Building New Skills

There is no better gig than getting paid to learn.

In August of last year, I decided to take on the challenge of teaching an applied statistical techniques course—one small problem—I’m not a statistician. Like most scientist and engineers, I’ve used (and abused) statistics all my entire career, so I do have experience with the concepts. Also, my math skills are not too shabby as I have incorporated calculus, differential equations, and other advanced topics into my work over the years. (Yes, some people do use algebra.) The good news? I had about four months before the first day of class and enough control over my schedule where I could commit 10+ hours per week to develop the course content. The real challenge was to create the course using R, a language, and environment for statistical computing and graphics. Being open about my own abilities, I was not proficient with this language.

With this in mind, I set out to create the course—with emphasis on the “applied” and “techniques.” Starting in the fall, I developed a module that students would work through each week. I estimated my 10–12 hours of work each week would translate into the three hours of material for each class and this worked out surprisingly well.

The course is coming to an end this week, and the best part of the experience was how much I learned (or maybe relearned). But most surprising, it was how much I learned the SECOND time I worked through the materials during the weeks I taught the course.

I classify skill sets at three levels:

  • novice: apply basic principles to solve structured problems,
  • hack: gather external resources to solve moderately complex problems,
  • expert: apply advanced principles to solve complex problems with the minimal use of external resources.

I still consider myself a hack when it comes to performing statistical analysis using R, but having the opportunity to expand my own skill set and providing a framework for others to learn something new—that was a great gig.


It is an unfortunate truth that as you get older, you start paying more attention to the obituaries than when you were young.

I recently read about the death of a someone I have fond memories. I hadn’t seen this person in probably 25 or 30 years, but as a young man, they had a significant impact on how I should view the world—to be kind.

Every once in a while we need to be reminded of the events and people who shaped our lives for the better. An obituary is a final remembrance. As I was recently reminded, obituaries are about life. In this case a reminder to embody the kindness that was shown to me as a young man and how it was shown to others.

Computer science for everyone!

Really? For everyone?

I think I agree with the idea of teaching computer science in every Utah school as presented in a recent Silicon Slopes post; however, I wasn’t sure what that means.

What would universal computer science education opportunities for K-12 students in Utah include? Afterall, computer science is a broad discipline which can cover topics as:

Algorithms and Computational Geometry
Architecture and VLSI
Data, Databases and Information Management
Formal Methods and Verification
Graphics and Animation
Image Analysis
Human-Computer Interaction
Machine Learning and Natural Language Processing
Networking, Embedded Systems, and Operating Systems
Parallel Computing
Programming Languages and Compilers
Scientific Computing

Which of these topics are appropriate for K—12? (I compiled the list above from the University of Utah’s department of computer science website.)

I would assert that, currently, only a small fraction of K—12 students participate in Computer Science as defined by the topics above. Robotics is the most obvious example where we see activity, but because of student interest and not part of a broader curriculum. Robotics is an interesting example because it encompasses multiple aspects of computer science (as defined above) including human-computer interaction, programming, networking, and embedded systems. The depth of knowledge needed for each of these will depend on the project; however, it is an example where teachers can integrate multiple projects into the K—12 curriculum.

But would an area like robotics be helpful to all students?

Perhaps a better idea is to add a “computer science” learning objective to the current core curriculum. What would be possible in science courses today?

Biology, Chemistry, Physics, could all incorporate areas such as data visualization and basic scientific computing (i.e., using computers to solve problems), but working with programming languages, compilers, parallel computing, formal verification?

Before jumping on the computer science bandwagon, we need to ask a straightforward question: What knowledge do want students to demonstrate upon graduation from high school?

My observation is that students receive very little formal training on what we would probably claim as necessary computer skills: writing using a word processor, working with spreadsheets and databases, etc. How much of the Microsoft Office (or LibreOffice or GoogleDocs or “insert your favorite, regular computer tasks here”) should students know when they leave high school? These skills are not computer science, but they are useful tools to have in one’s toolbelt.

