Data Science

A kid’s guide to data science – clustering

Bedtimes are the Worst

Having a bedtime is tough when you’re a kid. I get it. When the YouTube video you’re watching gets cut short because you have to get ready for bed, it’s the worst.

But like most kids, occasionally you get to stay up past your bedtime. Have you ever wondered why adults let kids stay up past their bedtimes? Think about it. Every so often, you get to stay up late.

Why? What’s different about those days? Maybe you should keep track of your bedtimes to see if you can discover the magic formula to staying up late!

Tracking Your Bedtimes

Tracking your bedtimes is easy. Let’s make a list of things that might help you solve this puzzle.

  • The date. It’s good to know which days you go to bed on time versus days you stay up late.
  • Your age. As you get older, you’re allowed to stay up later. That’s a good thing!
  • Is it a school night? You almost never stay up late on a school night.
  • Are you home? 
  • Is it a sleepover? Sleepovers are the best!
  • Are you sick?
  • Your bedtime. (Boo, Hiss!)
  • The actual time you went to bed that day. 

Look at this in action.

  • January 14, 2016 (The date)
  • 12 (Your age)
  • Yes (Is it a school night?)
  • Yes (Are you home?)
  • No (Is it a sleepover?)
  • No (Are you sick?)
  • 8:30pm (Your bedtime.)
  • 8:30pm (Actual time you went to bed.)

Great! You’ve got your first bedtime tracked! You’re well on your way.

Super Bedtime Tracking

Now let’s change the way we’re tracking your bedtimes to make it easier to track lots of days.

Let’s take a look at what 3 days looks like.

Screen Shot 2016-01-14 at 10.08.09 PM


Now imagine tracking bedtimes for a whole year—all 365 days. That’s a lot of data!

Clustering the Data

If you had a year’s worth of bedtimes tracked, you could begin to look for patterns. Are certain days better than others for staying up late? Clustering, aka grouping, the data allows you to observe meaningful patterns. Do you see any interesting patterns in the table below? It looks different from the table above since we consolidated the data into 4 groups.

Screen Shot 2016-01-25 at 5.16.46 PM

Group 4 is a winner! Nothing exciting here though—you already know that you get to stay up late during weekend sleepovers.

Screen Shot 2016-01-25 at 5.16.14 PM

Groups 1 and 3 are pretty boring. No staying up late on school nights. Boo!

Group 2 is interesting. Friday is not a school night, but being sick means going to bed early.

What did we learn?

If you want to stay up late more often…

  1. Have more sleepovers!
  2. Don’t get sick! The easiest way to avoid colds is to wash your hands!
Big Data, Data Science

Using Prescriptive Analytics to Make Better Decisions

Business Analytics is broken down into three distinct phases.

  1. Descriptive – What happened? This phase involves traditional BI tools to help organizations process and report on historical data. Trends are analyzed and decisions are made. The majority of management reporting uses this approach.
  2. Predictive – What will happen? This phase uses machine learning algorithms to build models from historical data and then uses those same models to predict a future outcome or its likelihood.
  3. Prescriptive – What action should be taken? This phase prescribes actions to achieve the best possible outcome based on the predictions made. Actions that lead to the highest chance of success are prescribed.

Prescriptive Analytics predicts and compares the likely outcomes of any number of actions, and then chooses the very best action to help advance an organization’s objectives.

Consider implications for the healthcare industry. Healthcare predictions are most useful when that knowledge prescribes clinical action for each predicted outcome.

Similar insights can help organizations improve decision making and have more control of business outcomes. Prescriptive analytics is an important next step on the path to insight-based actions and recommendations.

Data Science

3 free data tools you never knew you were missing

The right tools can make a world of difference. If you work with data, here are three tools to add to your toolbox.

1. Data Preprocessing

KNIME is an open source data analytics and integration platform. The interface allows you to assemble workflow nodes for data preprocessing (ETL) and data analysis. Modeling and data visualization nodes are also available, but I use other tools for those. Screen Shot 2015-12-18 at 5.01.46 PM

Need to create a monster Pivot Table? The Pivoting node can handle very large files with ease. I used a dataset in comma separated format (csv) and a simple KNIME workflow to create a pivot table with over 100,000 columns.

Screen Shot 2015-12-18 at 4.49.32 PM

Download KNIME at

2. Data Mining

Screen Shot 2015-12-18 at 5.06.25 PMWeka is a collection of machine learning algorithms that help you complete data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka also contains tools for data preprocessing, but can also manage classification, regression, clustering, association rules, and visualization.

Weka has a large online community and lots of support. The interface is easy to use.

Screen Shot 2015-12-18 at 5.10.46 PM

Weka also provides some great visualizations of your dataset.

Screen Shot 2015-12-18 at 5.11.12 PM

Download Weka here.

Here are a few sample datasets to get you started.

3. Data Visualization

Screen Shot 2015-12-18 at 5.21.26 PMTableau Public is a free tool to create interactive data stories on the web. It’s available as a service so you can be up and running as soon as you download it.  Connect, create, and publish interactive data visualizations  directly to your website. No coding required!

Tableau even provides How-to Videos and sample datasets.

Download Tableau Public here.


Data Science

6 steps to data mining awesomeness

Have a data mining project on the horizon?  These 6 steps make up the Cross Industry Standard Process for Data Mining (CRISP-DM) and will help make it awesome!  datamining

  1. Gain an understanding of the business problem you are trying to solve. Are the business requirements well defined?
  2. Get to know the data. What data is available? Is it complete? What data is needed?  Now is also a good time to identify any data quality problems. 
  3. Prepare the data. Data is rarely clean or in the right format for your modeling tools. This step can be time consuming.   
  4. Create your model(s).  – Pick your modeling tool and build your model – Linear Regression, Classification, Clustering. Several techniques can be used to solve the same data mining problem. Now might also be a good time to revisit Step 3 if the data isn’t quite right. 
  5. Evaluate your results.  Are the results meaningful? Do they solve the problem you identified in Step 1?  Ultimately, a decision on the use of the results should be made.
  6. Deploy your model!  How should the model be deployed? What steps should be taken to maximize the benefit of the model and results?

That’s it!

Do you use a different process? I’d love to hear about it. Please leave a comment.