyzing only 3 minutes of the conversation. To demonstrate the power of the Love Lab’s methodology, videos of 3 minute clips were provided to professional marital counselors (~200) whose projections on whether the couple’s marriage would end in divorce was no better than chance.

Key Lessons from the Love Lab

  1. Positive vs. Negative Emotions: Gottman’s team have identified that for a marriage to survive the ratio of Positive to Negative is around 5 to 1
  2. The Four Horsemen: Dr. Gottman’s research has found that the four key drivers, what he refers to as the Four Horsemen: Defensiveness, Stonewalling, Criticism, and Contempt are major variables in marital demise. Furthermore, of the Four Horsemen Gottman has found the most damaging emotion is contempt which Gottman defines as any statement made from a higher level. Contempt is such a strong emotion, the level of contempt a spouse experiences can even be used to predict the number of colds the individual will experience as the stress impacts their immune system.


  1. Blink: The Power of Thinking without Thinking, Malcolm Gladwell, 2005
  2. The Gottman Institute
  3. Predicting Divorce among Newlyweds from the First 3 Minutes of a Marital Conflict Discussion, Sybil Carrere & John Gottman, 1999

The Cost Effective Approach to obtaining a Six Sigma Black Belt Certification

Six Sigma is a problem solving methodology with deep roots in applying statistical tools and the DMAIC (Define, Measure, Analyze, Improve, Control) approach to reduce operational variance and produce significant financial gains. Six Sigma Black Belts lead large Six Sigma projects which require full-time focus and more in-depth expertise. Adding a Six Sigma Black Belt certification is a great way to increase your salary and add more value to your organization. According to Glassdoor the average base pay for a Six Sigma Black Belt is $96,637 (see graphic below).

Blackbelt Salary

Source: Glassdoor

As there is no formal body for issuing Six Sigma accreditations, professionals may turn to a variety of organizations for certification including many major universities which now offer certification programs. Though other options exist (e.g. independent agencies & consultants) this article will compare the cost of online programs through major universities and the American Society of Quality (ASQ)’s certification program.

Online Programs

Online programs such as Villanova and Arizona State University provide individuals with a classroom experience taught by University faculty. For individuals requiring the structure and discipline of traditional education (i.e. deadlines and structured coursework) online programs may be a great option; however, the added benefits of formalized education come at a price. Arizona State University’s online Six Sigma Black Belt certification comes with a steep $11,010 ($914 per credit hour for 15 credit hours) price tag. As a result, such programs are likely only affordable for individuals working for organizations which offer tuition reimbursement; however, such benefits tend to come with “strings” attached (i.e. multi year commitments).

ASQ Six Sigma Black Belt Certification

The Six Sigma Black Belt certification offered by the ASQ provides individuals capable of studying on their own with a much more cost effective alternative. In order to qualify, applicants must submit an affidavit signed by a project champion attesting to the completion of a Six Sigma project, along with a $538 fee ($388 for ASQ members). Applicants will then need to sit and pass an exam demonstrating their knowledge of Six Sigma principles (Source). Though the ASQ approach requires self discipline (independent studying) it comes with a cost that is an order of magnitude lower than major universities. Furthermore, though individuals are required to submit an affidavit attesting to the completion of a Six Sigma project, many Engineering and Quality professionals with several years of experience have likely led a project that meets with requirements.

Professionals looking to add additional credentials to their resume and increase their value to their organizations by becoming certified Six Sigma Black Belts have a variety of options, including the two choices outlined in this post. Major university offer programs taught by faculty, often online, which provide students with structured coursework and deadlines to aid those lacking the discipline to study on their own for a hefty price. ASQ on the other hand provides professionals with a cost effective way to become Six Sigma certified for those able to study on their own.


MBA vs. Engineering Management degree

Having interviewed a number of young engineers  over the last few years, I find in nearly every interview the discussion of getting an MBA or a Masters of Engineering Management degree becomes a topic of interest. As a result, I’ve elected to share my story and highlight several points related to each degree.

