# \[Suggestion request\] An advance book for data science with Julia?

**URL:** <https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387>\
**Category:** Community\
**Created:** [November 22, 2019, 2:03pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387 "2019-11-22T14:03:07Z")\
**Posts on this page:** 15\
**Page:** 1

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**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [November 22, 2019, 2:03pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/1 "2019-11-22T14:03:08Z")

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Does anyone has suggestions for a relatively advanced book on Julia focusing on data science, including statistics and numerical computations? I am thinking to books like _An Introduction to Statistical Learning_ for R or _Python Data Science Handbook_ for Python…

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**Author:** ![xiaodai](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xiaodai/32/15937_2.png) [@xiaodai](https://discourse.julialang.org/u/xiaodai)\
**Post date:** [November 22, 2019, 2:27pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/2 "2019-11-22T14:27:47Z")

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The issue is the DS scene is not as well developed as R’s or Python’s.

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**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [November 22, 2019, 2:29pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/3 "2019-11-22T14:29:40Z")

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…hmmm… I don’t think I do agree :-)… In relative terms to the community size, I believe DS is pretty common in Julia…

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**Author:** ![mthelm85](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mthelm85/32/224164_2.png) [@mthelm85](https://discourse.julialang.org/u/mthelm85)\
**Post date:** [November 22, 2019, 3:34pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/4 "2019-11-22T15:34:41Z")

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I really hate to do this because the authors are clearly very knowledgeable and capable but I can say that I personally did not find the McNicholas/Tait book _Data Science with Julia_ to be very helpful. To be fair, I did not work through the whole book (but that’s because I found it to be so unhelpful). Your mileage may vary but, for me personally, it didn’t really advance my DS or Julia abilities.

I have a couple of free .pdf drafts that (due to the fact they’re free) I can recommend:

Statistics With Julia:

> **[StatisticsWithJulia.pdf](https://people.smp.uq.edu.au/YoniNazarathy/julia-stats/StatisticsWithJulia.pdf)**
>
> 34.72 KB

Applied Linear Algebra (with Julia companion):

> **[vmls.pdf](https://web.stanford.edu/~boyd/vmls/vmls.pdf)**
>
> 7.47 MB

> **[vmls-julia-companion.pdf](https://web.stanford.edu/~boyd/vmls/vmls-julia-companion.pdf)**
>
> 1263.14 KB

I’ve also taken a couple of the machine learning courses on [JuliaAcademy.com](http://JuliaAcademy.com) and my opinion of those is just neutral.

I think the Julia community is really in dire need of a good online data science course like Jose Portilla’s [Udemy.com](http://Udemy.com) Python for Data Science course. I’ve not taken the course but it is highly rated and I’ve seen it referred to in glowing fashion on a variety of forums. I’ve personally taken numerous Node.js/Vue.js/web development courses on Udemy and found them to be absolutely fantastic (I would have been willing to pay much more than I actually had to pay).

I think there is a real opportunity for the right person/people in the Julia community to get together and make a good data science course and make some money in the process (I have no idea how profitable these Udemy courses are). I personally would be first in line to sign up and take the course!

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**Author:** ![xiaodai](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xiaodai/32/15937_2.png) [@xiaodai](https://discourse.julialang.org/u/xiaodai)\
**Post date:** [November 22, 2019, 9:25pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/5 "2019-11-22T21:25:57Z")

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> [@sylvaticus](#):
>
> In relative terms to the community size, I believe DS is pretty common in Julia…

In absolute terms, imo Julia has potential but is under developed.

This is person exp. I was commissioned to write a Julia course and I think many of DS packages do not work with DataFrames.jl nor tables and instead requires matrix input. This makes harder to do DS. Also, things like CCA isn’t as well documented. Mlj.jl is promising and improving fast but needs more documentation and simpler quick start guides.

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**Author:** ![robsmith11](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/robsmith11/32/29641_2.png) [@robsmith11](https://discourse.julialang.org/u/robsmith11)\
**Post date:** [November 22, 2019, 10:31pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/6 "2019-11-22T22:31:47Z")

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I highly recommend “Statistical Rethinking” if you’d like a good intro to develop your intuition for the Bayesian approach to statistical analysis.

The code examples don’t use Julia, but you’d probably want to code them up yourself anyway. There is also a 3rd party repo with some of the book’s examples written in Julia:  
[https://github.com/StatisticalRethinkingJulia/StatisticalRethinking.jl](https://github.com/StatisticalRethinkingJulia/StatisticalRethinking.jl)

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**Author:** ![aaowens](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/aaowens/32/12101_2.png) [@aaowens](https://discourse.julialang.org/u/aaowens)\
**Post date:** [November 22, 2019, 10:39pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/7 "2019-11-22T22:39:12Z")

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I do think something like the new QuantEcon data science lectures ( [https://datascience.quantecon.org/](https://datascience.quantecon.org/) ) would be great for Julia, but so far it’s only in Python. They provide their economics lectures in both Python and Julia, so perhaps this is coming in the future?

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**Author:** ![fipelle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/fipelle/32/4772_2.png) [@fipelle](https://discourse.julialang.org/u/fipelle)\
**Post date:** [November 23, 2019, 12:38am UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/8 "2019-11-23T00:38:28Z")

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> [@xiaodai](#):
>
> I think many of DS packages do not work with DataFrames.jl nor tables and instead requires matrix input. This makes harder to do DS.

Not sure if I agree with that. I see why you would want to use DataFrames for EDAs. However, `Plots.jl` is compatible with them already. For prediction / classification it is often less efficient to work with DataFrames as they would require to be converted into Arrays (when calculations get messy).

In my opinion, having Arrays as an input is actually a good idea. For simplicity, I could see wrappers to convert DataFrames into Arrays upfront (within packages for DS).