Do schools offer “typing?” During my first university teaching position, I asked students to type their reports. One student told me directly that she went to a private school and didn’t “type.” (In a voice indicating that she believed typing was below her—this statement was made the late 90s, so computers were not ubiquitous as they are today.) To this day, I am happy I took typing in high school; it has made work using a keyboard easier. Would this be considered an essential skill needed for computer science? Typing, as taught before typewriters became extinct, was targeted towards vocational workers; has it risen in status? I think the proper term today is “keyboarding;” however, is it still treated as a vocational course? Probably so.

If we advocate teaching computer science in K-12 classrooms, we need to define what skills we want students to master accurately. These skills are not going to be determined by the computer science departments; they’re going to be set by the disciplines who have adopted technology as part of their work. In math, students should have the opportunity to explore mathematical equations by defining the function, ranges, etc., as they explore new concepts, but only after understanding how to work through the problems manually. Biology, chemistry, and physics can all implement data analysis, scientific computing, visualization, and other topics into the classroom, but it can’t be at the expense of building a basic understanding of the underlying principles. Building a solid foundation is critical before jumping into complex problems that need the tools of computer science.

I hope someone is carefully thinking this through.

What would be the cost of public financed college?

For the taxpayers of Utah, 1% —1.5% is my best guess.

Bernie Sanders promoted “free college” during the 2016 Democratic Primaries; unfortunately, Senator Sanders wasn’t able to articulate that message into a winning strategy. Democrats, in general, are failing to capitalize on an issue that should resonate with the majority of Americans.

Who is concerned about college cost? If it’s not everybody, it should be. There are nearly 120,000 students enrolled in Utah’s colleges and universities. With approximately 900,000 households, we can estimate that more than 1 in 10 households have a college student and are directly impacted by the cost of college. I would wager that families with school-age children (K-12) are equally concerned. Add grandparents to the equation,  what percentage of Utah households are concerned? You and all these people — hundreds of thousands if not a one to two million — who want the next generation to succeed.

If the cost of college were negligible, wouldn’t the majority of these people (parents, grandparents, aunts, uncles, neighbors) consider college a worthwhile option?

If the definition of a public institution is one that receives the majority of its funding from the public, then the state of Utah is on the verge of losing its public institutions of higher education (Table 1).

tution and tax funds per utah higher ed TABLE 1

So, I want to pose a question: What would it cost to fund public higher education fully?

In Utah, the total FY 2016 budget was $14.2 billion; 12% of that was for Higher Education — approximately $1.7 billion. This amount is the Operating and Capital Budget. Students currently spend roughly $680 million in tuition and fees (Table 2). To fully fund higher education would require increasing the amount of money spent on higher education by 40% — but this is less than a 5% increase in the total state budget.

tution and tax funds per utah higher ed TABLE 2

What would this mean to taxpayers? Census estimates show Utah having just over 900 thousand households in 2016; the median household income was $61,000. A back-of-the-envelope calculation would have the typical family pay less than $800 per year which would amount to a 1% — 1.5% increase in the income tax rate (currently at 5%). If you are a parent who wants their child to go to college, this is a bargain. For those of us whose children will be out of college, it’s a price I’d be willing to pay to provide qualified students the opportunity to succeed along a path with known financial benefits.

Is this a reasonable estimate? (It’s a start.)
Is this feasible? (It seems possible to me.)

It’s not free college — taxpayers will pay the bill, but it’s the ideal of public education and investment in our state we should consider.

The War on College

The Republican Party has openly questioned the value of higher education, and the four-year college degree — ironic given the vast majority of their leadership attended college. The data does not support their position.

Some info:
The U.S. average salary for skilled trades (from the website):
Plumber: $20-$26 per hour
Electrician: $20-$26 per hour
HVAC: $18-$20 per hour
Machinist: $15-$18 per hour

Or, $36,000 to $52,000 per year (midrange of $44,000). The higher paid positions are for Master level positions which require approximately four years of work and exam that occur after 3-5 years as an apprentice and an exam. So the process is not quick and easy. Average apprentice pay is $14-15 per hour.

