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I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. Bu

I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. But not sure if an MBA would be a benefit. Thoughts from actual data scientists?

ecesena 10y ago
Personally I think a PhD would be better than an MBA if she really wants to do data science, but generally speaking I'm for as more edu as possible, and I'm sure she'll benefit from an MBA too.
- laughfactory 10y ago
  The PhD will only be useful in a small (but growing) subset of data science jobs. Their are data scientists who develop new algorithms and techniques, and those who apply the. For application, the PhD is probably overkill and extensive experience with a bachelor's, or a master's is better. For theory the PhD can't be beat, of course. I say the opportunity costs are high, and should be carefully weighed, because I have my bachelor's and am making more than a data scientist friend of mine who has his PhD. The vast majority of what he learned in the PhD program he hasn't found useful in the real world (yet, anyways). So it's all about what problems you want to work on. Personally I thrive in the application arena of data science, while others thrive in the R&D data science arena. If you like one you probably won't like the other.
  - tgokh 10y ago
    It may not necessarily be the best option for the OP's daughter, but it is possible to do a PhD that uses data science as a tool rather than the focus. There's a lot of interesting work going on in academia using data science in a variety of fields (like medicine) related to predictive analytics (predicting hospital readmission, predicting therapy outcomes), time series analysis (analyzing EKGs, and other vital sign data) etc. A masters would likely allow you to work on similar projects, but if the goal is a PhD, there are certainly application rather than theory focused research groups across the country
  - ecesena 10y ago
    I didn't say necessarily a PhD in machine learning. I think, generally speaking, that a technical PhD vs an MBA could make for a better data scientist. This said, I don't have an MBA, and most of the MBA people I know are not technical at all or try to stay away from the "technicalities". There's for sure a huge need of technically and business savvy people.
- stale2002 10y ago
  Lol, no. Just learn programming and start working for a company. No better way to learn data science than to work as a data scientist.
fny 10y ago
I'd recommend she become familiar with Python/R, go work in the data-science/tech industry for a few years, and then head back for an MBA, which might even be sponsored by her company. IMO, an MBA proves most powerful when backed by real-world experience.
- 11thEarlOfMar 10y ago
  Thanks, that was my intuition as well. I'd actually suggested doing the Stats BS with a couple of intro programming courses, then a data science boot camp, then a couple years as a data scientist before doing the MBA. The reason for the MBA was so she had a sense of the business value of the analyses she was performing, and, was more 'promotable' to executive positions.
deleted 10y ago
[deleted]
imh 10y ago
Never met a data scientist with an MBA. I work with plenty with PhDs (or a few Masters) in CS, stats, or the hard sciences.
StClaire 10y ago
I sat in on some MBA and masters level business analytics courses. My junior-level math stats course went deeper than the business analytics courses go. They had second semester masters students who didn't know how to interpret the coefficients for a linear regression. MBA courses for data science will bore her to tears. If she adds the R and Python and takes a couple graduate level math stats courses before she graduates, she'll know more than almost all the graduates from those programs
nrjames 10y ago
I'm a data scientist with a stats background. I'm also a self-taught programmer (going on 15 yrs of programming in various languages). There are very few data science jobs that I am aware of where somebody will hand you a tidy data set and ask you to start creating models. For those jobs, it's great to go straight from stats/math to data science, as long as you learn a bit of R and some SAS along the way. Most data science jobs that I know of involve a lot of data engineering. That is to say, you need to understand where the data originates, how to get it through an API or via other scripts, how to design and implement a reasonable (often relational) model of it and put it into a database, how to clean it, how to query it into nice and clean output files, and then how to model it. You also need to know how to visualize it effectively and use it to tell a business story. I would not be able to do my job at all if I didn't know Python, R, and JavaScript well (and know my way around various Linux flavors). The modeling is fun, but it comes at the end of a long pipeline requiring a lot of skills that are more engineering oriented. Rarely do my tasks sound like "model this weekly and give me the result." Often, they sound like, "I need you to pull together data from these 5 sources, model it, and produce a weekly report showing these derived KPIs. And I need to be able to access it in a web browser so that I can send links to colleagues. And it needs to be secure. And generating a report across an arbitrary date range needs to take less than a minute." By the way, that is a request that I've gotten at three different jobs. To give you a sense of what this looks like: most recently, I wrote Python scripts to harvest and ETL the data into a Postgres database (running on Google Cloud) and a BigQuery table, then wrote a Flask app to accept the arbitrary report requests and query the database, run models, store computed results in a local SQLite database for fast future retrieval... finally producing dynamic Reveal.js slide decks available through the Flask app. That's a long-winded way of saying that I strongly suggest that your daughter get some practical experience with the data engineering side of data science, preferably using Python for fetching, manipulating, storing, cleaning, and preparing data. It's the most flexible tool for the job and it easily the most important tool in my data science toolkit.
- nrjames 10y ago
  Replying to myself here just to throw out an idea for an easy project that is representative of the type of work a data scientist might be expected to do. Register as a Twitter dev (free) and get the tokens, etc. needed to access the public API. Pick a topic of interest -- hockey, for example, or whatever floats your boat -- and write scripts to harvest all tweets coming off of the public API related to the topic. Design a relational schema for the tweets and push them into a SQLite database. I suggest SQLite because it's ubiquitous and has a low barrier to entry. Something like MongoDB also works well for dumping everything straight off of the API. You can then have another script pull out of MongoDB and push into SQLite, for example. Once you're collecting tweets, storing them, etc... try some unsupervised clustering; k-means, for example, or a decision tree. If you feel up to it, go through 1000 or so of the tweets manually and label them according to some target variable of interest to the project. Then, use that labeled data set to run supervised models. Maybe start with a binary target variable and run a simple logistic regression. Then, visualize the data. There are a lot of ways to go about this, but I suggest trying to use something JavaScript based, such as D3 or p5.js, since it allows you to create interactive web-based visuals. Create a public GitHub repo and push work to it as you progress through the project. When done, use GitHub pages to put a summary of the project online. Twitter data is great because there are tons of variables. It's horrifying because it's like reading the refuse of language, littered with abbreviations, emoji, and other weird characters. However, it's the terrible part of it that makes it great for learning. There are other similar public APIs that would accommodate similar projects. Having a few self-initiated projects similar to this under your belt will really help you when it comes time to apply to graduate school or to jobs. If nothing else, it will give you something to talk about in interviews.
flente 10y ago
I've worked with dozens of data scientists and only one had an MBA. He didn't think it helped him get or do his job, and considered it a poor investment. MS's and PhD's in stats, applied math, physics, computational bio/chem were far more common.
digitalzombie 10y ago
Not actual data scientist but I have a classmate with MBA. We both were in CS in undergrad. She turned out she didn't like managing people and politics so yeah... If you want data science stat and comp sci is the way to go imo. I'm bias cause I'm doing stat for master now. There are some stat classes in MBA and they do prediction and stuff but your daughter may not like it. I certainly don't, they do power point and excel and visualization. Their regression classes ignore checking if their model's assumptions are valid are not (QQplot, residual, etc...) it's very dumb down.