My Story: Why I chose the Masters of Engineering Management

Though I would love to tell you I selected the Masters in Engineering Management approach with the a grand plan in mind; however, the truth is it was a relatively easy way for me to check the “management degree” box.  My thought process was in the event that I wanted to branch out away from engineering (e.g. Project Management, Operations, etc.) the Masters of Engineering would provide me with credibility to get me past the first HR filter.

When I say easy, I was quite lucky in that the graduate school where I obtained my Masters of Mechanical Engineering , also had an Masters of Engineering Management (MEM) program. The MEM program allowed for students to transfer several graduate level courses in other disciplines towards the MEM degree. Additionally, while completing my Mechanical Engineering (ME) degree I had taken several MEM courses as electives. The MEM electives and ability to transfer several 500-level ME provided me with ~40% of the credits I would need to graduate with the degree. In contrast, while talking to a counselor I learned that I would need to take a year of prerequisites in Accounting, Finance, & Economics to be eligible for an MBA program. Thus, I elected to take the path of least resistance.

My MEM degree consisted of courses in Six Sigma, Lean, Statistics, Operations Management, Decision Analysis, and Project Management. Like most graduate programs a capstone project was required. I elected to utilize a project I completed for a previous employer which inevitably became my Black Belt project I submitted for my Six Sigma Black Belt certification.

Masters of Business Administration (MBA)

MBA programs offer a broad range of courses in economics, marketing, finance, accounting and human resources. In most cases the curriculum is geared towards general management; however, specialized programs exist. MBA’s can be viewed as essential for individuals looking to go into investment banking, consulting (think McKinsey), private equity, and hedge funds (Source).

  • Median Salary with 10 years of experience: $98, 626 (Source: Tufts University)
  • GMAT is typically required
  • Available to undergraduates from every discipline
  • Prerequisites in Economics, Accounting, & Finance are generally required

Masters of Engineering Management

Engineering Management programs are tailored to individuals with backgrounds in science & engineering, though such a background may not always be required for the program. Course work typically includes: Project Management, Six Sigma, Operations Management, Finance, etc. If you graduated from an engineering program, the Engineering Management curriculum will likely bridge the gap between engineering school and the essentials needed for corporate success.

  • Median Salary with 10 years of experience: $107, 765 (Source: Tufts University)
  • Typically no requirement to take the GRE or GMAT exams
  • May require science or engineering degree, though that is not always the case

So… Which Degree is the Best?

Both the MBA & MEM degrees have the potential enhance an individual’s career opportunities the selection depends on career aspirations. Individuals interested in executive level management (i.e. the C-Suite) will likely be better served by an MBA from a top level school, whereas individuals interested in product, program, or project management are likely going to find the MEM to be a more valuable experience.


Lessons in Systems Thinking from the Creator of Dilbert

“Losers have goals. Winners have systems.”

-Scott Adams, Creator of Dilbert

Scott Adams, in his book How to Fail at Almost Everything and Still Win Big, compares and contrasts systems thinking with the goal oriented approach to success. Scott credits his systems thinking approach for the success he’s realized today. To compare and contrast systems vs. goals, Adam’s used the following definitions:

System: Something you do on a regular basis that increases your odds of happiness in the long run (Source)

Goal: A specific objective that you either achieve or don’t sometime in the future (Source)

Scott describes goal oriented people as living in a perpetual state of failure. In the event they achieve their goal, after a brief celebration goal oriented people will realize they have lost the thing that gave them purpose. In contrast, systems people are successful every time they execute their system. An example of Scott’s system at work is his blogging efforts which as Scott puts it “the blogging seemed to double my workload while promising a 5% higher income that didn’t make a real difference” (Source). Scott’s blogging provided him the opportunity to hone is writing and experiment with different voices and eventually led to guest articles in the Wall Street Journal and dozens of business opportunities (Source).

Scott shares a more humors example of systems oriented success with Tim Ferriss in a podcast interview. Scott contrasts his approach to seeking love in high school which showering a select co-ed with affection and admiration (goal oriented approach) while Scott’s friend flittered and dated as many co-eds as possible acquiring valuable social skills along the way (systems approach).  Though humors, this example clearly illustrates the point Scott is attempting to make that a system executed regularly will lead to “success” though the end result may be difficult to impossible to visualize initially.

