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Once the data preparation phase is complete, its time to move on to the fun part of the CRISP-DM framework: modeling. Here you’ll chose which modeling technique to use, create some tests to assess the accuracy of your model, build the model, and then assess the model using the tests you created.

When you start using a Raspberry Pi or BeagleBone Black, it comes with a default username and password. While this makes it easy to log in and get started, it also makes it easy for anyone to log in. We should probably do something about that. Luckily this isn’t very hard to fix, you just need to create a new account and disable the default account.

A key component in data acquisition or reporting is the ability to trigger your script to run at a set time each day. Whether you are attempting to download the latest stock prices or update corporate earnings reports, once you’ve created the script to do the actual work, you need to find a way for it to be run on the correct schedule.

After developing business understanding and data understanding, the next big objective in the CRISP-DM methodology is to prepare the data for modelling and analysis. This involves selecting, cleaning and transforming the data which will be used for the project. While this isn’t flashy work, it typically accounts for 60% to 80% of the effort for a project.

Corporate reporting is a prime candidate for automation if you can clearly explain the process to produce it, and the process remains consistent over time. Automating your reports has many potential benefits, it can save time, reduce errors, and alleviate the boredom caused by performing repetitive tasks.

Power BI is Microsoft’s data exploration and dashboarding tool. While it hasn’t risen to desktop prominence like Excel and Outlook have for the majority of knowledge workers, it is an incredibly capable tool which allows you to quickly visualize data from a number of data sources and explore the data using a graphical interface.

Previously we looked at how you can combine R and Markdown to create reports directly from your R scripts, and also how to send email from R using Microsoft Outlook. In this post, we’ll take these concepts a step further and look at how we can use R to embed images in email messages or even use Markdown to create entire messages.

Robotic process automation (or RPA) is transforming the way many businesses handle their repetitious, labour intensive tasks such as reporting, making basic decisions, and providing services. Using software these tasks can be automated; reducing the time to complete tasks while also improving their accuracy and consistency. If you want to get started down the RPA path without incurring licensing costs, there are free tools you can start using today.

Having developed business understanding and a deep knowledge of the problem you are trying to solve, the next step in the CRISP-DM framework is to develop that same level of understanding around the data itself. This step isn’t analysis, but rather looking at the structure and shape of the data in order to determine what information is available and how to go about building your analysis.

One underappreciated feature in R is the ability to easily create beautiful reports using Markdown. Markdown files contain a combination of code and text, allowing you to write your analysis alongside your code and publish both the analysis document and code in a wide variety of formats with little effort.