The U.S. average salary for engineers (civil, electrical, and mechanical) is $65,000 to $72,000.

The U.S. average annual salary for
Biotechnology research associates: $50,000
Chemist: $52,000
Software developers: $75,000

Not into science? the U.S. average annual salary for
Accountants: $49,000
Graphic designers: $41,000 (Senior Graphic designer $61,000)
Human resources specialists: $49,000

The median salary for workers between the ages of 35—44, arguably prime earning years (from

a high school diploma is $32,000
an associate degree is $42,000
a bachelor’s degree is $61,000
a master’s degree is $70,000
a doctorate or professional degree is $100,000

It is possible for a high school graduate to earn over $100,000? For individuals between 35—44 years of age, that number was 4% of those with earnings. For those with a bachelor degree, it was almost 23%!

It is helpful for young people to have options, but they should be aware of how their education level dramatically impacts the potential for financial independence.

The current educational system produced the income distributions summarized above. Efforts to drive more people to “skilled trades” will lower the number of people available to fill higher salary positions that require a bachelor’s degree or greater. It’s those higher salary positions that drive the U.S. economy and the current Republican war on education is short-sighted and self-serving.

“Pick a line!”

With ski season quickly approaching, this quote came to mind.

It’s exciting to visualize the line you want to take when standing at the top of a mountain—especially when you’re pushing down a familiar, challenging track. But even well-worn trails will have unexpected obstacles. A tight line may swing wide, a fast cruise may be interrupted by ski-school, the deep powder can hide a branch that stops your progress.

Second to worse case scenario:  you clip-out, reset, clip-in and continue. (The worse case scenario is someone takes you down the mountain.)IMG_3383

Seems like a good metaphor for life.


When will “talent development” be a real issue?

I would like to step back for a moment and look at the general trends in undergraduate and graduate education in the sciences.

Today, the U.S. is not producing enough science and engineering graduates. According to the National Center for Educational Statistics reports (September 1016), the total number of undergraduate degrees awarded increased by 63% between 1995 and 2015. The number of undergraduate degrees granted, as a percentage of all degrees, in the physical sciences and engineering increased only 52% and 57%, respectively. One bright spot, biological and biomedical sciences did see a 72% increase. Looking at the numbers, 358,000 students received Bachelor degrees in business compared to 98,000 for engineering, 30,000 for physical science and technology, and 105,000 for biological and biomedical sciences in 2014-2015. If the demand for scientist continues to grow, the shortage of talent will continue unless more students develop the skills needed for these industries.

Today, STEM graduates have a multitude of professional opportunities. As a chemist, I’m happy to see that unemployment is quite low, with only three to four percent of chemist or chemical engineers seeking employment (ChemCensus: 2015, American Chemical Society). High employment is excellent news for chemist and scientist in general; however, the number of chemists employed with only a bachelors degree has decreased significantly over the last 30 years, and this trend reflects the changing work environment.

When we think of the traditional path, students would complete their degree and move into the work force and be expected to execute specialized tasks: preparing samples, running analytical test, monitoring processes, etc. Today, employers expect their STEM workforce to not only have strong STEM knowledge, but also understand program management, be articulate communicators—both written and verbal and be able to work with marketing, sales, and business development groups.

Many of these skills, if not most, are not fully develop in an undergraduate STEM program. The good news today is that students have many paths to advance their careers.

The simplest option is to develop these skills while on the job. Larger companies often have corporate learning centers with structured training programs that deal with non-technical job functions such as corporate communication, best practices for meetings, project management, corporate sales training, marketing fundamentals, and basic finance. Technical training might include advanced Excel, SAS, or database workshops. Organizations may also sponsor graduate certificate programs. These are opportunities where one is getting “paid to learn.” (If you are looking at employment options, these benefits are worth asking about and using.)

