A Tour of Data Science Educational Programs

In my quest to become a data scientist, I have embarked on a series of educational journeys, which have included both formal, in-school, educational forums and self-learning, self-paced MOOCS (massive open online courses). Let me start at the beginning of my path to getting an education and training in data science.

Business Intelligence Analytics Advanced Diploma Program

I previously did work as a process engineer for an oil refinery, then switched careers and became a physician, a psychiatrist. After many years seeing patients, teaching medical students, and performing clinical research, I decided it was time to switch to yet another career.

As I was helping my son to pick out courses for his upcoming enrollment at a local community college, I stumbled upon a Business Intelligence Analytics Program at that same community college. As I read through the program description and courses, it looked very interesting to me at the time, and decided to enroll in that program, to learn about business intelligence analytics.

As I went through the program for the first term, it occurred to me that I was more interested in machine learning and predictive analytics, rather than just looking at historical data and presenting the descriptive statistics and what happened last quarter. Although this program gave me a good overview of databases, I found the program lacking in many ways, as the school did not put in the resources to link the students with coop jobs with the local industry in data analytics. So when I was unable to find a coop job in the summer after the first term was completed, I decided that I would need to augment my education, rather than just rely on the program at the school and let the summer go to waste.

MOOCS, Machine Learning, R, and Python

Over the summer break, since I could not find a coop job in data analytics, I decided to look into online learning, also called MOOCS (massive open online courses). I first started with a machine learning MOOC, which was quite informative and hands-on, and became skilled at using R when utilizing various models to apply regression, classification, and clustering to different datasets. From my first success in MOOCS with machine learning and R, I decided to take more courses in computer science and programming.

I particularly liked the MOOC where I was introduced to Python programming. Once I got introduced to Python, I was hooked, as I saw the versatility of Python for web scraping, data parsing, database, analysis, and visualization. Within just 4 weeks of daily Python immersion on MOOCS and solving problems on hackerrank.com, I became good enough to call myself a Python programmer.

It was also at that same time that it dawned on me that I had the requisite skills needed as a computer science student to apply to data science master’s programs. The idea of applying to master’s programs was now a reality, since I had accumulated sufficient skills in database, machine learning, and coding, which was required for many of the master’s programs in data science. Also, when I looked at the various job listings for data scientists, almost all of the companies required either a master’s degree, or 2 to 3 years of experience as a data scientist.

That cemented it for me to look into applying to grad school. Fortunately, I did quite well at the community college with the various database courses I took there, and was able to obtain excellent faculty references, which is one of the main requirements for applying to the master’s programs.

MS Analytics Program

As I mentioned in a previous post, I interviewed at an MS Analytics program, but it did not have computer science and coding as part of its curriculum, and was too focused on statistics. Given their total disregard for coding and computer science, I decided against that program (and that decision was mutual, as they did not offer me a position, citing stiff competition). But that statistics professor was way off base, and obviously did not know the definition of a data scientist, and confused them for statisticians. But data scientists are not only experts at statistics…they are also experts at computer science and coding, in addition to having domain expertise.

This being my first exposure to graduate programs in data science, I began to question their ROI (return on investment), especially when I already have two university degrees, one of which is a technical degree in chemical engineering. I was wondering if certificate and diploma programs in data science may be the better option for me, especially with my industry experience and engineering degree. And of course, I could take more MOOCS, as that is how I learned machine learning and how to code!
But fortunately, my next interview with a graduate program really impressed me, and I impressed them (how do I know this…read on!).

MSc in Computing and Data Analytics

Good news! I was accepted into a Master of Science Program in Computing and Data Analytics. This program is a well-balanced mix of computer science, statistics, and business intelligence. I had to pass a programming test to get in, as they only accept data science grad students who can actually code…imagine that! I’m very glad to be in this program, and looking forward to starting grad school this Fall. For me, this program was the missing piece for my data science training. For me, I have to get a master’s degree in data science, given my other degrees and industry experience was not in the IT industry.

EMC Data Science Certifications

Even though I am slated to start grad school in a few weeks, I still decided to take the EMC Data Science Associate (EMCDSA) course, as many of the practicing data scientists have the EMCDSA certification. Once I obtain my EMCDSA this summer, I plan on continuing to the next level, and work on obtaining the EMC Data Science Specialist (EMCDSS) certification. The great thing about these are that they are also in MOOC format. Fortunately, I have a group to study these courses, and we meet in-person weekly to go over the material we learned during the week.

Summary

So that is my tour of data science educational programs. For me, getting the master’s degree in data science from a well-balanced program is key to my education and training as a data scientist. I don’t believe everyone needs to take my same path, but it is an example of how one person is getting training in this data science field which currently has no official standards for training. For me, the master’s degree will serve as my foundation, while the MOOCS and various data science certifications will augment and enhance my training and experience.

As a word of caution, if you are looking into a master’s program in data science, please pick programs that are well balanced in all the core areas of data science, including computer science (algorithms, coding), statistics, and business intelligence. Skip the ones that ignore computer science, and skip the ones that ignore statistics.

Good luck on your journey to becoming a data scientist, and please contact me should you have any questions.

photo credit: velkr0 classroom via photopin (license)

So You Want To Be A Psychiatrist

Dr. Carandang and colleagues prepare for meeting with researchers from the University of the Philippines, Manila.