Other examples of Scott’s systems approach to happiness:

Diversification as a tool for Stress Management: Scott applies to the same logic as why one should diversify their portfolio (i.e. not keeping all eggs in one basket). Scott describes Dilbert’s success as a form of diversifying the number of bosses he has (1,000’s of newspapers) preventing any fear of losing his “job”.

Career Advice: Become very good at two or more things and combine those skills to create something that is both rare and valuable. In Scott’s case he combined his artistic ability (above average) with his wit and humor (also above average) to create Dilbert.


The Monte Carlo Method vs. the Normal Distribution: Approximating Uncertainty in the Absence of “Headwinds”

Disclaimer: This post is rather wonkish with statistical and metallurgical discussions.

In a previous post I outlined the use of Bill James (founder of Sabermetrics / Moneyball influencer) similarity index to a metallurgical engineering project (Link). In the example, a statistical model was developed for projecting the strength of a particular alloy with respect to changes in a processing parameter. A Monte Carlo simulation was used to evaluate the distribution in the projected strength as a result of 1,000’s of changes in the model inputs. The result of the simulation gives you the approximate probability of the various outcomes.

The Monte Carlo Method

The Monte Carlo method, as you may have guessed, derives its name from the Monte Carlo casino in Monaco. The approach was invented by a Polish mathematician named Stanislaw Ulam as part of the Manhattan Project (Source). The inspiration for Ulam came from playing solitaire and wondering an easy way to calculate the probability of winning the game, eventually leading him to apply this logic to neutron diffusion (Source). The method involves taking a model and feeding in distributions of the various inputs and recording the outputs over hundreds or thousands of iterations. An example of a distribution generated from a Monte Carlo simulation results for work performed on developing an improved age practice for 7068 aluminum is provided below. The simulation was performed by changes in the time and temperature adjustments to the two step age practice performed following solution heat treatment.


The Monte Carlo method as a tool for generating a distribution of probable outcomes, differs from the classical example taught in Stats 101 courses. Classical empirical modeling is typically introduced using linear regression (think Excel and the linear trend line). Linear regression models are developed such that a line is drawn through the average or expected outcome for an input variable or set of input variables. The residuals or difference between the actual and projected (expected) values at a given point are assumed to be normally distributed and any residual outside of 2 standard deviations from the projected value is considered an “outlier” (Source). This “lazy” approach to modeling the distribution of outcomes can be effective; however, comes with the risk of greatly underestimating the probability of “unlikely” outcomes.

When the Normal Distribution Fails

The defining characteristic of the normal distribution is its central tendency or in layman’s terms, the majority of the data is clustered around the mean. The image below outlines this concept by highlighting the percentage of the data in each area with respect to the number of standard deviations (σ) from the mean (μ). From the image you can see as only 0.1% of the data lies beyond the 3σ point. This feature makes the normal distribution easy to illustrate and drives its use as the basis for tools such as control charts.


Image source

Nassim Nicholas Taleb (NNT), in his book The Black Swan, exhausts the fact that using the Normal (Gaussian) Distribution is dangerous for approximating the likelihood of seemingly low probability outcomes. NNT states that things that are normally distributed face “headwinds” which make probabilities drop faster and faster as you move away from the mean (e.g. height, IQ, etc.). If the “headwinds” are removed the resulting outcomes will become significantly asymmetrical (think 80/20 Pareto principle). NNT illustrates this point by contrasting wealth distribution in Europe and contrasting it with what the distribution would look like if it were normally distributed.

Wealth Distribution in Europe:

  • People with wealth greater than €1 million: 1 in 63
  • Higher than  €2 million: 1 in 125
  • Higher than  €4 million: 1 in 250
  • Higher than  €8 million: 1 in 500
  • Higher than  €16 million: 1 in 1,000
  • Higher than  €32 million: 1 in 2,000
  • Higher than €320 million: 1 in 20,000
  • Higher than €640 million: 1 in 40,000

Normal Wealth Distribution:

  • People with wealth greater than €1 million: 1 in 63
  • Higher than  €2 million: 1 in 127,000
  • Higher than  €4 million: 1 in 886,000,000,000,000,000
  • Higher than  €8 million: 1 in 16,000,000,000,000,000,000,000,000,000,000,000


The above example demonstrates if wealth was  normally distributed, the likelihood of a Bill Gates or Warren Buffett is incomputable and provides a simple lesson in the fragility of the normal distribution when it comes approximating the probability of unlikely outcomes.