ecesena 10y ago

Personally I think a PhD would be better than an MBA if she really wants to do data science, but generally speaking I'm for as more edu as possible, and I'm sure she'll benefit from an MBA too.

laughfactory 10y ago
The PhD will only be useful in a small (but growing) subset of data science jobs. Their are data scientists who develop new algorithms and techniques, and those who apply the. For application, the PhD is probably overkill and extensive experience with a bachelor's, or a master's is better. For theory the PhD can't be beat, of course. I say the opportunity costs are high, and should be carefully weighed, because I have my bachelor's and am making more than a data scientist friend of mine who has his PhD. The vast majority of what he learned in the PhD program he hasn't found useful in the real world (yet, anyways). So it's all about what problems you want to work on. Personally I thrive in the application arena of data science, while others thrive in the R&D data science arena. If you like one you probably won't like the other.
- tgokh 10y ago
  It may not necessarily be the best option for the OP's daughter, but it is possible to do a PhD that uses data science as a tool rather than the focus. There's a lot of interesting work going on in academia using data science in a variety of fields (like medicine) related to predictive analytics (predicting hospital readmission, predicting therapy outcomes), time series analysis (analyzing EKGs, and other vital sign data) etc. A masters would likely allow you to work on similar projects, but if the goal is a PhD, there are certainly application rather than theory focused research groups across the country
- ecesena 10y ago
  I didn't say necessarily a PhD in machine learning. I think, generally speaking, that a technical PhD vs an MBA could make for a better data scientist. This said, I don't have an MBA, and most of the MBA people I know are not technical at all or try to stay away from the "technicalities". There's for sure a huge need of technically and business savvy people.
stale2002 10y ago
Lol, no. Just learn programming and start working for a company. No better way to learn data science than to work as a data scientist.

fny 10y ago

I'd recommend she become familiar with Python/R, go work in the data-science/tech industry for a few years, and then head back for an MBA, which might even be sponsored by her company. IMO, an MBA proves most powerful when backed by real-world experience.

11thEarlOfMar 10y ago
Thanks, that was my intuition as well. I'd actually suggested doing the Stats BS with a couple of intro programming courses, then a data science boot camp, then a couple years as a data scientist before doing the MBA. The reason for the MBA was so she had a sense of the business value of the analyses she was performing, and, was more 'promotable' to executive positions.

deleted 10y ago

[deleted]

imh 10y ago

Never met a data scientist with an MBA. I work with plenty with PhDs (or a few Masters) in CS, stats, or the hard sciences.

