Category Archives: Learn Data Science

This is a category for all things related to learning data science.

Heroku Thinks Sharing Data is Important

Last week,  Heroku announced a new feature to its PostgreSQL database service.  The new feature is called Data Clip, and it allows users to share results of an SQL query.  It has options to store the exact data from when the query was originally run or the query can be refreshed to return the current data.  I can definitely see this being useful for debugging of code and troubleshooting, which may have been Heroku’s original intent.

I can also see the Data Clip being very useful for data science and quick sharing of relevant data. I doubt the Data clip can handle huge result sets, but huge data is not always necessary. Sometimes, being able to quickly share data results is just as important. Plus the Data Clip allows the results to be downloaded into Excel, csv, json, or yaml formats. Therefore the data can be easily manipulated from there.

See an example in action.

Visual.ly Launches an Infographic Site

I love infographics because they are a great way to convey information about data. They go well with the thought that Data Scientists need to also be good story tellers. Well Visual.ly is startup that is aimed at helping people create, share, and discover infographics. Here is a quick example I created about my twitter account.

My Twitter Infographic

Another Big Data startup launches.

Gigaom

Hot Mountain View, Calif., startup BloomReach emerged from stealth mode on Wednesday with a message about how its marketing-optimization engine will help ensure that companies get their web pages noticed above the noise online. Using a potent brew of big data techniques presented as a software-as-a-service application, BloomReach says it can significantly improve the amount of traffic on product web pages by making them more relevant to consumers.

The problem right now, BloomReach Co-Founder and CEO Raj De Datta told me, is that companies just cannot know how to best present their product catalogs or other content in a way that best aligns with what customers are looking for. In fact, he said, less than 25 percent of web pages see any traffic from natural search or paid search in any given month. Companies are missing out on large swaths of customers because they can’t display their content in a…

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Don’t Miss – Stanford Machine Learning

In a matter of days, Stanford will begin the second round of the free online machine learning course. I enrolled in the course last fall, and it exceded all expectations. Professor Andrew Ng is great. The prerequisites are minimal, so don’t worry if your math is a little rusty. Also, the videos are short (around 8 – 12 minutes). Therefore, you don’t need large blocks of time set aside. Just watch a video or two during your lunch and you should be able to keep up. There are programming assignments (optional) and review questions to go along with the videos.

Don’t worry if you fall behind. The videos will still be there. The material you learn is more important than the pace. If you don’t know machine learning, the Stanford class is a great opportunity to get started.

Here is Professor Ng’s introduction to the class.

What is a data scientist?

If I am going to create a blog about becoming a data scientist, I must at least provide some type of definition.  One of the best definitions I have read is by Hilary Mason, Chief Scientist at Bit.ly,

A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics, and machine learning.

This definition is short and simple, but there are many more definitions out there.  In fact CITO Research, a site for CIOs and CTOs, set out to define what a data scientist is.  They interviewed six leaders in the data science community, and posted all of the interviews online.  The interviews produced varied results, but focused on some main themes of what a data scientist should know.

After reading Hilary’s definition, the CITO Research interview’s, a great post at Quora, and numerous other articles, I created a list of data science skills:

  • Machine Learning
  • Statistics
  • Story Telling (Communication)
  • Big Data
  • Algorithms
  • Curiosity

I am sure this list will change and evolve over time, but that is where I am going to focus for now.  If you have anything to add to the list, please leave a comment.  If you are interested in gaining some data science skills, please follow along and let’s learn together.