Wrapping it Up

“All models are wrong, some models are useful”

-George Box, Industrial Statistician

In process engineering, “head winds” to borrow the term from NNT are made up of the controls imposed on the process inputs. These controls form the basis for the Y = f(x) philosophy touted by Six Sigma books to demonstrate that if the inputs to a process are “in control” the resulting outputs will be as well. The problem with this logic is that it implies that the organization attempting to control the process has identified all the necessary input variables and deployed adequate controls (i.e. “headwinds).

Recently, I fell victim to this oversimplification after resurrecting a model which used the “lazy” approach to modeling uncertainty discussed above and applying it to a process where the “headwinds” (i.e. controls on raw material) had been removed. The result was a drastic underestimation of the probability of an undesirable outcome (production of material outside of the specification limits). Using the “lazy” approach (+/- 3σ) to modeling the probable outcomes the likelihood of nonconformity ended up being an order of magnitude higher than originally projected, DOH!.

Lesson Learned: Avoid the “lazy” approach and embrace the Monte Carlo!



Remembering Hans Rosling, Co-founder of the GapMinder Foundation


I had the privilege to see Dr. Hans Rosling speak at the 2012 ARPA-E Energy Innovation Summit. An event where Elon Musk announced Tesla would be repaying their government loans early and Secretary Steven Chu kicked off his talk with a dirty joke. The punchline, “Yes, but my previous husband was an entrepreneur. Every night he would stand at the end of the bed and tell me how great it was going to be”. With all the great technology being exhibited and guest speakers like Elon Musk, Dr. Chu, T. Boone Pickens, and several congressmen & women, Dr. Rosling blew them all out of the water! Dr. Rosling put an amazing show using his revolutionary data visualization tools as he painted a picture of global health and economics which he used to promote his battle against preconceived notions. Cheers to you Dr. Rosling!

If you enjoyed the video check out Dr. Rosling’s organization or his book  Factfulness.

“I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”

-Hal Varian, Chief Economist at Google

Attributes to Look for When you Must Select an “Expert”

If you enjoyed our last post where we explored the effectiveness of financial titans, an example of how medical professionals can be fallible, and how incentives lead realtors to to work against your best interest (Link), but find yourself still in need an “Expert” seek out these traits adapted from The Signal and the Noise by Nate Silver.


Attributes to Look For:

  • Multidisciplinary: Demonstrates the ability to incorporate ideas from other fields or disciplines
  • Adaptable: Finds new approaches or willing to pursue multiple approaches at the same time
  • Self-Critical: Willingness to take ownership for mistakes or failed predictions
  • Tolerant of Complexity: Understanding that the universe is complex and recognizes that some things are unpredictable (e.g. Black Swans)
  • Cautious: Express projected outcomes in probabilistic terms
  • Empirical:Rely more on observations and data than theory

Attributes to be Cautious Of:

  • Specialized: Career dedicated to working on one or two great problems. Skeptical of the opinion of “outsiders”
  • Stalwart: Singular approach to problem solving. New data is used to refine the original model
  • Stubborn: Blames others or “bad luck” for mistakes and errors
  • Order-seeking: Expects things to abide by relatively simple governing relationships
  • Confident: Speak in terms of certainty
  • Ideological: Expects solutions to be a manifestation of some grander theory


The two lists outlined above signify that the “best” experts will be reluctant to make bold statements regarding  projected outcomes. This reluctance may be perceived as a “weakness”, as it violates laws of persuasion. Always be on your guard for Gurus preaching certainty as they are of course trying to sell you something!

Persuasion Tip #9: Display confidence [either real or faked] to improve your persuasiveness. You have to believe yourself, or at least appear as if you do, in order to get anyone else to believe

Win Bigly by Scott Adams

Forget Gurus! Why you should do your own homework.

