The Interview Games: 10 Tips for Being Successful

Over my relatively short career, I have done many interviews.  Some have been for new jobs while others have been for new clients while working at the same consulting company. While I am sure I still have much to learn about the interview process, here are some insights I have already gathered in my experience in the BI and data analytics job market.

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1. The interview process experience depends greatly on the company culture and role.

In some companies, a culture fit appears to be most important: are you likeable, do you have good "soft" skills.  In consulting, while the broad technical skills are important, the nature of the work requires a lot of personal interaction, self-motivation, discipline, and communication as one works across multiple groups on a regular basis and is only on a project for a short time.  This is what matters most to the consulting company in recruiting employees.  In a more technical full time employment role, the "soft" skills are also important, but the technical skills appear to be much more valued as one works mainly with the same group of individuals in a deeply technical role for a long period of time.

Consequently, the interview style tends to match the nature of the work.  For a consulting position, one looks mainly for personal soft skills.  However, as a consultant interviewing for a technical client position, make sure that you have your technical skills down solid, as these may be the most important skills you have to the client.  You have to have both the soft and hard skills, but mostly it will be your technical skills that are on trial in an interview.  For a technical FTE, one looks mainly for technical skills, but the soft skills are important too, so don't minimize these.

In short, know what kind of company culture and role you are interviewing for, and present yourself accordingly.

2. In person meetings are important for getting an interview.

When possible, it is important to meet with a hiring manager in person before any interview.  This brings to life an otherwise unknown person defined by a piece of paper.  One can better empathize and relate with someone face to face.  And it distinguishes you from everyone else who applied for the position, but never met with the hiring manager.  It is too easy to ignore an email or glaze over a resume.  Don't let that happen to you.  Get a meeting, an "informational", to make that connection.

This is true for both full time positions and for consulting type work.  In consulting, getting in front of a potential client makes all of the difference.  When work becomes available, they will think of you for the position to do that work.

3. A personal connection still matters.

A common respected connection to introduce or recommend you for a position goes a long way in getting that interview, and can even smooth over any difficulties experienced in the interview.  They act as a character and work reference informally, and can root for you and help you prepare.  The fact is, a better candidate may be passed over for another candidate with a personal connection batting for him or her.  Don't ignore this importance, and make the most use of your connections.

In consulting, this is especially true.  Fellow consultants or employees that are known to the client and that can introduce you to the client will help you get the project.  Use the success of your connections to achieve your own success in meeting with clients.

4. Typically, but not always, interviewing is less about assessing ones technical skills and more about assessing one's ability to think and passion for the job.

For technical FTE positions, one may be asked very few technical questions, even though the position is technical.  Why?  Well, most technical questions about coding can be found in a minute through an internet search.  And most technologies can be learned fairly quickly with dedicated study.  Hence, the most important skill for long term success in a role is the ability to think well about a problem and to find an effective solution quickly.  This skill is not so easily learned and is much more valuable nowadays. And if one is not excited about the role, chances are one isn't going to do very good work or be motivated to give one's best.  So conveying passion is necessary.

I say this with the caveat that I have been in interviews where the technical was all that mattered.  My ability or inability to rattle off esoteric code syntax was what determined whether I got the job or not.  This is especially true in consulting.  While your employer may desire you to be a well-rounded individual, a technical client just wants you to deliver using a specific set of skills, and you may be completely judged on how well you can articulate those skills.  Make sure you can.

5. Personality matters, and if you have a personality mismatch with your interviewer, tough luck.  But maybe that is a good thing.

My worst interviews in my experience came as a result of ineffective communication and personality disagreements.  It's hard to interview well when the interviewer is cold, combative, and unclear, but you have to remain warm, excited, professional, and clear.  Perhaps this is even part of the interview, a test to see how well you do under stress and in dealing with a difficult "customer".  Reflecting back now, however, perhaps it is best when those jobs don't work out.  Is it really in my long term interest to work for a team, whether as an FTE or consultant, which has a culture that is negative or in which I just don't fit?  Probably not.

A good job can be characterized by a good project (work/subject matter), good pay (compensation), and good people (coworkers, clients, customers).  Even with good work and good pay, difficult bosses and coworkers can make work miserable.  So don't despair if you and the interviewer don't click.  This may be a blessing in disguise.