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**Author:** ![xiaodai](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xiaodai/32/15937_2.png) [@xiaodai](https://discourse.julialang.org/u/xiaodai)\
**Post date:** [November 23, 2019, 12:43am UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/9 "2019-11-23T00:43:16Z")

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We have different perspectives. I like DataFrame like into because the variable name is important and matrix form loses that info. Converting to matrix is unintuitive to me and makes every thing into numeric. I want factor types and string type support

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**Author:** ![sswatson](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sswatson/32/5135_2.png) [@sswatson](https://discourse.julialang.org/u/sswatson)\
**Post date:** [November 23, 2019, 12:52am UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/10 "2019-11-23T00:52:19Z")

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I’m not sure this will be what you’re looking for, but might be worth a glance.

I’m teaching a master’s level data science course at Brown University and have been developing the content for the course in Julia. I have exposition presented on a website I’m calling [Data Gymnasia](https://mathigon.org/data-gymnasia), [Jupyter notebooks](https://data1010.github.io/class/) with problems that we do in class, and [videos](https://data1010.github.io/class/) which walk through solutions of the problems. You can see a high level overview of the topics covered in this [cheatsheet](https://data1010.github.io/docs/cheatsheets/data1010-cheatsheet.pdf).

One major caveat is that the course is aimed more at developing facility with the mathematical ideas than proficiency in the Julia data science ecosystem. The students take other courses in which they learn data wrangling, etc., using Pandas and ScikitLearn. For this reason, this course is not analogous to the aforementioned Udemy course for Python.

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**Author:** ![Alec\_Loudenback](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/alec_loudenback/32/278_2.png) [@Alec\_Loudenback](https://discourse.julialang.org/u/Alec_Loudenback)\
**Post date:** [November 23, 2019, 2:33am UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/11 "2019-11-23T02:33:05Z")

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This looks amazing, thanks for sharing!

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**Author:** ![StevenSiew](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevensiew/32/218393_2.png) [@StevenSiew](https://discourse.julialang.org/u/StevenSiew)\
**Post date:** [November 23, 2019, 4:42am UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/12 "2019-11-23T04:42:42Z")

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Getting a DRAFT copy of this book isn’t that hard. It is not even protected

[https://github.com/rmcelreath/statrethinking\_winter2019](https://github.com/rmcelreath/statrethinking_winter2019)

 ![52%20PM](https://global.discourse-cdn.com/julialang/original/3X/8/f/8f87af6aab4d5fa0a23601e04449bc9b3cf6ea6b.png)

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**Author:** ![pontus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pontus/32/10438_2.png) [@pontus](https://discourse.julialang.org/u/pontus)\
**Post date:** [November 26, 2019, 1:28pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/13 "2019-11-26T13:28:54Z")

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@sswatson that’s quite an impressive cheatsheet there! Impressive collection of notebooks in a coherent arrangement, many thanks for sharing with us!  
@StevenSiew appreciate the nudge to the latest 2nd ed., had an earlier version. McElreath has become my go-to for expanding stats knowledge and understanding - interesting context for examples, fun video series, great ref material (and up to date), plus _code_ to go along…

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**Author:** ![rajnrao](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rajnrao/32/20637_2.png) [@rajnrao](https://discourse.julialang.org/u/rajnrao)\
**Post date:** [December 30, 2020, 2:06pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/14 "2020-12-30T14:06:31Z")

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Were you able to find what you needed?

We’ve taught a large class on data science and ML in Julia at the University of Michigan. See [mynerva.io/compml](http://mynerva.io/compml) for some of the ‘codices’. If you’d like to try the self-study version check out [mynerva.io/compla](http://mynerva.io/compla)

It is a computational textbook in the sense described in [this Juliacon talk](https://www.youtube.com/watch?v=R84L-BQcjHw)

So it’s very hands-on and experiential. If you want to get a flavor for it go to [Pathbird](http://mynerva.io/courses/register) and after registering try it with the code “JULIACON2020” (no quotes). Try the neural nets one and you’ll get a sense of what to expect.

There is an online version of the course we are doing for folks – see

> [@U. Michigan Online Course Computational ML for Scientists and Engineers](https://discourse.julialang.org/t/u-michigan-online-course-computational-ml-for-scientists-and-engineers/52574):
>
> The University of Michigan’s Continuum Jumpstart Course Computational Machine Learning (ML) for Scientists and Engineers is designed to equip you with the knowledge you need to understand, train, design and machine learning algorithms, particularly deep neural networks, and even deploy them on the cloud. You’ll learn by programming machine learning algorithms from scratch in a hands-on manner using a one-of-a-kind cloud-based interactive computational textbook that will guide you, and check you…

It’s a great time to be learning ML, particularly in Julia : )

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**Author:** ![JeffFessler](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jefffessler/32/6650_2.png) [@JeffFessler](https://discourse.julialang.org/u/JeffFessler)\
**Post date:** [February 11, 2025, 11:12pm UTC](https://discourse.julialang.org/t/suggestion-request-an-advance-book-for-data-science-with-julia/31387/15 "2025-02-11T23:12:25Z")

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Raj and I have a new book from Cambridge University Press:  
_Linear Algebra for Data Science, Machine Learning, and Signal Processing_

> **[Linear Algebra for Data Science, Machine Learning, and Signal Processing |...](https://www.cambridge.org/highereducation/books/linear-algebra-for-data-science-machine-learning-and-signal-processing/1D558680AF26ED577DBD9C4B5F1D0FED)**
>
> Discover Linear Algebra for Data Science, Machine Learning, and Signal Processing, 1st Edition, Jeffrey A. Fessler, HB ISBN: 9781009418140 on Higher Education from Cambridge

It is full of Julia examples and there are (free) online resources in Julia.