[HN Gopher] CS109a: Introduction to Data Science - Resources
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CS109a: Introduction to Data Science - Resources
Author : gtsnexp
Score : 192 points
Date : 2022-07-31 13:04 UTC (9 hours ago)
(HTM) web link (harvard-iacs.github.io)
(TXT) w3m dump (harvard-iacs.github.io)
| j-sizz wrote:
| Keras... Yikes
| azangru wrote:
| Video recordings of the lectures seem to require access to
| Harvard's Canvas platform? Is it possible for outsiders to watch
| them?
| adam12 wrote:
| It appears so.
|
| https://harvard-iacs.github.io/2021-CS109A/pages/syllabus.ht...
|
| "If you would like to audit the class, please send an email to
| the Helpline indicating who you are and why you want to audit
| the class. You need a HUID to be included to Canvas. Please
| note that auditors may not submit assignments for grading or
| make use of other limited student resources such as office
| hours."
| laGrenouille wrote:
| These notes might be a great source for what they cover, but as a
| whole I find this to be a good example of what is currently wrong
| with data science education. While the syllabus has bullet points
| that include "1. data collection", "2. data management", and "5.
| communication", the content and schedule have a 90%+ overlap with
| a standard machine learning course. They even use a statistical
| learning textbook (a good one, but still).
|
| Statistics departments keep trying to latch on to the excitement
| (and money) around data science by changing the superfluous
| things like department names and course titles without actually
| adjusting what they teach. I would love to see a version of this
| that actually engages at a non-superficial level with topics such
| as database design, theory(ies) of data visualization, methods
| for storytelling with data, and interactive design.
| p1esk wrote:
| _the content and schedule have a 90%+ overlap with a standard
| machine learning course_
|
| Note that neural networks are not even mentioned in the
| content. This is not a good course to learn modern ML.
| oddity wrote:
| At the time the comment was made the link was
| https://harvard-
| iacs.github.io/2019-CS109A/pages/materials.h... where neural
| networks were mentioned.
|
| See https://news.ycombinator.com/item?id=32295656
| boredemployee wrote:
| >> would love to see a version of this that actually engages at
| a non-superficial level with topics such as database design,
| theory(ies) of data visualization, methods for storytelling
| with data, and interactive design.
|
| I love these discussions and taxonomies in data science. So I
| have a few genuine/honest questions:
|
| 1) isn't what you said more "analytics" or "analytics
| engineering" oriented (which also and itself is a
| subtopic/subfield of data science) ?
|
| 2) I think that more and more people are trying to define what
| "data science" is, specially for marketing purposes, and then
| put it in a box, like any other science (i.e. chemistry - take
| an undergrad chemistry textbook and they will always cover the
| same topics). But since it isn't well defined yet, many
| different courses covers different algorithms/aspects of data
| science, so I think it end up looking superficial and hard to
| please everyone. Would you agree w/ that? For ex. I'm trying to
| find a good and in depth course that applies Data
| Science/Machine Learning in Big Data problems, but I just can't
| find any serious course covering it.
| laGrenouille wrote:
| I completely agree that it's an open question about what
| exactly constitutes data science and what should (or at least
| could) be covered in a standard introduction. For me, a
| fairly reasonable--though certainly not definitive--set of
| topics are five items listed on this course's syllabus. And
| that's what makes this so frustrating, personally. The
| instructors actually have a good proposal of what should be
| taught, but then just turn around and teach a classical
| course in statistical learning.
| ensemblehq wrote:
| The other topics you mentioned aren't exactly classified as
| "data science" so you likely won't see them in most university
| data science courses. Database design has its own course
| usually but I've seen more of the rest as part of
| college/certificate programs.
| endtime wrote:
| The data scientists I've worked with definitely do data
| visualization and storytelling with data. (Schema design, not
| so much...)
| laGrenouille wrote:
| You're thinking too narrowly about what "schema design"
| could mean. No, data scientists do not typically design
| back-end, production database systems. But defining and
| organizing a multi-sheet spreadsheet for manual data
| collection is what many data scientists spend much of their
| time doing (i.e., in the biomedical space). Doing that well
| definitely requires some understanding of concepts such as
| functional dependency, normal forms, and data types.
| infocollector wrote:
| Is there a similar class online with PyTorch?
| bluelightning2k wrote:
| FastAI's machine learning for coders: https://course.fast.ai
| It's amazing.
| asicsp wrote:
| link: https://course.fast.ai/
|
| discussion from 9 days back about the 2022 version:
| https://news.ycombinator.com/item?id=32186647
| [deleted]
| boredemployee wrote:
| I'm new in this field and one thing I have a hard time
| understanding is how to apply all these ML algorithms, python
| libraries, etc. on very large data (i.e. how to deal with the
| memory problem, etc.). If someone could point me to links and/or
| hands-on courses I would really appreciate it.
| fny wrote:
| You use versions of the algorithms that have been rewritten to
| be parallelized. For example:
|
| https://spark.apache.org/mllib/
|
| There are a lot of techniques where this won't be possible due
| to the nature of the algorithm.
| boredemployee wrote:
| Thank you.
| hodanli wrote:
| this is the newer version
|
| https://harvard-iacs.github.io/2021-CS109A/
| nabakin wrote:
| Or this https://harvard-
| iacs.github.io/2021-CS109A/pages/materials.h...
| dang wrote:
| Ok, we've changed to that from https://harvard-
| iacs.github.io/2019-CS109A/pages/materials.h..., since it
| looks like the most recent version of the same page. The home
| page of the course seems relevant too.
|
| Thanks to you both!
| twobitshifter wrote:
| Do you have a link to the videos that are mentioned as being
| posted on the site in the syllabus. I can't get much out of the
| introduction slides and the format seems to be more geared
| towards speaking rather than the content of the slides, which
| is fine, it would just be better with the speaker.
| wyclif wrote:
| Why are non of the newer versions available? I can't get this
| to load.
| theodpHN wrote:
| This seems to be a tremendous amount of material to cover - with
| associated programming exercises to boot - for a course that
| requires only intro courses in CS and Statistics as
| prerequisites. So one does wonder how superficial it might be and
| how much students adhere to the warning about Google usage. Or
| perhaps Harvard students truly are that smart and hard-working
| that they can manage to go deep into all this material while
| managing with the rest of a full course load! https://harvard-
| iacs.github.io/2021-CS109A/pages/syllabus.ht...
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(page generated 2022-07-31 23:01 UTC)