6. You have to sell yourself.

Prepare to be a sales person, and the product you are pushing is you.  You can take this in two ways: become everything to everyone you are interviewing with, or put your best foot forward.  I recommend the second route.  If you opt for the first route, you will feel like you are selling your soul in some sense by pretending to be what you are not, and likely, this job will not be a good fit for you anyway.  And people can see through the phoniness that you try to pass off as genuine, so it will likely backfire.  So focus on your strengths, be honest in your weaknesses, and look for jobs or clients that fit what you are excited about and what you can do best.

That being said, you really do need to sell the real you.  You'll probably feel like you are overdoing it, but that's ok.  If you are excited about something, be visibly excited!  Turn gaps in your resume into opportunities for learning.  Explain in detail what you do and what you know.  Be positive and confident.  You need to be likeable.

In short, put the best spin on who you are and what you do in your presentation to the hiring manager or client.  Be true to yourself, but show the best version of yourself that you can.

7. Be prepared for a marathon.

The job search and interview process is grueling.  Be prepared for a long slog and (unless you don't have a current job), wait to begin the process until you can be prepared to put in the effort.  It will feel like working two jobs at the same time.  You need to have the time and energy to do good job searches, prepare for interviews, and conduct those interviews.  If you don't have a good month or two or three to do this in, wait for a better time.  You don't want to hurt your future chances by doing interviews prematurely that do not go well, but which are part of your interview record nevertheless.

As a consultant, one interviews pretty regularly with clients.  But maybe 1 out of 5 of those turns into something.  And with consulting, the stakes are much lower for a bad hire, so the interviews tend to be less intense and grueling.  For an FTE position, maybe 1 in 10 will result in an offer, or perhaps less.  It's a numbers game, and you may have to keep playing for a while before you win in this game of roulette.

8. You can't tell your current boss, until you have an offer.

One can't feel good about the necessary deception (or at least omission) about your job search and interviewing with your current employer.  But what option do you have?  If your job search is known, you may be let go, put on a terrible project, lose a promotion or bonus, etc.  This could especially come back to bite you if you are not successful in finding another job.  So you can't talk about it.  But you still have to go on working as though you will continue to be there long term.  I don't like advocating this duplicity, but I am not sure that there is any other choice here.  In a political working world, one has to be political sometimes.  If you have any better suggestions, I'd love to know them.

Be careful who you trust with knowledge of your job search.  Perhaps you have been blessed with a great manager who cares more about your happiness and long term success than whether you remain a part of the team.  But if that is the case, it is hard to imagine you leaving that situation under normal circumstances.  Best to play it safe if you aren't sure.

That being said, once you have an offer, talk to your boss or manager about it.  You can use the offer as leverage for something more.  "Something more" need not be more money, but it can be whatever reason you might have for thinking about leaving (e.g., promotion, experience).  If you think you can have a good discussion about it, talk to your boss about his or her thoughts on the offer and reasons for going or staying.  He or she may convince you to stay.

9. Great isn't good enough.  You have to be the best

In an employer's market, with tens, even hundreds, of people applying for the same job, getting an interview is an accomplishment.  But even if you get that far, you will still be competing against several others.  It doesn't matter if you can easily do the job and if you are a great fit.  If you aren't the best fit, you won't get the job.  You have to be the best.  Sometimes you aren't, and that is hard, because you didn't do anything wrong.  You just got unlucky, beaten by a better candidate even though you truly did your best and couldn't have done anything more.  Pick yourself up and try again.  If you don't give up, someday, you will be the best and you will get the job.

10. The grass isn't always greener

Why are you interviewing in the first place?  Potential employers will ask you, so you better know why.  Is it for better compensation, more employer engagement, better job experience, a promotion, work-life balance?  What will you gain by leaving?  What will you lose?  Make sure you understand what you really want and the prospects for getting these things.  Then before you decide to interview or leave for other positions, consider how you might bring about or participate in the desired changes in your current role.

If you want a raise or promotion, have you asked for one?  If the work-life balance is bothering you, have you talked with your boss about solutions?  If company engagement is lacking, have you suggested ideas for better communication and engagement?  Changing jobs is difficult, and you especially don't want to trade a mediocre, or even good, known, for a bad unknown that you thought would be great but isn't.

Do an honest assessment of your wants, needs, and expectations, and think realistically about whether these will be satisfied somewhere else, or if you can get them in your current role.  You may be surprised to discover that where you are already is in fact the best place to be all things considered.

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Hope that helps.  What do you think?  What has been most challenging and surprising in your job search and interview experience?