If you are considering a career as a psychiatrist, these are important and exciting times for the profession, as it tries to figure out the neurobiological underpinnings of mental illness. Currently, clinical psychiatry does not have objective, biological tests to help confirm mental illness. Rather, mental illness is diagnosed based on history and clinical presentation. However, psychiatry is fast becoming a specialty of medicine based on the brain. The mind, and the various problems and illnesses that are from disorders of the mind, can basically be explained at a molecular level, with neurons communicating with each other via synapses, and these synapses connect to one another via neurotransmitters. These neurotransmitters are the chemicals which carry out the message between neurons, and the receptors of these neurotransmitters are the targets of the psychiatric medications prescribed for mental illness…this is the so-called “chemical imbalance” theory of mental illness. But mental illness is much more complex than a chemical imbalance. In the brain on a macro level, mental processes have specific circuitry, which connect different parts of the brain, and this circuitry is comprised of the neurons which conduct the message between brain areas. Functional neuroimaging is already revealing preliminary evidence that mental illness is associated with disruptions of these brain circuits, and that treatment can normalize these circuits. In addition to neuroimaging research, genetics research is on the verge of finding the constellation of genes responsible for the transmission of mental illness in families. In the next few years, psychiatry should have objective, biological tests to help diagnose mental illness, and cures may be possible.

Given the multitudes of research in the neurosciences to find the biological underpinnings of mental illness, it is a great time to join the ranks of psychiatry. As a student interested in psychiatry, it would be advantageous for you to have an undergraduate degree in a science field, given the neuroscience emphasis in psychiatry over the past two decades. The following knowledge and skill set are important for the modern biological psychiatrist: organic chemistry, neuroscience, statistics, clinical trials, neuroimaging, genetics, epidemiology, psychopharmacology, psychology, evidence-based psychotherapy, biopsychosocial model, business management (for managing your medical practice), and managing clinical teams.

Dr. Carandang teaches at Dalhousie University Department of Psychiatry

In high school, if you already know you want to be a psychiatrist, you should take chemistry, physics, biology, Latin, a second language course (to communicate with patients in your region who speak a different language), English literature, home economics (you need to know basic activities of daily living), athletics (physical activity to model good health), debate team, student leadership positions, and pre-calculus. If possible and if you have the time in high school, take all the AP (advance placement) courses you can find, like AP English, AP Chemistry, AP Calculus, AP History, so you can get college credit and get into a top undergraduate premed program. In high school, I would also recommend that you volunteer for hospitals and medical clinics, as it shows dedication to the medical profession. I would also recommend that you get paid work, as it demonstrates maturity, experience, knowledge, skill, and organization that an employer is willing to compensate.

For college, you should attend a top tier private or public university. People recognize brands, so go to a brand university that everyone knows. And if you have to pick between an Ivy League university and a top state university (of which you are a resident of that state, as in-state residents get the lower tuition costs), pick the top state university, as it is cheaper, and it is easier to be at the top of the class at a state school. Remember, medical schools are looking at those from the top of their class, so if you are at the bottom of the class at Harvard, then you will most likely not be accepted to medical school. It is easier to be a big fish in a little pond. As an example, in Texas, the top private university is Rice University, and the top public university is the University of Texas at Austin. I chose UT-Austin, graduated at the top of my class, and was granted admission to a state medical school, the University of Texas Medical Branch School of Medicine at Galveston. I’m sorry Texas A&M and Baylor, but in Texas, Rice and UT-Austin are tops for undergraduate universities (and for graduate school in UT-Austin’s case).

You have to go by the numbers and follow them…this is not about allegiance to a particular school…it is about getting into medical school, which is highly competitive. As an example, when you look at the 8 allopathic (MD) medical schools in Texas, the majority of the medical school enrolees have undergraduate degrees from Rice or UT-Austin. So the formula to get into the medical school of your state of residence is to try to gain admission to the top private school or the top public school in your state for your undergraduate degree…in Texas it is Rice and UT-Austin; in California, it is Stanford and UCLA; in Nebraska, it is Creighton University and University of Nebraska; in Massachusetts, it is Harvard and University of Massachusetts; in Illinois, it is University of Chicago (Northwestern is probably tied) and University of Illinois.

Once you gain acceptance to your top-tier state private university or top-tier state public university for your undergraduate degree, you can pick any major, but the majority of students who get accepted into medical school have a science degree. This is a matter of convenience for the premed student, as the requirements for applying to medical school are full of science courses. Certainly, you can focus on a non-science major, but you will have to work harder to fulfill all the science courses for pre-med, which will not be included within your non-science degree. From the time you enroll at university, start looking at MCAT test preparation, as a high MCAT score is also needed for acceptance to medical school.

So you made it to medical school…congratulations. In these tough economic times, the competition is fiercer, as the unemployed will join the ranks of returning to graduate schools or professional schools. During the basic science years, focus on neuroscience, behavioral sciences, brain and central nervous system (CNS) dissection in anatomy, pharmacology, genetics, epidemiology, statistics, research methodology, medical ethics and clinical trials. During the clinical clerkship years, focus on family medicine, internal medicine, psychiatry, neurology, neurosurgery, neuroradiology, neuropsychiatry, and psychiatric research.

For psychiatry residency, pick one that is going to give you the best chance of becoming a competent, modern, biological psychiatrist. The future of psychiatry is what I have been discussing above, so you need a program that does research into the neurobiology of mental illness. This type of program will best position you to develop the necessary knowledge and skills to be a modern biological psychiatrist. Although psychiatrists need to learn and utilize psychotherapy, it is not something that is utilized in daily psychiatric practice as a primary modality, as psychiatrists are just too expensive to relegate them to just talk therapy. Avoid psychiatric residencies that focus on psychotherapy only, as you will not learn the biological approach to psychiatry at those programs.

As you see, the modern biological psychiatrist wields diverse knowledge and skill set. The modern psychiatrist also requires an analytical mind to synthesize the various subjective data and produce a formulation for each patient. Hopefully, in the coming years, psychiatrists will be able to utilize objective biological tests to aid with diagnosis and treatment planning. This is an exciting time to join psychiatry, given it is at the brink of finding the cause (and cure) of mental illness.