Data is everywhere these days! From smart watches tracking our steps to Netflix recommending shows, our lives are saturated with data. But how much do we really understand about using and interpreting that data? This brings us to the crucial concept of data literacy.
With the massive growth in data, companies today have many options for managing all that information. Getting to grips with the differences between databases, data warehouses, data lakes, and data marts is critical for selecting the right platform. This post will provide an in-depth look at each Data Storage solution.
New to analytics? You’ll likely hear programmers tossing around all kinds of language names. Python, R, SQL, Julia, Scala – it’s enough to make your head spin!
While you don’t need to become an expert coder, knowing what languages are used and why can be helpful. Let’s decode the top options with some basic background and code snippets.
Job titles containing “data” or “analytics” seem to multiply every day. How is a data analyst different than a data scientist or BI developer? , what do they actually do day-to-day? As data becomes more important across industries, these roles remain confusing to many. Let’s explore what six key data jobs
What do all these buzzwords even mean?! Analytics, business intelligence, data science – it’s easy to get confused with terms like business intelligence, machine learning, and big data get thrown around constantly. But if you don’t work directly with data, I bet many of these probably go over your head. I know from experience how confusing and intimidating these data expressions can sound! Well, I’m here to help decode some common data science lingo into plain English so we can all follow the conversation.