Master's in Data Science and Data Analytics: A List of Programs

Introduction

I recently decided to go back to grad school to work on a Master's in Data Science/Data Analytics.  There are lots of programs to choose from, and many different factors to consider: location, reputation, curriculum, cost, online/on-campus, etc.  While there are many lists available online, here is the list of schools I looked at with information about each program based on my information gathering.

The Programs

Some of this data may get out of date as time goes on. Also, some programs are more complete in their information than others. Please verify the information gathered below with the official information on the website.  This information below is provided merely to get you started in thinking about what you may be interested in looking at.

In alphabetical order, here is a list of programs I looked at:

CUNY (City University New York School of Professional Studies)

• Website:
• https://sps.cuny.edu/academics/graduate/master-science-data-analytics-ms
• Degree:
• MS Data Analytics
• Admission Requirements:
• BA, 3.0 GPA, personal statement, resume, two letters of recommendation, skill assessment, admissions interview
• Time to complete:
• 36 credits, 12 courses
• Part time student: 2-3 years
• Cost:
• Tuition is $425 per credit, $1275 per course (online program = in-state tuition)
• approximately $18,000 after fees
• Curriculum:
• Business focus: very little.  Electives available (e.g., project management)
• Technical focus: very technical.  Topics include security and architecture, web analytics, networks, Hadoop/Mahout, Github, Python, R, MapReduce, SQL, NoSQL, graph databases.  More focus on data science as opposed to traditional BI/analytics.
• Online Time:
• 100% online
• Live classes:
• Yes, but students are not strictly required to attend the weekly meetings.  No grading for attendance.  All meetings will be recorded. 
• Campus Time:
• None
• Faculty:
• Practitioners, not researchers.  Members of data science/analytics business and technology community.
• Application Due:
• For Fall 2016, due by July 15, 2016

Northwestern

• Website:
• http://sps.northwestern.edu/program-areas/graduate/predictive-analytics/index.php
• Degree:
• MS Predictive Analytics
• Admission Requirements:
•  The Graduate Record Examination (GRE) is not required, but strong scores bolster chances for admission.  Letters, resume, statement of purpose, transcripts.
• Time to complete:
• Students are required to complete 12 courses to earn the degree. It is designed to be completed in two to three years of uninterrupted part-time study (one to two classes per quarter), although students are allowed five years to finish the program.
• Cost:
• $49,368 
• Curriculum
• Business focus: not much.  Project management, Theories of leadership
• Technology focus: very technical.  SQL, NoSQL, R, Python, SAS.
  Topics include marketing analytics, risk analytics, text analytics, web and network data science, variable selection, PCA, clustering, GLM, Poisson, survival, ARIMA
  database management, ML, data visualization.  Focused specifically on predictive analytics, as opposed to all around BI.
•  Online Time:
• 100% online.
• Live classes:
• Yes, but you can watch recordings.  Looks like you can choose a day to do a live sync with professors for office hours.
• Campus Time:
• None
• Faculty:
• Unknown.
• Application Due
• Fall - due by July 15, 2016

 

Southern Methodist University (SMU)

• Website:
• https://datascience.smu.edu/
• Degree:
• MS Data Science
• Admission Requirements:
• GRE scores are required for admission to the program. The GRE requirement can be waived if you have five or more years of industry experience in a related field or a previous master's degree.
• Time to complete:
• Students in the program complete 32 credits, with 30 credits of core coursework and a 2-credit immersion experience, which will take place at SMU. 
•  Students can earn the Master of Science in Data Science in 18–24 months.
• This is a rigorous program for highly motivated students. In addition to the weekly class sessions, you can expect to dedicate approximately 10 hours per week per class to studying and completing self-paced online coursework.
• Cost:
• $54,528
•  $1,704 per credit
• Curriculum:
• Business focus: not much
• Technical focus: more technical.  Statistics, ML, and big data.  Python, Github, Shiny, SAS, MongoDB, XML, network security, SQL, No SQL.  More data science than traditional BI.
•  Online Time:
• All online, except for one weekend campus visit required.
• Live classes? 
• Unknown
• Campus Time:
•  There is an extended weekend experience which takes place on the SMU campus in Texas when students have the chance to meet in-person with classmates and faculty for collaborative, hands-on workshops and informational sessions with networking and relationship building opportunities.
• Faculty:
• Unknown
• Application Due:
• There are three cohort start dates each calendar year. January, May, and August.  For the September 2016 Cohort, Priority Application Deadline - May 16, 2016.  Final Application Deadline - July 11, 2016.  Classes Start - August 29, 2016

 

