Popular Posts - Treasure Data Blog - Part 58

Popular Posts

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......

Using Analytics to Uncover the Best Path to Monetization

At some point, every successful app needs to turn a profit - no two ways about it. Those millions of daily active users don't mean jack to your investors until you find something they'll actually pay for. And, when it comes to mucking around with your user experience looking for paydirt, good analyt...

AdWords Reports: An Awfully Partial View

Which AdWords campaign has the best return on ad spend (ROAS)? Every digital marketer has been asked this question at least once, if not every week. You would think such a simple question could be answered with an AdWords report. But as you dig deeper into your data and product, you realize that ...

Capture your Kinesis Stream Analytics Events in Treasure Data

With data-driven companies like Coinbase, Dash and Supercell all getting on board with Amazon Kinesis for real-time delivery of in-app data, handling billions of events per day, we can imagine why you too might...

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......

Wrangling Huge .CSVs Made Simple (with a little help from SQL)

Let's say you have a very large .csv (containing more than 80k rows) that you want to analyze and visualize. Usually, given only Excel and Tableau (or Looker, Chartio, etc.), you would have one of two options: Put it into Excel and run pivot tables, charts, and whatnot, OR Ingest the data di...

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...

Importing and Exporting Google Spreadsheets with Treasure Data

Despite various business intelligence vendors’ campaigns against Excel and similar programs, spreadsheets continue to be the most popular business intelligence tool. In fact, they are more than a BI tool: Bec...

Opening up the Great Wide Open, Atlanta, March 16-17, 2016

It's not often the case that you get to combine both a cutting-edge technology conference with a market that, in many ways, is only just waking up to the possibilities of open source analytics. But that's exactly what the Great Wide Open Conference (GWOC) is and I'm glad I got to attend this year!...

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...

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