The Keys to Effective Data Science Projects – Part 7: Create and Train the Model

We’re in part seven on our series of the Keys to Effective Data Science Projects.  This is the section that most people think of when they think of "Data Science". It's where we take the question, the source data which has been turned into the proper Features (and potentially Labels), and select an algorithm or two …

The Keys to Effective Data Science Projects – Part 6: Feature Selection

We're in part six on our series of the Keys to Effective Data Science Projects. I won't cover basic Feature Engineering in this article - it's a huge topic and central to working in Machine Learning areas. I do recommend you check out as many articles as you can find on the subject, and once …

The Keys to Effective Data Science Projects – Part 5: Update the Data

In this series on the “Keys to Effective Data Science Projects”, we've seen a process we can use, we've determined what we want to know, and we've ingested the data. In the last step we explored the data, and in a different way than we might be used to when working with in a database …

The Keys to Effective Data Science Projects – Part 4: Explore the Data

We’re in a series on the “Keys to Effective Data Science Projects”. We've identified the question we want to solve, and made a preliminary pass at the data we need to answer that question. Next we brought in that data to a central location we can work with. We now want to explore that data. …

The Keys to Effective Data Science Projects – Part 3: Import Initial Data

We're in a series on the "Keys to Effective Data Science Projects" - and we've completed the hardest part - identifying the problem(s) we want to solve. Note that each problem gets it's own project - you're not going to predict when a hurricane will hit and how much money you'll get in your bonus …

The Keys to Effective Data Science Projects – Part 2: The Question

In a previous post I explained where to start in a Data Science Project. I've also given you a Project Plan that shows all the steps you need to help your organization with a Data Science objective. The first step in that Project Plan is "Business Understanding", and the first step in that process is …

Simplifying (?) Artificial Intelligence

Artificial Intelligence is a vastly complicated topic. It's not just that it has a lot of components (because it does) it's because it isn't really a single topic. It's a surprisingly old field of study that has morphed and changed over the years. I worked with "AI Systems" in the 1970's - and even then …

What is the main difference between Business Intelligence and Data Science?

I've explained in other articles that Data Science is not a replacement for other data technologies, it's a continuation of the data analytics process. So if Data Science is used in analytics of large sets of data, how does it differ from Business Intelligence (BI), which is also used to analyze large sets of data? …

Build the House Around the Plumbing

There's a show on US television called "This Old House", where a team of craftsmen fix up older homes - like VERY old (at least old in the US sense). In one episode, an entire quarter of the show time was devoted to the amazing lengths the team went through to put in updated plumbing …

Data Science: Start at the very Beginning, It’s a very good place to start

I have found that Data Science projects often deal with two types of clients: One of whom understands the Data Science Process and has a good grasp of what it can do, and the other who wants to add a dash of Machine Learning (ML) and/or Artificial Intelligence (AI) on top of Business Intelligence to …