Diana Shealy - Treasure Data Blog

Author Archive for: "diana"

Debugging Your Data: 4 Tips for Easier Data Management

It happens to us all, you receive the new A/B testing results and need to verify the data. Or you are turning your latest funnel analysis into a data application so you can continually reap the benefits of your hard work. As you scrutinize your work, you notice that the numbers aren't quite adding ...

Accurate Sales Forecast for Data Analysts: Building a Random Forest model with Just SQL and Hivemall

Rossman is a pharmacy chain with over 3,000 stores in seven countries within Europe. The manager of each store has been tasked to predict the sales of the store for up to six weeks in advance. Sales of each store depends on various factors such......

We Analyzed Over 6 Million SQL Queries & Here’s What We Found

As part of our effort to improve product usability, I decided to take a look at how other analysts and TD users write their SQL. Like any other language, you can develop your own style over the years, and we should design our query editor accordingly......

Dimensionality Reduction Techniques: Where to Begin

When dealing with huge volumes of data, a problem naturally arises. How do you whittle down a dataset of hundreds or even thousands of variables into an optimal model? How do you visualize data through countless dimensions? Fortunately, a series of techniques called dimensionality reduction aim to...

The Analytics & Data Science Hierarchy of Needs

Inspired by Maslow's Hierarchy of Needs, I wanted to create a similar idea around data science and analytics. Too many times do I see individuals putting the cart before the horse and trying to build a complex predictive analytics pipeline when they don't even know the basics of their customer's usa...

SQL is Not Dead and How to Make it Part of Your Toolbox

Even as SQL draws ever closer to being half a century old, it continues to be relevant for the majority of the business world. One reason for SQL's continued reign as the main data query language is its ease of use. Back when SQL was being built at IBM, their goal was to create a language that mirro...

Microsoft Offers a Faster, More Efficient R, But Is it Right for You?

In early 2015, Microsoft announced its successful acquisition of Revolution Analytics, which made R available as an enterprise ready statistical and data science solution. Initially, Microsoft stated that they would be using this acquisition to seamlessly integrate the power of the R language into M...

February 25, 2016

451 Revisits Treasure Data: What’s Changed This Year

A year ago, analysts at 451 Research took a close look at TD. Now, they’ve come back to see what’s changed. Since the data science space is constantly evolving, analysts are imperative to helping navigate the field......

February 10, 2016

What’s the difference between Amazon Redshift and Aurora?

As you plan your analytics and data architecture on AWS, you may get confused between Redshift and Aurora. Both are advertised to be scalable and performant. Both are supposedly better than incumbents. Both have optically inspired names. So, what's the difference? In short, Redshift is OLAP where...

February 1, 2016

Graduate from Mixpanel: Tracking Your Funnel Long Term

This post is part two of a two part series. Read part one here. As we saw with Part 1, we were able to create a funnel that allowed us to easily change the period of time and events that defined the funnel, and then visualize that funnel using R. However, like most business driven analytics, we w...

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