UC Berkley

• Website:
• https://datascience.berkeley.edu/
• Degree:
• Masters in Information and Data Science (MIDS)
• Admission Requirements:
• GRE is required.  No more than five years may have passed between the GRE or GMAT test date and the application deadline.  Resume, transcripts, statement of purpose, etc.
• Time to complete:
• The  program consists of 27 units. Students can complete the program on one of three paths: full-time, accelerated, or part-time.  The full-time path is designed for working professionals and can be completed in 20 months, with two courses per semester.  The part-time path allows students to drop down to one course per semester and complete the program in no more than 32 months.
• 9 courses
• Cost:
• $2,222.22 per unit, plus a $525 semester fee. Tuition is charged per unit.
• $59,999.94 total cost for tuition.
• Curriculum:
• Unknown
•  Online Time:
• While all courses are delivered online.
• Live classes:
• Unknown
• Campus Time:
• Students are required to attend at least one, 3-4 day immersion on the UC Berkeley campus. 
• Faculty:
• Unknown
• Application Due
• The Master of Information and Data Science program starts three times throughout the year (January, May and September).

 

University of Maryland, University College (UMUC)

• Website:
• http://umuc.edu/academic-programs/masters-degrees/data-analytics.cfm
• Degree:
• MS Data Analytics
• Admission Requirements:
• transcripts, statement of purpose, etc.  No GRE required.
• Time to complete:
• 36 credits are required, 6 courses, 6 credits each.
• Cost:
• $24,984
• Curriculum:
• Business focus:  much more business than other programs.  Strategy.
• Technology focus: technical.  Big data, BI, visualization
• A required grad info course.
• A balance of business and technical courses.  More BI than data science focused.
•  Online Time:
• Unknown
• Live classes:
• Unknown
• Campus Time:
• Unknown
• Faculty:
• Unknown
• Application Due
• Unknown

 

University of Washington

• Website:
• http://datasciencemasters.uw.edu/
• Degree:
• MS Data Science
• Admission Requirements:
• GRE required.  Statement of purpose, transcripts, letters of recommendation, etc.
• Time to complete:
• Nine 5-credit courses, for a total of 45 quarter credits.
• Cost:
• $44,775
• Curriculum:
• Unknown
•  Online Time:
• none.  All is on-campus.
• Live classes? 
• Campus Time:
• Classes held in the evenings on the UW Seattle campus.  Classes one or two times a week in evening.
• Faculty:
• Unknown
• Application Due:
• Applications due April 22

 

University of Wisconsin

• Website:
• http://datasciencedegree.wisconsin.edu/data-science-program/
• Degree:
• MS Data Science
• Admission Requirements:
• Letters, transcripts, statement of purpose, etc.  No GRE needed.
• Time to complete:
• 2 year program , 12 courses. 
• Summer, fall, spring semester schedule
• Cost:
• $825 per credit.  36 credits required
• $29,700 for degree
• Curriculum
• Business focus: ethics, decision theory
• Technology focus: R, Python, SQL Server, and Tableau, classification, visualization, network, web analytics, PowerBI, GIT, data warehousing, big data, Hadoop, Pig, Hive.
• Virtual lab with all software preloaded.  Less technical than some but more than other programs.  Gives BI and business background. 
• No electives. 
• Online Time:
• 100% online
• Live classes:
• Unknown
• Campus Time:
• None
• Faculty:
• Mix of math, business, computer science researchers.
• Consulted with practitioners to make sure topics are relevant.
• Application Due:
• Application is rolling.  August 1st is latest for Fall 2016.

 

Villanova

• Website:
• http://analytics.villanova.edu/lpppc-msa/
• Degree:
• MS Analytics
• Admission Requirements:
•  Completed online application, resume, two essays, transcripts, recommendations, GMAT or GRE score (recommended).
• Time to complete:
• Earn your degree in as few as 20 months.  The program consists of five semesters—each of which is divided into two terms. You will take one or two courses per term.
• 33 credits, 11 classes
• Cost:
• $37,950
• Curriculum:
• Business focus: done through the school of business.
• Technology focus: statistics , Hadoop, text and web analytics.  Mostly industry BI software.  Has R, but no Python.
• More business focused, all around coverage of everything BI.  Definitely more traditional BI than data science. Designed to expose students to the whole analytics continuum from data collection through analysis through implementation and use.
•  Online Time:
• 100% online format
• Live classes:
• Courses are primarily delivered in an asynchronous environment using a combination of tools such as recorded presentations, discussion forums, and interactive case studies to let students learn according to their own schedule. However, select synchronous elements including online discussion sessions (all recorded so you can watch them on your own schedule) and virtual office hours are also incorporated into each course.
• Campus Time:
• None
• Faculty:
• All are professors, academicians, researchers, usually with business background.
• Application Due:
• Fall Semester: 6/30/16