If learning on the job fills one side of the scale, full-time graduate school is on the other. In the physical sciences, this has traditionally been the realm of doctoral programs while engineering has favored the master’s degree. The National Center for Educational Statistics (September 1016) reports, of the total number of master’s degrees awarded in their field of study, engineering has been steady at approximately 30% over the last twenty-years, while physical science and science technologies have decreased from 20% to 16%. Biological and biomedical sciences have seen master’s degrees increase from 9% to 11%. For doctoral degrees, engineering has remained steady at approximately 7% of total degrees awarded in the field; physical sciences at 15% and biological and biomedical sciences at 7%. Full-time study for both the master’s and doctoral degrees require a substantial time commitment—two to four years for a master’s degree and four to eight years for a doctoral degree. While the student’s research may focus on academic problems, there are excellent opportunities for those who can make the transition to non-academic careers. Unfortunately, outside of engineering, graduate degrees focus on academic research.

An increasingly common path for STEM graduates who enter the workforce is to find opportunities “outside of the lab” where they leverage their technical and analytical skills in the world of business. For many scientists and engineers, pursuing an MBA can round out their skill set and open doors to management careers.

In the late 1990’s, there was recognition that physical science and science technologies would benefit from a professional master’s degree; this led to the formation of the Professional Science Master’s initiative in 1997 that now includes over 356 programs at 165 institutions (including one at the University of Utah where I work). A significant difference between PSM programs and the traditional physical science M.S. or Ph.D. is the focus on industry based projects and internships as part of the degree in place of an academic thesis—while still completing graduate level courses in their scientific field. Additionally, students supplement their science courses with graduate level training in communication, leadership, writing and other areas often neglected in traditional STEM programs yet valued by employers.

There is a consistent message, both from businesses and policy makers that leading industries (biotechnology, data science, cyber security, software engineering, and others) can not fill high skilled positions. Why?

We need to work with students early. These careers require people to follow a challenging academic path that probably starts with high school math. In these new jobs for the 21st century, you WILL use algebra every day (and statistics and calculus, too). Strong math skills developed in high school are a good proxy for success in undergraduate science and engineering, but a four-year science degree is not enough in fields where the technical and intellectual challenges are large. Modern industries have moved toward specialization since the industrial revolution, and now that specialization takes longer than what can be obtained in a four-year undergraduate program. Everyone—students, teachers, employers—needs to understand the road to success is long.

It takes WORK to push through scientific and technological challenges. It can be mentally (and physically) exhausting. I often find myself in the role of “coach,” and it is rewarding to see determined students working to advance their careers by developing new skills. They will be positioned to fill the employment needs of today’s industries. It is emotionally satisfying when you succeed in solving problems—which what most scientist and engineers do.

Grading on a curve

Grading on a curve. The term gets thrown around a lot, especially on a university campus; however, it has meaning beyond academics.

Students believe grading on a curve helps those at the lower end of the distribution.

In academics, it is a measure of student ability compared to their peers so when faculty grade an exam they don’t necessarily care about the absolute grade, but the distribution of scores. For the record, grading on a curve is hard work. You have to calculate the class statistics—mean, standard deviation, etc.—and then determine how many points needs to be added to each test to move the curve to the desired point. (In reality, “scaling” is more often used with the same number of points being added to all scores to change the numerator, or questions “thrown out” to modify the denominator.) I graded on a curve—students on the low end of the curve loved it… students on the high end of the curve hated it because what usually happened is that it helped low scoring students more than it helped those at the top. (For the record, I often felt the need for grading on a curve was due to my inability to craft a good test, not just a reflection on the students abilities.)

But, we are also graded on a curve professionally, and that grade is most often expressed in dollars.

Where is this going? When I take the time to reflect, I ask myself three things:

  1. Do I like what I am doing?
  2. Am I being compensated fairly?
  3. If number two is no, what do I need to change?

Do I Like what I’m doing? Sometimes, when the answer to question one was a “yes,” I stopped. This decision may not have been right financially, after all, if you are treating employment as Me, Inc., the goal should be to maximize shareholder value (being a shareholder of one). Trading time and talents for money should dictate finding the maximum return on those valuable commodities. For the majority of my career, the answer has been a “yes, but…” which leads to question two.

