There are many dashboard solutions out there these days. They all compete on similar features like top visualizations, data connectivity, usability and simple data processing. The simplest form of self-service analytics, however, is not getting much love from these solutions. And what is the simplest form of self-service analytics? Well… pivot tables of course.

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When you are dealing with potentially hundreds or thousands of customers, the rate at which those customers come or leave is a critical KPI. Especially when it comes to certain industries like SaaS companies, customers are coming and going all the time. In fact, the rates at which clients come and go can also change drammatically based on different factors like marketing campaigns, price segmentation, price changes, etc..

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Today we will have a look at how to setup an analytics dashboard with the Elastic suite, specifically at Elasticsearch, Kibana and Logstash.

The Elastic suite is a set of applications made to provide everything needed to build a data analysis system fully integrated with existing software, be it an ecommerce like Salesforce or a custom-made platform, like Django or Rails-based applications.

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Python is a powerful open source (and free) programming tool. You can do and automate everything with Python.

Python is now at a such level of maturity that it is used by businesses of all types (not only the Google types). It is getting increasingly popular in business settings for data science and analytics.

I created a quick challenge that will get you coding and data crunching in Python in under 1 hour, to get started, follow these steps:

• Install Anaconda

• Download a datase on your pc

And then try to answer those questions using Python and the Pandas library on the Anaconda platform:

• Get the top 10 of something (i.e. top 10 regions in terms of sales)
• Count of something (i.e. how many customers?)
• Sum of something
• Max of something (i.e. biggest customer)

You can post your answer and solutions in the comments below and I’ll give you my feedback. If you need help or have questions, also just ask below in the comments.

Bonus exercise. Now cascade this exercise down into a company or team. What additional challenges do you see and why?

Ever read “must be very detailed person” in a CV? Me, all the time. It seems all companies do in finance and analytics is surfing through mountains of detail. Detail is so sexy… employees swear by it.
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Understanding the world of data science and analytics today can be a daunting task. A lot of it is because definitions are not clear. Sometimes data science is defined very broadly, sometimes very narrowly. All this can be very confusing and hard to pin down.

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We first discussed Superset here. The post was about first impressions, potential, pros and cons vs more mature solutions like Tableau, I suggest to read that post too as there is a lot of information and thoughts about Superset there. But I wanted to test this tool a bit more, so I thought why not trying something out with Shopify?

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