Applied, Accepted, Committed

The programs above are CUNY, Northwestern, SMU, UC Berkeley, University of Maryland (UC), University of Washington, University of Wisconsin, Villanova.  Which program did I choose, and why?  Your values may differ, but here is what I was interested in:

• Admission requirement:
• no GRE requirement.  I've been there, done that, and I didn't want to take it again.
• Time to complete: 
• not very important to me.  I was not interested in how long it would take.  In fact, some of the longer programs interested me more because they would cover more ground and be more in depth.  They had more classes.
• Cost:
• low cost is important since I am paying for the program myself, but it is not conclusive.  However, it can be a deciding factor in deciding between two similar programs.
• Curriculum:
•  some programs are more business oriented.  Some are more traditional BI focused.  Still others are more data science and technically oriented.  I wanted to be in a program that was very technical.  I felt that this was where I needed the most help in gaining the skills and experience I needed for the sorts of jobs I was interested in.
• Online:
• I wanted to be in an online program, rather than an in person program.  The program needed to fit my schedule since I would be working and I have many other commitments.
• Live classes:
• I needed to be able to watch recorded lectures, not simply attend live sessions at inconvenient times.
• Campus time:
• not critical one way or another.  A short weekend visit would be fine.  But I didn't want the whole program to be on campus.  Ain't nobody got time for that.
• Faculty:
• I wanted to learn from people on the cutting edge in business and technology.  That is, I wanted to learn from people doing and using the analytics skills being taught.  This field is changing fast, and I wanted to learn from those that knew where it was currently at in the industry and where it was going.
• Application due date:
•  not as crucial, as long as I could get the application in on time.

So which programs did I apply to based on the above criteria?  Here is what I did along with the reasons for making my decision to apply or not:

• Applied:
• CUNY
• 12 courses in semester system, taught by practitioners, no GRE needed, least expensive, very technical.
• University of Wisconsin
• 12 courses, mostly technical, no GRE needed, and not very expensive.
• Villanova
• More BI and business focused, but not as expensive as other programs.  Good reputation.  11 classes and no GRE needed.
• Northwestern
• Very expensive, but has great reputation, great technical curriculum, no GRE required.  12 courses in quarter system.
• Didn't apply:
• SMU
• really technical, but really expensive compared with similar programs.
• UC Berkeley
• really expensive, required GRE, not as many classes offered in program.
• University of Maryland (UC)
• inexpensive, but very few courses and the program was more business focused.  6 long courses.
• University of Washington
• expensive and also not online.  GRE required.  Not as many courses in program - 9 courses in quarter system.

Of the four programs I applied to, I was accepted into the three programs I heard back from before I made my decision (I didn't wait for the fourth program to respond back to me).  Ultimately, I chose CUNY to do my Master's degree.  It was the least expensive, very technical, taught by practitioners (not researchers), and it covered many courses and topics I was interested in taking.  It was, in short, the best fit for my interests and needs for a Master's program in analytics/data science.

Conclusion

Again, the above list is not exhaustive.  There are many other data science and data analytics programs that one can apply to.  These are only the ones that I looked at.  Also, simply because I chose CUNY does not mean that another program may be a better fit for someone else.  An on-campus program may be a better fit for some, a less technical program for others.  Some may not be concerned with cost (especially if their company is paying for it), and others may need to finish a program as quickly as possible.  Still others may be more interested in research as opposed to business/practical applications.

The point is, you need to find the program that is best for you, not me.  Find the program that meets your needs and interests.

I hope the above information helps you in that endeavor, wherever you go and whatever you do in your educational and career aspirations.

Back To Grad School: Master's in Data Science/Data Analytics

I'm going back to grad school.  While I have not made any final decisions as to where, I intend to enroll in in a Master's program in Data Science/Data Analytics.  I have spent the past two months deciding where to apply and then fulfilling the application requirements.  Now I wait to hear back on final responses from the various schools I have applied to.