Am I being compensated fairly? The problem is finding the right metric. For most of my professional career, I have used the American Chemical Society (ACS) Salary Calculator. This tool is a member only resource I have found very useful. New graduates and experienced professionals can use the online tool and it covers academic and non-academic positions, degree type, the degree year, geographic location, and additional job details. The output is not just the median but also a breakdown by centiles. Today,,, and other online services are providing basic information (usually, a salary range) or an estimation of your market value.

Is the information accurate? The ACS Salary Calculator is the most direct as it uses data from their annual employment survey using a large member base. From a limited sampling of positions for which I know the salary ranges, and offer useful information to make comparisons. So this rephrases the question to “where am I on the curve?”

I’ve taken comfort at being “above the mean”—or median. Except, of course, those times when I stopped at question one because the gig was just too exciting to worry about money. (I remember my father making the following statement to my musician brother when he stated he had a “gig:” “If you’re getting paid, it’s not a gig, it’s a job.” (Gigs can be a lot of fun if you don’t need the money.)

Being used by an organization is not a good feeling, but if you’re on the low-side of the salary curve, what can you do?

What do I need to change? Unlike a test, in the work world, you can’t rely on the grader to modify the curve. You can ask for a raise and attempt to justify the change based on your performance against the curve, but I can’t think if a case where this worked. The most direct way is getting an offer to work at a similar job for a new company for a higher salary. Again, based on a small sample of managers I’ve discussed this issue with, most are not willing to negotiate based on a competing offer. If you are ready to change companies, it’s a good way to move on the curve. But what if you like the company where you work?

Increasing your capabilities is another way to move on the curve. Are there opportunities at work to learn new skills that will make you more productive or allow you to contribute elsewhere in the group or organization? If not, what can you do outside of work? I have used a fair amount of personal time for professional development. The effort might not lead to an immediate improvement; however, building new skills and solving significant problems should be rewarded. If it’s not, look for another gig—I mean job.

Always ask questions. As a successful scientist or engineer or (insert your field here), what do I need to learn to manage projects or teams effectively? How can I better support my company (business unit) to satisfy our customers, both internal and external?

Which curve are you on? How do you move on the current curve or switch to the next curve?

Of course, you can also jump to a new curve. You may be in the 90% percentile for engineers in the world-wide-widget industry, but if the widget industry pays comparatively low salaries, it’s very likely you won’t be able to move much further on the curve. Unless your skills are directly transferable, you’ll probably need to do something that puts you on a different curve.

Changing where you’re at on any curve, or changing curves, takes time, effort and usually a monetary investment.

  • The time commitment can be substantial: one, two or even 10 years!
  • It takes real effort. It’s much easier to go golfing or skiing on the weekend than to learn skills you’ll use only at work.
  • Funding may be a significant obstacle, but with planning or finding “next best alternatives,” progress is possible.

Science Education: 1990 to 2017

In 1990, Shelia Tobias published “They’re Not Dumb, They’re Different: Stalking the Second Tier.” Billed as “An occasional paper on neglected problems in science education,” the book was published by Research Corporation, a foundation for the advancement of science. I vaguely remember reading the book around 1998 at the beginning of my teaching career; however, after deciding to leave academics, there was no need to think about the topic. Until last year, when I met the author at a national meeting focused on Professional Science Masters programs, and I decided it would be interesting to revisit the book.

Anyone who is preparing students for college should read this book – particularly, if your students are in the typical “college prep” track course work in calculus, physics, chemistry, etc. The book not only attempts to address why able students don’t pursue careers in science but also why students leave the sciences and pursue other studies.

The methodology was unique with seven recent, non–science graduates hired to “seriously audit” first–year chemistry and physics courses. The practices described by the participants are in line with my experiences as an undergraduate science major, with my observations as a Teaching Assistant in graduate school, and as faculty at a state university. As I started my teaching career 20–years ago, making changes to the status quo was not overly encouraged and this was one factor in my decision to leave academics.

