A Practical Intro to Data Science
blog.zipfianacademy.com
A Practical Intro to Data Science
1–10 of 37 posts
Re: A Practical Intro to Data Science
#2Useful link... but I think intro is a little ambitious based on all the content there.
Re: A Practical Intro to Data Science
#3Useful link... but I think intro is a little ambitious based on all the content there.
Meant to get someone to see the value of their $15,000 6 week course. But this is a great collection. I had no idea the amount of courses out there.
Re: A Practical Intro to Data Science
#4Pretty decent list of links. However, I feel the importance of SQL has been completely downplayed...there are more hadoop-oriented links than there are SQL. Data retrieval and manipulation is where a data scientist will spend 95% of her time, and SQL is still more ubiquitous by far.
Re: A Practical Intro to Data Science
#5Useful link... but I think intro is a little ambitious based on all the content there.
Well we might as well split it into Big Data and "Little Data".
Little Data being:
Have a basic grasp of Python and Javascript/D3.js for the pretty visualizations. That and basic statistics. The latter is probably the one developers (at least here) would spend most of their effort on.
"Little Data" in itself can take you a long way.
Re: A Practical Intro to Data Science
#6Pretty decent list of links. However, I feel the importance of SQL has been completely downplayed...there are more hadoop-oriented links than there are SQL. Data retrieval and manipulation is where a data scientist will spend 95% of her time, and SQL is still more ubiquitous by far.
Ryan, co-founder of Zipfian Academy here. Completely agree -- data scientists can spend up to 90% of their time cleaning and getting their data in the proper format for analysis. This, plus the emergence of Hive and Pig as dominant higher-level abstractions on top of Hadoop, have made robust SQL skills more important than ever. We have an upcoming blog post specially focusing on learning SQL and the differences between SQL/HiveQL.
Re: A Practical Intro to Data Science
#7Pretty decent list of links. However, I feel the importance of SQL has been completely downplayed...there are more hadoop-oriented links than there are SQL. Data retrieval and manipulation is where a data scientist will spend 95% of her time, and SQL is still more ubiquitous by far.
Dang, I wish I could find the link to this...an HP data scientist wrote a short essay (something like "Intro to Data Science") and said that the proper collection and cleaning of data is often seen as dirty grudge work that has to be done (by someone else, hopefully) before the real groundbreaking work can be done. However, the author said, this dirty grudge work is the real work.
When I think about, in my data programming related work, I'd say about 5% is doing analysis or executing statistical routines. And 95% of my time is spent on finding, cleaning, and properly normalizing data. This applies to whether you're a solo researcher or Facebook...think about it: Facebook is a pretty good website, but what it excels better at than just about anyone is being a platform to collect personal data in a way that...well, causes you to quite willingly give it your personal data.
There was a presentation where Peter Norvig pointed out a data routine in which someone had implemented with a naive Bayesian classifier with a comment saying that they'd think of something better...and years later, no one realized it was still a todo. Norvig said something like "You don't have to be very smart when you have a lot of data"
Re: A Practical Intro to Data Science
#8Pretty decent list of links. However, I feel the importance of SQL has been completely downplayed...there are more hadoop-oriented links than there are SQL. Data retrieval and manipulation is where a data scientist will spend 95% of her time, and SQL is still more ubiquitous by far.
I've recently seen this slidedeck [1] from guys on Twitter's data team where they say that most of the time the data mining process is basically:
1. Your boss says something vague
2. You think very hard on how to move the needle
3. Where’s the data?
4. What’s in this dataset?
5. What’s all the f#$#$ crap in the data?
6. Clean the data
7. Run some off-the-shelf data mining algorithm
8. ...
9. Productionize, act on the insight
10. Rinse, repeat
[1] http://www.slideshare.net/Hadoop_Summit/scaling-big-data-min...
Re: A Practical Intro to Data Science
#9>While R is the de facto standard for performing statistical analysis, it has quite a high learning curve
What? R has a ridiculously low learning curve. I remember literally the first time I used R I loaded up a dataset and had a histogram and qqplot within 5 minutes and 3 or 4 lines of code. Just figuring out what libraries I would need to do that in python (and installing them) would probably take me at least 30 minutes.
I think it's still highly debatable if Python is the way to go for general data science, especially if you're spending a lot of time analyzing data. R is more mature, but the tides are steadily moving in python's direction.
Re: A Practical Intro to Data Science
#10Useful link... but I think intro is a little ambitious based on all the content there.
Well we might as well split it into Big Data and "Little Data". Little Data being: Have a basic grasp of Python and Javascript/D3.js for the pretty visualizations. That and basic statistics. The latter is probably the one developers (at least here) would spend most of their effort on. "Little Data" in itself can take you a long way.
What is D3.js?