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How likely is it that a doctor who went to a lower-ranked, less prestigious medical school would be just as good or better than a doctor who went to a prestigious medical school?

Nonmedical people will often ask where a physician went to medical school, but physicians and those hiring them ask, “Where did you train?” After medical school a future physician must do at least three more years of training (called residency) and as many as nine more years including fellowship. As a faculty member of a residency program, I am of course concerned about which medical school an applicant graduated from, but also what they actually did on their clinical rotations dealing directly with patients more than what grades they got in their first year medical school classes in anatomy and biochemistry. After they finish residency they will be licensed physicians and will be able to apply to see patients from specific insurance companies, especially Medicare. Without residency training, they cannot see patients in a hospital or bill most insurance companies for their services. Therefore a physician who does not complete a residency is an inferior physician, regardless of which medical school they graduated from (although it is extremely rare for someone who graduated from a prestigious medical school not to finish a residency). When someone graduates from the program where I taught, they will be asked by colleagues and administrators who want to hire them, “Where did you train?” They can answer, “Oregon Health and Sciences University,” and if they are in Family Medicine, they have trained in one of the top five programs in the country.Therefore, when choosing a physician (if you have a choice) you need to know where they trained and then be able to know what kind of a program that is. If you have a very complicated disease that is also very rare, you probably are best to go to a major medical center which specializes in that disease, or in an extreme case, be referred to the Center for Rare and Undiagnosed Diseases at either Stanford or the National Institute of Health, or the Mayo Clinic. However, it is much more complex. Not only where a person trained, but when they trained can make a difference. MD Anderson is a well-known and reputable cancer center. However, in 2012 a conflict occurred between the president and many of the department chairs. Most of the leading faculty left. As a result, if someone were at MD Anderson between 2011 and 2015 for their fellowship in oncology, they would not have had as good a training as someone who trained there earlier, or is training there now. As a result, the James Cancer Center in Columbus, Ohio, The Barnes Hospital in St. Louis and Dana Farber Cancer Center were infused with some very top quality faculty and these were the better oncology fellowships during the last few years. The University of Washington is now considered the top Diabetes Center, but that would not apply to someone who trained there 15-20 years ago.Stanford has always had a one of the best cardiology fellowships in the Western United States, but the University of Colorado has usually given their fellows more opportunities to perform cardiac catheterization and their cardiac surgeons perform more operations, so which programs trains better? If you are a university hiring a researcher, you want someone from Stanford who has done a lot of research and will discover new things that are still unknown. However, if you are a patient needing a cardiac catheterization or open heart surgery, wouldn’t you be better served by someone with more experience in doing that procedure? If your child has a very unusual congenital cardiac condition, you probably want to go to Harvard or another major research center, but if your child has a typical case of Tetralogy of Fallot, you want to find a surgeon who has already done many surgeries on such children and had very successful outcomes.If you are looking for a physician to be your primary care physician, you want someone who trained at a reputable program in primary care, not someone who mostly did research at a “prestigious medical center.” An internist who trained in the Bronx may have more actual hands on experience than someone from Yale or Duke. However, a subspecialist such as a rheumatologist or head and neck cancer specialist most likely did a residency at a prestigious institution before being accepted into a fellowship.If someone is older and did their training many years ago, the question then becomes-are they staying current. Especially in primary care (family medicine, general internal medicine and pediatrics) physicians have to retake their Board Exams every few years. Is the physician you’re seeing doing that. Especially if the physician is doing “Maintenance of Certification.” This is a rigorous program to keep primary care physicians current. Not all physicians are doing it.The regulations for training programs and even medical schools have changed over the years. Almost 20 years ago, before our program had the reputation it currently has, we sometimes did not get enough applicants for all our positions and would have to take graduates from medical schools outside of the US (usually of questionable quality, especially the for profit schools in the Caribbean that pray on students who don’t get accepted into US medical schools), as well as graduates from schools that had somewhat mediocre programs. Sometimes we had to ask these “doctors” to leave before graduation. Some had to stay on for an extra year of training. A few never passed their Board Exams and could only work in Urgent Care Centers.Things have changed. Medical schools are harder to get into, so no one is accepted into any MD