“How could I have been so mistaken as to have trusted the experts?”

-John F. Kennedy after Bay of Pigs

Turning to experts for solutions to problems can be appeasing for a number of reasons the most common of which is the comfort in outsourcing the decision making process. In my own experience I’ve found organizations love to delegate decisions, especially technical decisions, to an ordained group of “Experts”. These shaman will fly in on Monday afternoons and confer in conference rooms until Thursday at which point they will collectively provide their OPINION on the matter at hand. This OPINION will taken as fact via a combination of confirmation bias and office politics. Decisions will be made based on the new found “facts” and opportunities will be lost.

This post; however, is not about the use of Guru’s in the corporate world, but how we turn to these anointed professionals  for help making decisions in our everyday life. In this post we will explore the effectiveness of investment professionals, political pundits, doctors and realtors with the goal of providing you with a little extra motivation to do your own research.

Investment Professionals:

Turn on Fox Business, MSNBC, or pop open the Wall Street journal and you can witness the worship of investment “greats” providing you a myriad of explanations of where the market is going or what the hot stock is. Though Wall Street analysts make average salaries measured in the million$, are these analysts anymore effective than dart throwing chimps? The answer is no!

Need further evidence?! A 1995 study of the highest paid Wall Street analysts invited by Barron’s to its annual roundtable to make recommendations revealed investments made based on the projections of these oracles merely matched the average market return (Source: Mlodinow). Furthermore, studies from 1987 and 1997 found that the recommendations from the television show  Wall $treet Week significantly lagged the overall market performance, while a Harvard study of 153 investment newsletters indicated “no evidence of stock pickling ability” (Source: Mlodinow).

You may now be asking yourself how these stock market gurus who apparently have at best the insight of a dart throwing chimp be touted as “beating the market” and be paid outrageous amounts of money? The answer is the illusion of patterns. Humans have evolved to be excellent at pattern recognition, allowing us to perform feats such as circumnavigating the globe with the stars. Excellent abilities to recognize patterns is a double edged sword, as we can also identify patterns where none exist. The example below provides a distribution of the top 300 fund managers and the number of consecutive years they beat the market (S&P 500) and the success of students correctly guessing a series of coin flips. The two distributions are almost identical providing insight that the ability of fund managers to beat the market equivalent to the probability of correctly calling “heads” or “tails”. As the population of coin flipping students or fund managers increases the probability of a small number of “extraordinary performers” increases to the point of certainty.

bell curve

Top 300 Fund Managers vs. 300 Coin Flipping Students (Source: Mlodinow)

Political Pundits:

After recognizing the failure of political scientists to accurately predict the fall of the Soviet Union, Phil Tetlock (then a professor at UC Berkeley) undertook a 15 year study to evaluate  the accuracy of political predictions. Tetlock’s findings were published in his book, Expert Political Judgement.  Tetlock’s findings concluded that experts were barely better than random chance (think dart throwing chimps) at predicting events. In fact, events Tetlock’s “experts” predicted had a zero percent chance of occurring, actually occurred about 15% of the time, while absolute certain events (per the “experts”) did not occur about 25% of the time.  Tetlock has noted that the more interviews an expert participated in the worse his or her prediction accuracy (Source: N. Silver)!

In his own review of political pundits, Nate Silver evaluated predictions made by panelist on the television program The McLaughlin Group. Silver analyzed almost 1000 predictions and found that the pundits were about as accurate as a coin flip. Furthermore, Silver noted that “experts” on The McLaughlin Group predictions were influenced by their political affiliations (Source: N. Silver).

Persuasion Tip #1: When you identify as part of a group, your opinions tend to be biased toward the group consensus.

Win Bigly by Scott Adams

Medical Doctors:

As it may be easy to “write off” investment analysts and political pundits as modern day snake oil salesmen, lets take a brief look at Medical Doctors. Studies have shown that radiologists fail to identify lung disease in about 30 percent of the X-ray results they read, despite the clear presence of the disease on the film (Source: Malkiel). Other experiments have shown that professional psychiatrists were unable to distinguish between the sane & insane (Source: Malkiel).

