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The Open Source Data Science Masters

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Re: The Open Source Data Science Masters

#11

Post by _coaxialcabal »

nl wrote: post #10

I don't like this curriculum very much - I think it is way too heavy on the data engineering side and way, way too little about the actual mechanics of the data science bit. For example, the words "validation" (as in cross validation) and "overfitting" aren't mentioned anywhere on that page, and yet things like data scraping are mentioned multiple times. With all due respect, I can find lots of people to do scraping,…

Good point. That said, easy enough to augment an expert data scientist with 4:1 data engineering support, whereas a data scientist working solo will spend 80% engineering data. With all the hype and inflated expectations, IMO much easier to hire aspiring data scientists than talented engineers who are satisfied with the data prep and admin aspects. MSPA programs are realistic that the bulk of their graduates will be spending much of their time as data janitors.

Re: The Open Source Data Science Masters

#12

Post by _coaxialcabal »

nl wrote: post #10

I don't like this curriculum very much - I think it is way too heavy on the data engineering side and way, way too little about the actual mechanics of the data science bit. For example, the words "validation" (as in cross validation) and "overfitting" aren't mentioned anywhere on that page, and yet things like data scraping are mentioned multiple times. With all due respect, I can find lots of people to do scraping,…

Good point. That said, easy enough to augment an expert data scientist with 4:1 data engineering support, whereas a data scientist working solo will spend 80% engineering data. With all the hype and inflated expectations, IMO much easier to hire aspiring data scientists than talented engineers who are satisfied with the data prep and admin aspects. MSPA programs are realistic that the bulk of their graduates will be spending much of their time as data janitors.

Re: The Open Source Data Science Masters

#13

Post by jeffmax »

nl wrote: post #10

I don't like this curriculum very much - I think it is way too heavy on the data engineering side and way, way too little about the actual mechanics of the data science bit. For example, the words "validation" (as in cross validation) and "overfitting" aren't mentioned anywhere on that page, and yet things like data scraping are mentioned multiple times. With all due respect, I can find lots of people to do scraping,…

The machine learning coursera course listed on the page covers bias/variance and validation.

Re: The Open Source Data Science Masters

#14

Post by jupiter90000 »

thr0waway1239 wrote: post #5

"how can you afford not to take advantage of an open source education?" But there is a time cost to learning. For example, suppose a Masters degree takes two years, does the author have an estimate of how long it would take to complete her list? And since the list is a little old (in terms of the rate at which this field is progressing), I would add Apache Spark to the list. I watched a video recently about how Scala…

Spark is a topic covered in the intro to data science UW videos, the first mentioned resource on the curriculum.

Re: The Open Source Data Science Masters

#15

Post by markovbling »

pYQAJ6Zm wrote: post #3
Joof wrote: post #2

So far, the downside of this is that many companies seem to require a masters.

What about showing a portfolio of data analyses? Anybody has experience on this?

Yes, I got a data science job advertised as masters only by showing past work and painting a picture of how I could do similar for their industry specific problems. I think credentials are often more a factor in getting the interview which can be hacked through connections and good networking but once you're in the interview, you can often communicate compentency through past work rather than a stamped piece of paper. That said, I've since done a MSc in Statistics and it's definitely easier. Think it helps but not strictly required in all cases (some companies will insist but think especially now with such a shortage of skills, I think you can get away with just a solid portfolio and good networking skills)

Re: The Open Source Data Science Masters

#16

Post by jupiter90000 »

pYQAJ6Zm wrote: post #3
Joof wrote: post #2

So far, the downside of this is that many companies seem to require a masters.

What about showing a portfolio of data analyses? Anybody has experience on this?

Someone got hired on a data science team I worked on with no master's degree, partially due to scraping some of our company's data from our website and showing some cool data science-like stuff using that scraped data to the people interviewing/hiring. That said, this may have been a successful tactic there too because the team would sometimes hire non-master's/PhD holders if a person showed aptitude and 'passion' for this type of work. We had PhDs who were really not equipped to do well at the business level, and those with Bachelor's that made major contributions (and vice-versa). Unfortunately there are still companies and teams out there that think a higher degree is a good filter...

Re: The Open Source Data Science Masters

#19

Post by nextos »

All these curricula seem a bit too complex. IMHO, there's one thing that should be prioritized on top of everything else. The concept of probability, computable probability distributions, and Bayesian inference.

It's the one thing that brings a unifying umbrella to all modes of reasoning under uncertainty. https://probmods.org/ and http://forestdb.org/ seem to be the best resources for this at the moment.

Besides, I dislike Data Science which seems to be a new buzzword. Data Engineering would be more acceptable, as I think people working in companies are building stuff rather than developing new theories. But I don't like it that much.

Re: The Open Source Data Science Masters

#20

Post by [deleted] »

nextos wrote: post #19

All these curricula seem a bit too complex. IMHO, there's one thing that should be prioritized on top of everything else. The concept of probability, computable probability distributions, and Bayesian inference. It's the one thing that brings a unifying umbrella to all modes of reasoning under uncertainty. https://probmods.org/ and http://forestdb.org/ seem to be the best resources for this at the moment. Besides, I…

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