program in the US today that isn’t a genius. However, the person getting into the prestigious medical school will need a very high MCAT score and near perfect grades plus some scientific research experience. This may exclude some excellent students whose grades are a little lower, but have excellent communication skills. As a medical school faculty, I have seen students do extremely well in the first two years (which is mostly classroom learning), but struggle in the last two, which are clinical. When I was in my fellowship at Ohio State University, the number one student in the 3rd year class was asked to limit his training to pathology, so he would never deal with living patients.For the last 20 years the curriculum at American and Canadian MD schools has been fairly closely monitored by the accreditation body for medical schools. I was an honor student, but did not have a 4.0 as an undergraduate. That year there were 40,000 applicants to Stanford for 100 places. I didn’t get in there, but after a couple of years of graduate school, I was admitted to a brand new school with no reputation. We ended up with 3 faculty from very prestigious medical schools, including Harvard. We got the same exams during our scientific classes. Those of us in the top half of the class did just as well as those in the top half of the class at Harvard. The education at most MD schools is pretty homogeneous whether you go to Duke, University of Michigan or some other prestigious medical school or little known schools such as the University of South Alabama or Howard. In residency we had graduates from some of the top schools and some “bottom” level schools, yet we all excelled and got excellent positions, including my fellowship in endocrinology at Ohio State University.The training in residencies and fellowships sponsored by MD institutions are governed by the Committee on Graduate Education and they all must meet high standards and rigorously test their residents annually. Therefore, they all turn out excellent physicians during the last 10 plus years, but each physician may vary in their procedural or clinical skills, especially communication skills. Unfortunately, 15 or more years ago, residencies graduates some physicians with outstanding communication skills, but not good actual medical knowledge of anything but routine cases (which make up 90% of what a physician sees, but they could miss the diagnosis or correct management of cases that were less common). These physicians often have an excellent reputation with their patients and the public at large, but are known to have deficits by their colleagues. As long as they know their limitations and refer those cases they can’t manage to others, it’s not a problem, but there are some who go many years before their state board finds out. Also, the strictness of state boards varies a lot from state to state. I have practiced in Oregon (very strict state) and Arizona (not so strict).There are many physicians in the US who did not go to medical school in the US and it would be difficult for the lay person to know the quality of their medical school. If they graduated from a prestigious residency and/or fellowship, then they are every bit as qualified as a physician who graduated from a prestigious school.The other difficulty in the US, is that there are two types of medical schools: those that grant an MD degree and those that grant a DO degree. Both groups sponsor residency programs and there are even a few DO fellowships. For the last few years, all residencies and fellowships are governed by the American Committee on Graduate Medical Education. So all graduates, both DO and MD, must meet the same standards. However, a few years ago, many of the DO programs were week in assuring that their graduates had adequate exposure to a wide range of patients with different presentations of different disease.The rapid increase in the number of DO schools has in some ways improved the quality of the residents in graduate medical education. Good residencies are expensive and most are very dependent on State Legislatures for funding. A few years ago there were far more residency slots than there were graduates form US medical and osteopathic schools. Weaker residencies had to fill those slots with graduates from medical schools outside the USA. DO graduates often are weak in basic research, but are usually quite strong in communications skills and in most cases speak English as a first language. They are more familiar with American culture and these things make learning much easier when it comes to taking care of Americans, who may not just “do what the doctor say,” as patients do in many developing countries, or are poor, of a different culture, sexual orientation or gender identity. American medical students understand this more than students from developing countries, especially paternalistic countries. So a foreign medical graduate may have excellent scientific skills and the potential to make a great surgeon, they may have difficulty with clinical and communication skills. Unfortunately, so many DO schools are being created, that the US may soon graduate more students from medical school that there are residency slots to finish training them. Those who do not get into a residency program are often forced into research, yet these are often students with little research experience or desire. We are short 30,000 physicians in the US, especially in primary care and the cognitive specialties of Endocrinology, immunology, rheumatology and infectious disease. These specialties do not generate much revenue for a hospital or medical school, so without funding from the state, these programs do not get created. State Legislators need to wake up to this impending catastrophe.

