What skills does an engineer need to be a successful professional?


Last year I took one of the most important decisions in my life, which was to quit my jobs in my home country and move to the United States of America to start a Master of Sciences in Industrial Engineering at University of South Florida, in Tampa, Florida, an educational institution in with the status of “preeminent state research university”.

When I first came to Tampa to get to know the university before starting my studies I had the opportunity to talk to Dr. Tapas Das, Chair of the Department of Industrial and Management Systems Engineering at the University of South Florida. I wanted to know what skills I needed to succeed as an engineer and who better than him to answer these questions.

So I asked him for an interview and he gladly accepted. Here I am telling my experience as an graduate student to understand how what he mentioned in the interview are actually applied in the program I am studying at University of South Florida.

In the next line I will try to summarize some of the aspects that are actually very accurante to what Dr. Tapas Das told me:

  1. Communication is an important skill for engineers (and indeed for every professional): Some of the courses in taking require a lot of presentation skills. I need to be sincere on this. I have been teaching for the last 17 years and this skill is kind of familiar to me, but not every engineering student is able to make a presentation to communicate ideas in a way that they can draw the attention of their audience, use the right amount of content in the presentation with clear objectives within the stipulated timeframe. I have seen this even in experienced professionals both here in the USA and back in Peru. That is why within the data science field I devote most of my time to learn about data visualization and storytelling skills. I think that no matter how good you are in math or statistics as an engineer if you cannot properly communicate your ideas. USF places a lot emphasis in this aspect which translates in homeworks and projects that takes this important aspect into account.
  2. Data driven decision making using data is across the board, everybody is going to benefit from that: the courses I have liked the most in the master program, are related to data. I am taking a course on Statistics this semester as an open student in which I am learning not only how to use a formula to solve an exercise but how we actually apply Statistics in common problems every day, even without knowing we are doing so. Engineering Analytics is a course I liked a lot last semester since we got the opportunity to apply our knowledge in a real data science competition. Being able to apply what you study actually lets you know how good your learning is. That is why I am firm believer in project based learning to solve real problems instead of being just a listener in a class
  3. Team work is important: My experience working in teams has taught me that expecting everyone in a team to contribute equally is not only unrealistic but counterintuitive since not everyone posses the same skills. It is perfectly fine to let somebody contribute more in a part in which one is more skilled and later other members might contribute in other different areas in which they are more skilled. Sometimes people will find hard to understand that approach because in school or even university we were taught or expected to contribute equally but that does not happen in real life. Every individual have different skills and understanding that premise is hard at first, but the best results are obtained that way in my experience. I think that make everyone feel talented in a group despite different contributions made in a project is a challenge but when we succeed, team work succeeds.

In summary, I was quite amazed to learn that nowadays the skills that are most demanded are the ones that build relationships even in classical fields like engineering where math and science skills are imperatively needed. The idea of the lone genius discovering solutions by themselves is long gone by now and data literacy, communication and team work is the new skillset for a successful professional.

Here is the transcript for the short interview to Dr. Tapas K. Das:

AF: In your opinion, what is the profile or skills needed to succeed profesionally as an engineer?

TD: OK, As an engineer, right?. Not only as an industrial engineer, right?.

AF: Right, as an engineer. 

TD: I think that the most important skillset for an engineer is communication. The ability to communicate both in writing and verbally, in both ways, is an important aspect of engineering. Engineers makes decisions and for decision making, they need to be involved in team work and so if they can communicate to the team, they can make good decisions. Of course they need to know the engineering part of it too, but the reason I am putting communication ahead of engineering skills is because we know no matter how much you know you are not going to be able to do well without these skills.

AF: Soft skills?

TD: Actually nowadays people are objecting to call them soft skills, because by saying soft skills it seems like we are taking the value off from those skills, by calling them soft. Those are hard skills too: communicating in both ways, in written and in verbal form, it is not easy.  Leadership, team work, etc. these are all skills that need to be acquired.

AF: Should we called them main skills?

TD: Yes, they are an integral component, these are like a portfolio that an student, an engineer must acquire.

AF: What technologies do you think will have the most impact in our society in the next years?

TD: I do not have a special potion to answer this question but undoubtedly data skills: skills to put data into decision making is going to be an skill that will be above most skillsets in coming years, as clearly as you can see, because in the past people went to dig for gold, now gold is hiding in the data, data is the new oil. Now you can mine in the data to find the value you are looking for.

AF: That skill is for any professional not only engineers, right?

TD: For every professional. Data driven decision making using data is across the board, everybody is going to benefit from that.

AF: What books, fiction of non fiction, would you recommend to read to an engineer?

TD: I am not sure that I have an specific recommendation for a book to read, there are plenty of books in those areas: writing, communication, team work, etc..

AF: Is there any professional field that you would recommend to an engineer to work in?

TD: That is a broad question. I think, fields where data driven intelligence can make more gains. That is what everybody is talking about now. Everybody is talking about artificial intelligence, which is really a fancy nice word, even though is not new, it is been there forever. Artificial intelligence is finally coming to benefit us. Now we have the ability to really benefit from it, because we have the tools to glean the intelligence from data. We have the algorithms, we have the computing power, we have the tools, the sensors that are collecting data. Now it is the time for artificial intelligence. So I think that engineers looking to choose their field, it does not matter whether it is in healthcare, manufacturing, service areas, banking, consulting, etc. everything is data driven. So developing skills to build artificial intelligence or being able to learn what is in the data through those algorithms is going to be the main push. Everybody is looking for engineers who can do that, who can work with A.I.. Artificial intelligence is the key, is the set of the keywords right now.