Why go back to grad school?  Why now?  Why a Master's in Data Science/Data Analytics?  My statement of purpose for my various applications provides the answers:

"I aspire to be a leader in data science and analytics.  To become one, I need to have the necessary training and credentials.  While an undergraduate education in mathematics, a masters in philosophy, and a certificate in data science are all extremely valuable, they are not extensive enough in the right ways to advance me in my career.  My math degree prepared me for deep analysis, but was not broad enough to encompass the entire data cycle.  My philosophy degree taught me critical thinking, analytical skills, and how to write persuasively, but not in a business or data context.  My data science certificate provided me with a view of the broad landscape of data science.  However, it did not do so with the level of rigor or depth required for most technical and advanced positions.  A Master's in Data Science/Data Analytics will offer me a comprehensive range of formal education in data integration, warehousing, analytics, simulation, machine learning, and visualization, while also exposing me to greater depth in each of these areas.

In the short term, this position will allow me to be hired as a data scientist, and not merely as a data analyst.  In the Seattle area, a data analyst is confined to tasks focused on gathering and reporting data.  The modeling and analysis is reserved for data scientists, who focus on the deeper questions raised by surface level reporting.  If I want to be hired as a data scientist, I need to have at least a Master’s degree in Data Science or some other related field.  Long term, this degree will enable me to pursue career opportunities that are reserved for individuals with both higher levels of education and the necessary experience in that field.  In short, a Master's in Data Science/Data Analytics opens up opportunities for me that would be otherwise be closed with my present education and experience.

While I recognize the benefits of receiving a Master's in Data Science/Data Analytics the decision to pursue one has not been made lightly.  I am a full time employee, a husband, and a father of a young child.  I am actively involved in my community in service and activities.  The prospect of taking on another Master’s degree is daunting given my other commitments.  Fortunately, several online and part time programs offer me that opportunity without the fear that earning the degree will be a burden.  These programs are flexible in time commitment each term and offered all online.  Given the curricula, I am confident that they will have the depth and breadth of analytics education needed for me to succeed as a data science professional at the highest levels in business.  Thus, these programs offer me exactly the sort of education I need to advance in my career without placing any personal burdens on me that are too costly to bear.

While such a degree offers me much, what can I offer to the data science field and learning community?  I bring with me a passion for knowledge.  I desire to make sense of our world and our place within it.  I studied several subjects in college and continued on to graduate school.  Afterwards, I continued to study (and still do study) computer programming, data modeling, and business methodology on my own while applying my data skills in both my regular work projects and my personal pursuits in order to succeed in this career path. 

There is always more to learn in the exploding field of data science and analytics.  Since it is hard to keep up, I think many companies are unaware of its potentialities.  Even if they are aware, the ability to use the data is often mired in such bureaucracy that it is nearly impossible to leverage the needed data for deep insight.  What is needed is the coupling of technological advancement with an agile and open business approach to quickly harness the data for effective use.  This vision is what I strive to bring to my projects.  I want to help others catch this vision and to get excited about how their data can improve their decision making. 

Of course, such power needs to be used in a responsible manner.  There is an increasing asymmetry of knowledge between the organization and the individual.  We appreciate the ways analytics technology has improved our lives, but we don’t want to be manipulated or to lose our privacy and safety.  We want leaders to be socially responsible and to improve the world we live in.  This is why I admire companies like Made in a Free World, which uses predictive models to uncover the amount of forced labor in a supply chain, and thereby enables businesses to operate ethically.  I am excited in particular by the application of analytics to healthcare, which has proven useful in predicting and diagnosing ailments.  Whether it is through blogging and sharing discoveries I have made through the use of data analytics and my experiences in the working world, or by regularly participating in and leading knowledge sharing events inside my consulting firm, I strive to apply my analytics skills in ways that can help improve the professional and personal lives of others.

Altogether, I bring with me an interdisciplinary approach to solving problems, using the wide variety of knowledge and experience I have attained through my formal education and work experience.  I am aware of the great possibilities for using analytics to create a better world, but I am also aware of the many dangers that such technological power can pose if used irresponsibly.  I desire to help lead the charge in using data-driven decision making to improve our world.  And by participating in a Master's in Data Science/Data Analytics program, I will be able to more effectively pursue and make use of data science and analytics opportunities in my career."

And so I go, back to grad school.  A little nervous, perhaps, but mostly excited.  I love data and the scientific study of it and its uses.  I love using data to solve problems and answer questions.  Consequently,  I can't wait to embark on this next chapter of my educational and professional career.

