We have some interesting reads

In a company, it’s extremely important that everyone works together to contribute to constantly bettering the company and customer experiences. A team of data engineers, data scientists and business analysts can work together to extract meaningful insights from raw data that is collected over time. These insights can provide a company with a competitive advantage and assistance in making informed business decisions. Overtime misconceptions have arisen around the roles that data scientists and data engineers play in industry. In many companies, both roles are thought to be interchangeable, however, they are distinctly different. Let’s have a look at the differences!

Great, you’re sorted with Machine Learning and Artificial Intelligence and you’re good to go, right? Wrong. Now you need to think about data collection, algorithms and all the rest of it! Don’t worry, we’re here to help you out! In machine learning (ML) and artificial intelligence (AI), both of which we have covered in previous blogs, there are two main ways that an algorithm can make observations in a dataset, that being through supervised or unsupervised learning. Today we’ll have a look at what the differences are between the two, as well as which would be better for YOU. Let’s get to it.

As customer service and support becomes more and more important, companies are having to come up with new ideas to keep their customers happy and to respond to their requests or questions as soon and as conveniently as possible. Cue Chatbots – as messaging apps become a popular communication channel, businesses have started using Chatbots to interact with their customers and their employees.

Picture this – a robot or machine that can tidy your clothes cupboard JUST the way you like it, or serve every member of your house their ideal beverage specific to them, how awesome is that?! This would make your life so much easier right? Well, these are products of Artificial intelligence (AI).

Which frontend framework should you be looking at in 2021? With many options and opinions available, today we’re taking a look at the two front runners, two words you may be hearing quite frequently – Angular vs React, whats the verdict? Let’s break it down, compare the two and find out!

SQL – it’s like the database whisperer! Back in the day companies stored their records and documents in file cabinets, for large businesses this meant tons and tons of filing cabinets to accommodate all the documents. Now days we have the luxury of storing data in digital databases, although finding information within these digitised databases is not as simple as opening a filing cabinet drawer. For searching databases, Structured Query Language plays a massively important role!

In our last blog we discussed the importance of BigQuery and BigQuery ML, in order to fully understand the importance and usefulness of this, we need to introduce you to Tensorflow. Tensorflow is an inventive open-source software application, also developed by Google, which can be used to build and train Machine Learning (ML) models. We learned a bit about ML in our BigQuery blog, although now we’ll dig a bit deeper! Tensorflow is innovative in that it can make the process of gathering data, training models, evaluating and refining models, easier and quicker, here’s what you need to know!

Analytics have become such an important aspect in your company’s online success, it’s imperative to know how your company is performing in the digital world, which includes measuring your online success from the total number of users on your website, to how long your users spent on your website and which pages they were most interested in.

In order to have an in-depth understanding of our next blogs about Tensorflow and BigQuery, it’s important to understand the concept of Machine Learning (ML). Machine Learning is a well-known branch of artificial intelligence (AI) that involves deriving meaning from data that is readily available.