Here is a little fragment of the video of the interview:

How Analytics is changing football


In recent years, Analytics has influenced the tactics and decisions in professional sports, such as baseball and basketball, but traditionally football (also called soccer in the USA) was not one of the sports that heavily relied on analytics to make decisions, maybe because football was assumed to be unsuited to the analytical approach.

To illustrate this difference with baseball, an interesting case study is the Oakland Athletics, which began to use an analytics approach to baseball by focusing on sabermetric principles. This started when Billy Beane took over as general manager in 1997 and then hired Paul DePodesta as his assistant. Through the statistical analysis done by Beane and DePodesta in the 2002 season, the Oakland Athletics went on to win 20 games in a row. The success of the Oakland Athletics encouraged sports teams around the world to replicate the model pioneered by Billy Beane.

His approaches to baseball soon gained worldwide recognition when Michael Lewis published “Moneyball: The Art of Winning an Unfair Game” in 2003 to detail Beane’s use of Sabermetrics and how Oakland Athletics’s baseball team found a competitive advantage by evaluating players using a different criteria. In 2011, a film based on Lewis’ book also called “Moneyball”, was released to further provide insight into the techniques used in the Oakland Athletics. Sabermetrics is the analysis of baseball statistics that measure in-game activity, by collecting and summarizing the relevant data to answer specific questions. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research, founded in 1971.

Unlike baseball, following conventional wisdom, football seemed apparently impossible to quantify. Much of the game involves moving the ball from player to player while waiting for an opportunity to create a situation to score. But this was proved wrong when Ian Graham, a PhD in physics from Cambridge University, built from scratch his own database to track the progress of more than 100,000 players worldwide, so that he could recommend which of them Liverpool F.C. should acquire, and then how these new players should be part of the strategy of the club. Graham’s main responsibility is helping Liverpool F.C. decide which players to acquire. He does that by feeding detailed data on games into his decision models and, contrary to what we would expect, he does not watch football games in order to create these models, because he thinks it helps to create a negative bias to make appropriate decisions.

Liverpool F.C. results in recent years are the tangible evidence that the strategies were working, being both a runner-up and a champion in the last two seasons of the UEFA Champions League and whatever their future outcomes are, Liverpool’s outstanding results have already started to make data-crunching a fashionable trend, not only in England but beyond. As a result more football clubs contemplate hiring data analysts with no soccer backgrounds to try to replicate this unique success.

Additionally, it is worth noticing that Graham recommended Liverpool F.C. to acquire Egyptian footballer Mohamed Salah in 2017, who in that moment was playing in Italy. That year, Liverpool F.C. paid Roma, an Italian football club, about USD 40 million for Salah. Graham’s data showed that Salah would make a good match to Brazilian player Roberto Firmino, another of Liverpool’s strikers, whose statistics show that he generates more expected goals from his passes than nearly anyone else in his position, and eventually that prediction turned out to be true: during the following season 2017-18, Salah turned those expected goals into actual ones and at the same time broke the Premier League record by scoring 32 times in a season.

Data analysts are now recording data from thousands of actions during games and training sessions. But it is not so much about collecting the data. It is more about making sense of this data. Analytics and big data are driving the strategies of major corporations around the world and these methods are now begin applied into football, from the boardroom to the boot room. Football clubs over the last decade have had to deal with a technological revolution and what that meant is that they now have started to collect lots of data. Sport data is basically a reconstruction of the match. But, why is it useful to collect all this data? The main reason is to have a way to tell a detailed story of how a specific match was played and have the possibility to look at it through various lenses, for example how many passes and shots were made, this is the event data, and if the tracking data is collected as well, for example using wearable GPS tracker vests, we can also see the detailed activity of each player or dots running around the field or heatmap visualizations, so that it is possible to tell the detailed story of a match in a better way, since everything a player does is recorded.

Conceptos básicos de Lean Analytics

El libro Lean Analytics Book indica cómo se debe de medir un negocio basado en los siguientes arquetipos:

  1. Ecommerce
  2. Marketplace
  3. Software As a Service
  4. Mobile App
  5. User Generated content
  6. Media

Principales métricas para saber si un producto tiene atracción

1. Adquisición del Cliente

  • CPC (Cost Per Click)
  • CTR (Click-Through Rate)
  • CAC (Customer Acquisition Cost)

2. Productos Digitales

  • Número de descargas
  • Usuarios activos diariamente
  • Promedio de ingresos por usuario

3. Fidelización

  • Tasa de referencia
  • Coeficiente viral
  • Tasa de recompra

4. Valor del Cliente

  • LTV (Lifetime Value)

5. E-Commerce

  • Promedio de compras en la web
  • Tasa de abandono

6. Email Marketing

  • Tasa de apertura del mail
  • Costo por suscriptor
  • Tasa de crecimiento de suscriptores.