Workplace Philosophy as Political Philosophy

I observed recently that many traditionally political concepts had applications in the workplace.  That is, I could think of the workplace as a micro political society, with rulers, laws, economics, and social phenomena.  In thinking about the workplace in such terms, the knowledge related to these concepts could be fruitfully applied to the workplace.  In other words, I could characterize various aspects of the work environment and experience using analogous political concepts, and then apply what I knew about such political concepts to the analogous workplace concept to gain insight.  Make sense?

 

Perhaps this isn't news to you, but I found the comparison to be useful.  Here is an example to show what I mean and how thinking in this way may be helpful.

 

Type of Government = Management and Decision Philosophy

Some of us may jokingly refer to our boss as a "tyrant" and not give the analogy a second thought.  But such jokes contain more truth in them than we often give them credit.  As a consultant, I have worked in many organizations and under many different management styles.  Here are two extremes:

 

Anarchy = The Absent Manager/Decision Maker

I've worked for managers that relied completely on bottom up decision making and allowed for unceasing input and discussion in meetings.  Oftentimes, the manager was unavailable and absent.  Consequently, decisions were rarely made and were not made with authority, unified action, or clear direction.  The manager would swoop in for fire drills (perceived or real) that were often caused by the lack of direction given.  The overall result was that the team experienced chaotic, inefficient, and sluggish behavior in achieving objectives.

 

Tyranny = The Micro Manager/Decision Maker

I have also worked for managers that completely exercised a top down decision making style.  While such leadership was strong and decisive, there was little input or discussion from the ground floor implementers and a general fear of voicing opinions and suggestions. Decisive action often went in the wrong direction.  Employees were fearful and frustrated, feeling undervalued and as though they were always under the gun.

 

History suggests that, as in politics, a middle ground seems to be best.  We can call it a democratic republic style of management and decision making. 

 

Democratic Republic = The Informed Manager/Decision Maker

The ideal manager truly leads.  He or she takes decisive action, but only after soliciting input from those who are responsible for implementing and maintaining business solutions and processes.  Employees feel they are truly heard and respected, knowing that their opinions do matter and are considered as part of any final decision. 

 

This manager trusts his or her direct reports to do their jobs.  Amazing, right?  In fact, this manager backs his or her direct reports in the decisions they make, having set clear expectations and roles for how they are to do their jobs.  The manager empowers his or her direct reports to do their jobs effectively, giving them the resources and authority needed to take action when needed.  Thus, the manager doesn't need to do their job for them.

 

The manager is primarily concerned about setting the general direction of the organization.  He or she only gets involved in the details when unforeseen problems arise or issues are escalated, requiring quick authoritative decision making or a course correction.

 

More details could be added, but you get the general idea.  This manager functions more like a member of congress or a president.  He or she is clearly a leader, but is a leader for the people, for the purposes of the organization and its members.  While not necessarily reflecting current realities, that is how the US government was originally intended to function.  It is a model that has served our country well.  Perhaps it can serve our business organizations well too.

 

There is nothing radical or revolutionary here.  Simply, the observation that a company, an organization, a workplace is a micro society of its own.  Consequently, it can be studied and treated as such by its members, for their good and its success, or their harm and ultimately its failure.

From Trolley Problems to Transactional Databases: My Move from Academia to Business

This post was originally written for use at VersatilePhdD.com with help from the founder of Versatile PhD, Paula Chambers.  Versatile PhD is a site devoted to helping graduate students and PhDs transition to non-academic careers and to be successful in them.  The site provides a community of support, job postings, and transition success stories, among other resources for those thinking about such a transition.

Below is an edited version of my own success story.  Enjoy!

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            Early in the spring of 2011, my first year of a two year MA program, I began to have serious doubts about entering into a teaching career and continuing on into a PhD program in Philosophy.  I became more aware of the job market and what lay ahead of me if I wished to continue studying philosophy at the professional level.  Faced with the prospect of many years of graduate study, followed by a long struggle to find a tenure-track position with little financial incentive, even at the peak of my career, I began to doubt whether this was the right path for my career to take.

I started to discuss my situation with professors and classmates.  Fortunately, the professors I talked with were understanding and sympathetic.  They affirmed that my concerns were legitimate and discussed their own experiences of struggle in obtaining secure teaching positions, the pressure to publish, the challenge of teaching unmotivated students, leftover debt, and the lack of choice of where to live.  Why then did they continue to teach?  Simply put, my professors were so passionate about philosophy that they could not imagine doing anything else.  They would sacrifice money, drive long distances to teach, and move away from friends and family, all so that they could continue to study and teach philosophy for a living.  In other words, doing philosophy was their highest priority.  Like my professors, many of my classmates felt the same way.  I recall that one of my classmate’s idea of taking a break from writing a metaphysics paper was to read an epistemology book.  He, like many others, could not get enough philosophy and didn’t want to do anything else.