I've published several single-author papers in reputable journals but am receiving my PhD (in the humanities) from a university which ranks *very* poorly. Will I be wasting my time applying for academic jobs?

Yes, a low-ranked university will be a handicap, but if you are interested in an academic job, I think it is worth a shot. Be strategic about your applications. Apply to places where your dissertation topic and published papers will be a great fit. In your application letters, lead with the publications. Emphasize the contribution to scholarship, while name dropping the journals, of your publications. If your university is very low-ranked, then the fact that you nonetheless produced good, publishable work indicates that you have the intellectual autonomy and gift for scholarship that any university should want.I would also suggest that you consider jobs that could be called “para-scholarship.” In my time as a Dept. Chair, I discovered that universities have a large number of jobs where the people in them have advanced education and deal with various academically-related kinds of things. These are not tenure-track professor jobs, but they are often very intellectually interesting and draw upon considerable education. You might be the academic administrator of a fellowship program, or an international institute, or a program to tackle a special topic or issue.Then there are also the jobs that I have dealt with as a professor—librarian and archivist. With a library degree, you could get a job as the acquisitions librarian for the humanities in a college. It would be your job to monitor publications in that area, attend relevant conferences, and collect books in the subject areas that your college emphasizes.If you get a master’s in library/archives, having a Ph.D. is a competitive ADVANTAGE in university libraries and archives hiring, compared to other candidates. Occasionally, a Ph.D. can be hired without the master’s degree.As you have a humanities background, the most right-on job that I can think of is becoming an archivist. Archivists don’t just manage archival papers, they also go out and ACQUIRE the papers of people or organizations in the collection topics of their library or archives. I don’t know what your humanities specialty is, but conceivably you could be in a job working to acquire the papers that are in your own area of interest. So you would be enriching scholarship by collecting more of the research materials scholars need. I once had an advisee who had gotten a Ph.D. in the humanities who had almost gotten several professor jobs, but ended up in second place for several positions. He had reluctantly fallen back on the idea of becoming a librarian. When I told him about the possibilities for becoming an archivist (also taught in “iSchools,” or Information Schools), he was so happy he looked ready to cry. He realized he could still pursue his academic interests in his new career, and immediately switched to an advisor who could take him through the archives specialty for the master’s degree.There are a lot of jobs out there where your interests could be a match. Scan the listings of college and university non-tenure-track jobs as well—not just adjunct teaching but full-time academics-related jobs in universities—with health care and retirement benefits too!

How do I become a data scientist without a PhD?

A while ago, I wrote about some free resources you can use to learn data science on your own. This was mainly geared towards folks who wanted to apply to The Data Incubator's free Data Science Fellowship as a useful "getting started" guide but it's a useful place to start regardless of where you want to apply to be a data scientist. I'll break up my answer into three parts:Free resources broken down by topic: While you are coming at this with an expertise in machine-learning, there are a number of other useful aspects of data science to learn. The response is more general interest.Free Data sources with which you can gain hands-on experience. One of the linchpins of our data science fellowship is the building of a capstone project that you use to showcase your newfound data science knowledge.Apply to be a fellow at The Data Incubator: Obviously, I'm biased but I think we run a great fellowship to help people transition into data science. It's open to people with masters degrees and those with prior work experience. I've included some alumni testimonials as well.#1: New Topics to Learn [original post]Here are five important skills to develop and some resources on how to help you develop them. While we don't expect our applicants to possess all of these skills, most applicants already have a strong background in many of them.Scraping: There's a lot of data out there so you'll need to learn how to get access to it. Whether it's JSON, HTML or some homebrew format, you should be able to handle them with ease. Modern scripting languages like Python are ideal for this. In Python, look at packages like urllib2, requests, simplejson, re, and beautiful soup to make handling web requests and data formats easier. More advanced topics include error handling (retrying) and parallelization (multiprocessing).SQL: Once you have a large amount of structured data, you will want to store and process it. SQL is the original