Python is a programming language with virtually limitless functionalities and one of the best languages for working with data. Jupyter Notebooks, on the other hand, is the most popular tool for running and sharing both your Python code and data analysis.
Putting Python and Notebooks together with Google Analytics, the most popular and a really powerful tool for tracking websites, gives you almost like a superpower for doing your analysis.
If you’ve seen “The Matrix”, then you may recall characters developing the ability to essentially see the virtual reality in the code streaming down their screens. Sadly, outside of fiction, humanity isn’t all that great at interpreting vast swathes of raw data. In a time of granular analytics, that’s why data visualization has become so important.
Data visualization allows marketers to physically see and communicate with their target audience. Marketers typically have all the same information via text, but turning it into a visual aid can allow companies to make business decisions much faster. Simple aids like pie charts or line graphs can take thousands of pieces of information and compress them all down into something legible.
Creating these visuals on your own can take more time than reading through the slog of information. The good news is that technology has created data visualization tools for marketers to easily pour their data into and create the visuals for them.
There is a wide variety of Javascript data visualization (charting) libraries available for free. One problem this (good) situation has caused is that it can be rather difficult to pick the right one for your project. And, trust me, switching the library after it has grown to be part of your project is a real pain in the butt.
If you’ve ever worked with Google Analytics API, you are probably familiar with the Query Explorer. What many users don’t know about is that Google also has a similar tool called Request Composer. The main difference between the two is that while Query Explorer is built on top of Reporting API v3 the Request Composer […]
Apache Superset is a modern, enterprise-ready business intelligence web application that makes it easy to visualise large datasets and build complex dashboards. At Reflective Data, we are using Apache Superset to monitor all data going through our platform with minimum latency. This allows us to easily combine data from different databases and every analyst can […]
You’ve probably heard of Google Data Studio, a powerful and flexible tool that lets you visualize data from various sources. One of the most popular use cases, in my opinion, has been visualizing data from Google Analytics.
Recently, Google made it even easier to get started with connecting the two, Google Analytics and Data Studio, by giving us three nice-looking yet insightful reports. Even better, you can customize the reports in any way you wish.
At Reflective Data, we are all advocates for data-driven business. Not only does it make things easier and more logical, being data-driven in your business decisions will also save you time and make tons of more money!
Every analyst likes awesome charts. A good chart is nice to look at, easy to understand, and creating one shouldn’t be a rocket science.
There are hundreds of great tools and frameworks that allow you to create beautiful charts. Having tried tens of them, I finally chose Google Charts as my go-to tool.
