I recommend:

"Data Smart: Using Data Science to Transform Information into Insight":

https://www.amazon.com/Data-Smart-Science-Transform-Informat...

This will allow you to excel in Excel with data science.

As others have noted, many data scientists work in Excel. This book, which teaches data science, does just that:

"Data Smart: Using Data Science to Transform Information into Insight" by by John W. Foreman

https://www.amazon.com/Data-Smart-Science-Transform-Informat...

This is likely the quickest way to start.

If you want to know the answer, check out Data Smart[1]

It's a book about doing data science using Excel, until it doesn't make sense using Excel anymore. KMeans, Naive Bayes, Regression, etc. all in Excel, without totally abusing it.

[1] https://www.amazon.com/Data-Smart-Science-Transform-Informat...

Can't answer whether to learn R or Julia, but for a more general education on Data Science, this book might be a really good fit for you:

Data Smart: Using Data Science to Transform Information into Insight

https://www.amazon.com/Data-Smart-Science-Transform-Informat...

All the work is done in Excel. I enjoyed the book quite a bit. Author is Chief Data Scientist for MailChimp.

You can use Excel for more than many people think. For some great examples, and with only a single chapter at the end devoted to R, see Data Smart, by MailChimp's Chief Data Scientist:

http://www.amazon.com/Data-Smart-Science-Transform-Informati...

If you want to learn the nitty-gritty of machine learning and optimization, I highly recommend his book, Data Smart:

http://www.amazon.com/Data-Smart-Science-Transform-Informati...

It's one of the few books on the subject that doesn't get bogged down with mathematical notation, nor does it cheat with "And Then A Miracle Happens" library calls. The book is primarily aimed at business analysts, but programmers can get a lot out of it too.

I agree that the notation is tough. Especially for folks who need to learn to do this stuff as a one-off and not as a career. That's why I wrote a book that teaches the algorithms with nearly no math notation while trying to be very rigorous: http://www.amazon.com/Data-Smart-Science-Transform-Informati...

I recently read a book called "Data Smart" [1], where the author does k-means and prediction algorithms literally in Excel. This was quite eye opening as the view to ML is not so enigmatic to enter. However, the translation of your data into a format/model to run ML is another challenge.

[1] http://www.amazon.com/Data-Smart-Science-Transform-Informati...

Very cool. Naturally, scraping out of HN is gonna provide a few wonky teasers, but a great idea nonetheless. Just submitted my book per the site's instructions for kicks http://www.amazon.com/dp/111866146X

Yeah, google drive spreadsheets are only good for sharing fairly static small sheets. I just wrote an intro data science book that uses spreadsheets to demonstrate certain algorithms (simplex, kmeans, modularity, LOF, etc.) And in the book I warn the reader to not use Drive. Slow, clunky, and the Solver implementation is garbage. Plug:

http://www.amazon.com/Data-Smart-Science-Transform-Informati...