query language and its syntax is so prevalent that there are SQL query interfaces for everything from sqldf for R data frames to Hive for Mapreduce.Normally, you would have to go through a painful install process to play with SQL. Fortunately, there's a nice online interactive tutorialavailable where you can submit your queries and learn interactively. Additionally, Mode Analytics has agreat tutorial geared towards data scientists, although it is not interactive. When you're ready to use SQL locally, SQLite offers a simple-to-install version of SQL.Data frames: SQL is great for handling large amounts of data but unfortunately it lacks machine learning and visualization. So the workflow is often to use SQL or mapreduce to get data to a manageable size and then process it using a libraries like R's data frames or Python's pandas. For Pandas, Wes McKinney, who created pandas, has a great video tutorial on youtube. Watch it here and follow along by checking out the github code.Machine-Learning: A lot of data science can be done with select, join, and groupby (or equivalently, map and reduce) but sometimes you need to do some non-trivial machine-learning. Before you jump into fancier algorithms, try out simpler algorithms like Naive Bayes and regularized linear regression. In Python, these are implemented in scikit learn. In R, they are implemented in the glm and gbm libraries. You should make sure you understand the basics really well before trying out fancier algorithms.Visualization: Data science is about communicating your findings, and data visualization is an incredibly valuable part of that. Python offers Matlab-like plotting via matplotlib, which is functional, even if its ascetically lacking. R offers ggplot, which is prettier. Of course, if you're really serious about dynamic visualizations, try d3.These are some of the foundational skills that will be invaluable to your career as a data scientist. While they only cover a subset of what we talk about at The Data Incubator(there's a lot more to cover in stats, machine-learning, and mapreduce), this is a great start. For a more detailed list of topics, you might want to checkout this great infographic:#2: Cool Data Sources: [original post]At The Data Incubator, we run a free six week data science fellowship to help our Fellows land industry jobs. Our hiring partners love considering Fellows who don’t mind getting their hands dirty with data. That’s why our Fellows work on cool capstone projects that showcase those skills. One of the biggest obstacles to successful projects has been getting access to interesting data. Here are a few cool public data sources you can use for your next project:Economic Data:Publically Traded Market Data: Quandl is an amazing source of finance data.Google Finance and Yahoo Finance are additional good sources of data. Corporate filings with the SEC are available on Edgar.Housing Price Data: You can use the Trulia API or the Zillow API. In the UK, you can find price paid in house sales and historical mean house price by region(use this tool to translate between postcode and lat/long).Lending data: You can find student loan defaults by university and the complete collection of peer-to-peer loans fromLending Club and Prosper, the two largest platforms in the space.Home mortgage data: There is data made available by the Home Mortgage Disclosure Act and there’s a lot of data from theFederal Housing Finance Agency available here.Content Data:Review Content: You can get reviews of restaurants and physical venues from Foursquare and Yelp (see geodata). Amazon has a large repository of Product Reviews. Beer reviews from Beer Advocate can be found here. Rotten TomatoesMovie Reviews are available from Kaggle.Web Content: Looking for web content? Wikipedia provides dumps of their articles. Common Crawl has a large corpus of the internet available. ArXiv maintains all their data available via Bulk Download from AWS S3. Want to know which URLs are malicious? There’s a dataset for that. Music data is available from the Million Songs Database. You can analyze the Q&A patterns on sites like Stack Exchange (including Stack Overflow).Media Data: There’s open annotated articles form the New York Times, Reuters Dataset, and GDELT project (a consolidation of many different news sources). Google Books has published NGrams for books going back to past 1800.Communications Data: There’s access to public messages of the Apache Software Foundation and communicationsamongst former execs at Enron.Government Data:Municipal Data: Crime Data is available for City of Chicago and Washington DC. Restaurant Inspection Data is available forChicago and New York City.Transportation Data: NYC Taxi Trips in 2013 are available courtesy of the Freedom of Information Act. There’s bikesharing data from NYC, Washington DC, and SF. There’s also Flight Delay Data from the FAA.Census Data: Japanese Census Data. US Census data from 2010,2000,1990. From census data, the government has also derived time use data. EU Census Data. Check out popular male / female baby names going back to the 19th Centuryfrom the Social Security Administration.World Bank: They have a lot of data available on their website.Election Data: Political contribution data for the last few US elections can be downloaded from