So what’s happening today?

Unfortunately, in some areas, not much has changed. Although my impression is akin to someone looking through a single, transparent pane in a broad framework of stained glass, discussions with both my daughters during their first-year chemistry courses indicate the primary focus is still on problem-solving. To be successful, you need to recognize the problem and have the right tools in hand to solve it; asking “why” is less important than asking “how.” Why is it important for citizens to understand science? Why do scientists challenge the status quo? Why is the scientific method important? However, on a positive note, the uber–competitive environments of the past seem less so, and student collaboration is encouraged. (Maybe, over encouraged.) Additionally, the number Department, University, and online resources allow students to learn the topic in ways that better fit their learning style, although making students aware of these resources is difficult.

That college students from 1990 were turned off by the teaching pedagogy was my main take away from the reading. The importance of strong math skills being the second. I don’t believe a revolution occurred during my absence from the University classroom; furthermore, the need for strong mathematical skills is still important and should now include vital digital tools such as spreadsheets and graphical analysis. Reading this book only reminded me of the enormous amount of work still needed to improve science literacy and participation in our country.

First-year Environmental Chemistry After 30 Years

As both of my daughters are now in their first year of college and having a Ph.D. in Chemistry, there is an assumption that you can be a helpful resource with basic chemistry. (For the record, this is not a safe assumption.)

In my efforts to be helpful, I pulled out my first-year Chemistry text. In the spring of 1987 I was completing my second semester of Chemistry, and as I reviewed the old class syllabus, I noticed that Environmental Chemistry was one of the chapters covered. In thinking about the course, I specifically remember Professor John Hubbard making the analogy that the environment was like a buffer. I don’t recall the particular system, but the analogy applies to both the atmosphere and oceans.

The definition of a buffer solution is pretty simple; it’s a solution that resists change in pH upon addition of either an acid or a base. In a broader sesnse, we use the term to describe any system that resists change upon addition of a compound that would alter the equilibrium of a system.

A single section of the chapter discussed the topics of acid rain, photochemical smog, carbon monoxide, and climate. Within this text, a single paragraph summarized the role of carbon dioxide and its role in maintaining surface temperatures. Within this one paragraph, there was the warning “If the calculated effect of doubling of CO2 level on the surface temperature is correct, this means that the earth’s temperature will be 3 degrees C higher within 70 years.” (Chemistry: The Central Science, T. Brown and H.E. LeMay, Jr., 3rd ed., Prentice-Hall, Englewood Cliffs, 1985, p. 393.) Current CO2 levels are 405 ppm (parts per million) compared to 330 ppm as referenced in the 1985 text.
( A 23% increase.

I was curious… can I see this prediction in data from my home locale of Salt Lake City, Utah?

I pulled a simple data set from NOAA’s website–annual averages from 1948 through 2016. Here are the data and a simple analysis.

SLC annual temperatues 1948-2016Annual Average Temperature (°F) for Salt Lake City, UT

It’s pretty remarkable. Over the past 69 years, the average annual temperature is increasing at a rate of 0.05 °F/year (0.028 °C/year).

The average (mean) value over this period is 52.4 °F (11.33 °C) with a 95% lower and upper confidence limits of 52.0 °F (11.33 °C) and 52.8 °F (11.54 °C), respectively. The top and bottom traces on the graph show the 95% prediction intervals.

So what does this mean? If we look at temperatures from 2012 through 2016, they all fall inside the 95% prediction intervals. So, no problem! (Right?)

But go back to the initial premise that the atmosphere is a buffer, when will we know that we’ve exceeded the “buffer capacity” for CO2?

buffer example rev00An example of a buffer curve showing the variable
under observation versus percent completion.

And that’s the problem, we don’t know how much of the buffering capacity we’ve consumed, and we probably won’t know where we are on the curve until after we’ve reached a tipping point, and temperatures accelerate beyond the slow, apparently linear trend we observe today.