StClaire 10y ago

I sat in on some MBA and masters level business analytics courses. My junior-level math stats course went deeper than the business analytics courses go. They had second semester masters students who didn't know how to interpret the coefficients for a linear regression. MBA courses for data science will bore her to tears. If she adds the R and Python and takes a couple graduate level math stats courses before she graduates, she'll know more than almost all the graduates from those programs

nrjames 10y ago

I'm a data scientist with a stats background. I'm also a self-taught programmer (going on 15 yrs of programming in various languages). There are very few data science jobs that I am aware of where somebody will hand you a tidy data set and ask you to start creating models. For those jobs, it's great to go straight from stats/math to data science, as long as you learn a bit of R and some SAS along the way. Most data science jobs that I know of involve a lot of data engineering. That is to say, you need to understand where the data originates, how to get it through an API or via other scripts, how to design and implement a reasonable (often relational) model of it and put it into a database, how to clean it, how to query it into nice and clean output files, and then how to model it. You also need to know how to visualize it effectively and use it to tell a business story. I would not be able to do my job at all if I didn't know Python, R, and JavaScript well (and know my way around various Linux flavors). The modeling is fun, but it comes at the end of a long pipeline requiring a lot of skills that are more engineering oriented. Rarely do my tasks sound like "model this weekly and give me the result." Often, they sound like, "I need you to pull together data from these 5 sources, model it, and produce a weekly report showing these derived KPIs. And I need to be able to access it in a web browser so that I can send links to colleagues. And it needs to be secure. And generating a report across an arbitrary date range needs to take less than a minute." By the way, that is a request that I've gotten at three different jobs. To give you a sense of what this looks like: most recently, I wrote Python scripts to harvest and ETL the data into a Postgres database (running on Google Cloud) and a BigQuery table, then wrote a Flask app to accept the arbitrary report requests and query the database, run models, store computed results in a local SQLite database for fast future retrieval... finally producing dynamic Reveal.js slide decks available through the Flask app. That's a long-winded way of saying that I strongly suggest that your daughter get some practical experience with the data engineering side of data science, preferably using Python for fetching, manipulating, storing, cleaning, and preparing data. It's the most flexible tool for the job and it easily the most important tool in my data science toolkit.

nrjames 10y ago
Replying to myself here just to throw out an idea for an easy project that is representative of the type of work a data scientist might be expected to do. Register as a Twitter dev (free) and get the tokens, etc. needed to access the public API. Pick a topic of interest -- hockey, for example, or whatever floats your boat -- and write scripts to harvest all tweets coming off of the public API related to the topic. Design a relational schema for the tweets and push them into a SQLite database. I suggest SQLite because it's ubiquitous and has a low barrier to entry. Something like MongoDB also works well for dumping everything straight off of the API. You can then have another script pull out of MongoDB and push into SQLite, for example. Once you're collecting tweets, storing them, etc... try some unsupervised clustering; k-means, for example, or a decision tree. If you feel up to it, go through 1000 or so of the tweets manually and label them according to some target variable of interest to the project. Then, use that labeled data set to run supervised models. Maybe start with a binary target variable and run a simple logistic regression. Then, visualize the data. There are a lot of ways to go about this, but I suggest trying to use something JavaScript based, such as D3 or p5.js, since it allows you to create interactive web-based visuals. Create a public GitHub repo and push work to it as you progress through the project. When done, use GitHub pages to put a summary of the project online. Twitter data is great because there are tons of variables. It's horrifying because it's like reading the refuse of language, littered with abbreviations, emoji, and other weird characters. However, it's the terrible part of it that makes it great for learning. There are other similar public APIs that would accommodate similar projects. Having a few self-initiated projects similar to this under your belt will really help you when it comes time to apply to graduate school or to jobs. If nothing else, it will give you something to talk about in interviews.

flente 10y ago

I've worked with dozens of data scientists and only one had an MBA. He didn't think it helped him get or do his job, and considered it a poor investment. MS's and PhD's in stats, applied math, physics, computational bio/chem were far more common.

digitalzombie 10y ago

Not actual data scientist but I have a classmate with MBA. We both were in CS in undergrad. She turned out she didn't like managing people and politics so yeah... If you want data science stat and comp sci is the way to go imo. I'm bias cause I'm doing stat for master now. There are some stat classes in MBA and they do prediction and stuff but your daughter may not like it. I certainly don't, they do power point and excel and visualization. Their regression classes ignore checking if their model's assumptions are valid are not (QQplot, residual, etc...) it's very dumb down.