the FEC here and here. Polling data is available from Real Clear Politics.Food, Drugs, and Devices Data: The USDA provides location-based information about the food environment in their Food Atlas. The FDA also provides a number of high value public datasets.Data With a Cause:Environmental Data: Data on household energy usage is available as well asNASA Climate Data.Medical and Biological Data: You can get anything from anonymous medical records, to remote sensor reading for individuals, to data on the Genomes of 1000 individuals.Miscellaneous:Geo Data: Try looking at these Yelp Datasets for venues near major universitiesand one for major cities in the Southwest. The Foursquare API is another good source. Open Street Map has open data on venues as well.Twitter Data: You can get access to Twitter Data used for sentiment analysis,network Twitter Data, and social Twitter data, on top of their API.Games Data: Datasets for games, including a large dataset of Poker hands, dataset of online Domion Games, and datasets of Chess Games are available. Gaming Unplugged Since 2000 also has a large database of games, prices, artists, etc.Web Usage Data: Web usage data is a common dataset that companies look at to understand engagement. Available datasets include anonymous usage data for MSNBC, Amazon purchase history (also anonymized), and Wikipedia traffic.Metasources: these are great sources for other web pages.Stanford Network Data: year, the ACM holds a competition for machine learning called the KDD Cup. Their data is available online.UCI maintains archives of data for machine learning.US Census Data.Amazon is hosting Public Datasets on s3.Kaggle hosts machine-learning challenges and many of their datasets are publicly available.The cities of Chicago, New York, Washington DC, and SF maintain public data warehouses.Yahoo maintains a lot of data on its web properties which can be obtained by writing them.BigML is a blog that maintains a list of public datasets for the machine learning community.GroupLens Research has collected and made available rating data sets from the MovieLens website.Finally, if there’s a website with data you are interested in, crawl for it!#3: Here are some reasons to join:Gain Familiarity with the Latest Industry Technologies: You don’t want to invest 6 months learning a tool that no one uses. But with so many open-source tools out there, it can be hard to know which tools have widespread industry adoption and which ones are pet academic projects. We work with hundreds of employers to stay on top of the latest industry trends to shape and model our curriculum to the tools and techniques that have gained (or are gaining) wide-scale adoption. We also have access to tools and hardware to which a typical individual may have a hard time getting access.Meet Data Scientists at Top Companies: If you are moving into big data from academia or another industry, it can be hard to get your foot in the door. We provide students access to top data scientists from all over the country and help them find interview opportunities at amazing companies like Yelp, EBay, Palantir, Genentech, or the New York Times.Free for Fellows: The program is tuition-free for Fellows whose tuition is supported by placement fees from participating hiring companies. While the fellowship program is very competitive, we also have a paid Scholar option for those who do not quite make the cut or have employer sponsorship.Peer and Alumni Group: Be a part of a great network of top data scientists. As either a Fellow or Scholar, you’ll be working with and learning from other bright, motivated students, making lasting professional connections and building an amazing professional network. You will meet past Fellows who are now hiring managers looking to hire from the fellowship.#4: Here’s what our Fellows are saying about us:Dorian Goldman (NYTimes): "The Data Incubator team did an incredible job of emphasizing the most important and fundamental concepts that a data scientist needs to know in his career – I know, because all of these things were confirmed in my first week at my new job."Justin Bush (Palantir): "Already by the second and third week of the Data Incubator there were companies contacting me that may not have noticed my resume so readily otherwise. I also got a tremendous exposure to the variety of data science jobs out there, something that would not have happened had I taken a job directly out of grad school."Brian Farris (Capital One): " was an extremely efficient way to do a lot of networking in a short amount of time, which greatly increases the chance of finding a job. It is much easier to initiate a dialogue with a hiring partner if you have already met someone from the company in person."Yash Shah (AppNexus): "At The Data Incubator there are so many hiring companies each seeking a varied skill set, there is ample opportunity to find your perfect match."Sam Swift (Betterment): "The intense incubator experience was also a great way to quickly transition my thinking and language from academic abstraction to business pragmatism. Like miscommunication between any two fields, I found that there was lots of common ground on ideas, but that it was obfuscated by specialized jargon on both sides."

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