Given the sacrifices and choices I would have to make, I realized that, unless I was also passionate about and fully committed to studying and teaching philosophy, I should not do it professionally.  That is, unless I could not conceive of doing anything else as a career besides philosophy, I should choose to do something else.  If I could conceive of alternatives, it was probably better for me to pursue those.  Consequently, later that spring, I chose to stop looking into prospective PhD programs and began to look into alternative career paths. 

Generally, my professors and classmates were supportive of my decision to not apply for PhD programs and to pursue other options.  Some professors were a little disappointed as they recognized that I could have placed well and been successful.  And some classmates, when I voiced my reasons for not continuing on to the PhD level, pushed back and tried to persuade me to stick with philosophy.  Nevertheless, I mostly received support for my decision as the best decision for me.  My department head even told me that his view of success for graduating MA students was not that they would necessarily go into top ranked PhD programs and teach in philosophy.  Instead, it was that each student would graduate with valuable skills in writing, reasoning, and analysis that would make him or her successful in whatever career path the student chose. Thus, I largely had support to move forward in pursuing other options.

            Having come to peace with my decision to leave academia after completing my MA, I needed an alternative career path.  But what would I do now?  Discussing my situation with a close college friend, he suggested I intern with his dad’s database consulting company, over the summer.  As I began my internship, I discovered that many technical skills were simply applied philosophy skills I had already learned.  For example, object oriented programming was simply applied logic, and SQL querying was simply applied set theory.  I had a BA in Mathematics and a logic background from my philosophy training, so the transition to learning SQL and understanding database design and technology was relatively smooth.

The internship went well and I decided to continue on in that direction.  When I returned for my second year of graduate study in philosophy, I took introductory C++ programming, advanced computer programming, and continued to read about database design, programming, and SQL querying on my own.  As such, upon graduation with my MA in spring 2012, I was immediately marketable as an entry level database administrator.

            I continued on as an intern with my previous company that summer until I could be placed on a project.  I worked on internal projects, met with prospective clients, helped my coworkers, and continued to study and develop the skills I would need for this line of work.  Meanwhile, my boss continued to look for opportunities to place me on a project.  In July 2012, one of my friend’s network connections let him know of an available contract position on his team at Microsoft.  My friend then let me know about the position and suggested I consider it and meet with the client for an interview later that day, which I did.

The interview went very well.  The client knew of me from several of my friends and had no concern about my ability to do the work.  Our commonality in having an MA in philosophy probably cemented the deal as he knew I would be able to think intelligently and creatively in dealing with the day to day work requirements.  And, thanks to my internship, classes, and independent study, I had the technical expertise to actually do the work.  In any case, I was offered the job and I accepted.

While continuing to develop my skills on the job, I enrolled in a certificate program in Data Science through the University of Washington.  Through this program I learned more about database design and administration, Business Intelligence, data mining, and predictive analytics.  Now, having graduated from the program and through my experience on the job and continued learning off the job for over a three years, I have become marketable as a database administrator, business intelligence consultant, and data scientist.  Thankfully, and in great contrast to academic teaching at the college and university level, these positions are widely available and those that can fill them are greatly in demand.  As such, I continue to consult in the Seattle area as part of a consulting firm, working in these areas of data management and data analysis.

Some specific practical advice for getting a nonacademic job.  First, you need to prepare yourself technically.  My experience, and the experience of others I know, is that in the business and technology world you don’t need a degree in something to be hired to do it.  You just need to know how to do it.  So continue your education by reading books, taking continuing education courses, doing certificate programs, and taking classes outside your degree while still doing your degree.  Tailor your independent study towards the set of skills needed for the sorts of jobs you are interested in.  And continue your studies at least until you have acquired all of these needed skills.

Second, network.  Most people I know that work in the business and technology world that don’t originally have that background are in these positions because they knew someone who knew someone else who knew someone else… who needed someone like us for a job.  A personal recommendation from an intermediary that knows both the employer and potential employee is incredibly valuable in terms of securing a position.  Talk to your parent’s friends, your friend’s parents, and your friend’s friends that work in fields you are interested in.  Learn how they got to be where they are and get to know them personally.  Ask them what you need to do to get into that field.  And ask them to keep an eye open for an opening wherever they work.  Very likely, your first nonacademic job will come from this sort of connection.