A classic example is a study by the American Child Health Association performed in the 1920’s were 1,000 children from the New York City public schools were examined by physicians to determine the need for a tonsillectomy (Source: Malkiel). Of the original 1,000 students, 611 (61.1%) were identified as needing their tonsils removed. The remaining students were then evaluated by another group of physicians who then selected 174 (44.7%) as requiring a tonsillectomy. The remaining group of 215 students were evaluated by a third set of physicians who concluded that 99 (46%) of those students were in need of the operation. The final 116 children were examined a final time and the next group of physicians recommended that 51 (43.9%) students needed a tonsillectomy. The results of this study indicated that parents taking their children to a New York City physician in the 1920’s for tonsil issues were effectively paying doctors to flip a coin!


Realtors, or as I refer to them as the most morally bankrupt reptiles on the plane (I’ve recently had some bad experience with realtors), are another example of anointed professionals who often fail to deliver the insight and results their clients expect.  Realtors, as the video from the Freakonomics Movie, demonstrates how real estate agents are incentivized to work against their clients to quickly ascertain a sale.

If you have found yourself complaining about the time on the market of your home or lack of open houses and your realtors only advise is “lower the price” the video below is essential. The previous examples of investment professionals and medical doctors hinted at the influence of random luck, the example of real estate professionals provides insight into how incentive systems lead anointed professionals to provide you with misinformation. Leverage websites such as and data from your local realtor association to generate your own insight.

Wrapping it Up:

Gurus and professionals are not infallible as the examples provided indicate. In some cases as we explored with investment professionals and tonsillectomy diagnosis, professionals may about as effective as dart throwing chimps or coin flipping. Professionals will be influenced by the consensus of the groups they are in, as was the case with political pundits. Lastly, they may be incentivized to act against your best interests (think real estate agents). Thus, it is essential to do your own homework! Filter the signal from the noise in the data you collect and mindful of the bias of Guru’s as a result of incentives and affiliations.

“All professions are conspiracies against the laity.”

-George Bernard Shaw, Major Barbara

Forget SMART. Be Unreasonable!

“The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man”

-George Bernard Shaw, Maxims for Revolutionists

SMART is an acronym for SPECIFIC, MEASURABLE, ATTAINABLE, RELEVANT, TIME-BOUND which is “preached” as gospel in business school textbooks. Though Specific, Measurable, Relevant and Time-bound are essential, “Attainable” leaves the door open for excuses and under performance. SMART leads organizations and individuals to set small goals (e.g. 10% improvement in insert random KPI) and neglects to do things that can lead to 50% or 100% or more improvement. The key to rapid and meaningful improvement is being UNREASONABLE.


Being UNREASONABLE forces you and your team to think differently and discontinue the status quo. One of my favorite examples of  this is the setup reduction results obtained by Wiremold, the company who is the subject of James Womack’s book Lean Thinking. In each of the examples below setup time was previously measured in hours and ended up being measured in minutes with the smallest reduction being 88%! Setting large goals when it comes to operational excellence enables your organization to truly change the process vs. “just working harder”.

Setup Reduction

Source: Art Byrne, The Lean Turnaround

As Tim Ferriss puts it in his book The 4-Hour Workweek, setting unrealistic goals provides you with an “adrenaline infusion” which will motivate you to be successful. Ferriss also asserts that the level of competition is fiercest for “realistic” goals, paradoxically making them the most time and energy-consuming. So take the road less traveled and set unreasonable goals with these tips:

  1. Select goals that are relevant to you or strategically important
  2. Aim big! 50 to 100% improvement minimum
  3. Break up goals into defined steps with the maximum timeline of 6 months
  4. Do things that get you out of your comfort zone


If the set-up time reduction didn’t do it for you, check out other examples below:

  • Losing 20lbs. of fat in one month (Link)
  • Retire at the age of 30 (Link)


If you enjoyed this post and are curious to learn more about the books referenced check out Tim Ferriss’, The 4-Hour Workweek and Art Bryne’s, The Lean Turnaround using the links below.


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