Internet enabled computers to be connected with each other.
Internet enabled Mobile Devices to be connected with each other.
Now, Internet will be used to enable physical things to be connected with each other. This is what is called “Internet of things” (IoT).
So what happens?
since more devices are connected with internet – we will able to generate more data! This is usually good if there’s a business vision around how to make sense of data to increase efficiency of all these things.
Here’s a nice case study from Microsoft (focus on the business case – the things in this case is “elevator” to drive reliability)
This is all good news for data professionals! There will be increased demand for professionals who can help businesses make sense of data generated via IoT.
Also beware of the “hype” around this technology. It’s important to take incremental steps to achieve the vision – Instead of trying to analyze data from ALL devices in your organization, start with one physical thing that matter the most for your organization or start with data that you have and take incremental steps to spread data culture in your organization!
Now that Big Data has become a mainstream word in IT and business, we have a new buzzword to learn/talk about IoT – but remember it’s all about making sense of data and your skills would be more valuable than ever!
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider’s perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning.
Mark is a consultant who provides enterprise data science analytics advice and solutions. He uses Microsoft Azure Machine Learning, Microsoft SQL Server Data Mining, SAS, SPSS, R, and Hadoop (among other tools). He works with Microsoft Business Intelligence (SSAS, SSIS, SSRS, SharePoint, Power BI, .NET). He is a SQL Server MVP and has a research doctorate (PhD) from Georgia Tech.
Hope to see you there!
Business Analytics Virtual Chapter’s Co-Leader
Classification algorithms are commonly used to build predictive models. Here’s what they do (simplified!):
Now, here’s the difference between Multi Class and Two Class:
if your Test Data needs to be classified into two classes then you use a two-class classification model.
1. Is it going to Rain today? YES or NO
2. Will the buyer renew his soon-to-expire subscription? YES or NO
3. What is the sentiment of this text? Positive OR Negative
As you can see from above examples the test data needs to be classified in two classes.
Now, look at example #3 – What is the sentiment of the text? What if you also want an additional class called “neutral” – so now there are three classes and we’ll need to use a multi-class classification model. So, If your test data needs to be classified into more than two classes then you use a multi-class classification model.
1. Sentiment analysis of customer reviews? Positive, Negative, Neutral
2. What is the weather prediction for today? Sunny, Cloudy, Rainy, Snow
I hope the examples helped, so next time you have to choose between multi class and two class classification models, ask yourself – does the problem ask you to predict two classes or more? based on that, you’ll need to pick your model.
Example: Azure Machine Learning (AzureML) studio’s classifier list:
I hope this helps!
Need to understand the patterns in Quality test results data across all plants.
- The solution involved creating a Business Intelligence system that gathered data from multiple plants. I was involved in mentoring IT team, development and end-user training of a Business Intelligence Dashboard that used SQL server analysis services as it’s data source.
- Dashboard development involved multiple checkpoint meetings with business leaders since this was the first time they had a chance to visualize quality test results data consolidated from multiple plants. Since they were new to data visualization, I used to prepare in advance and create 3-4 relevant visualization templates to kick off meetings.
(it is intended to look generic since I can’t discuss details. Also, drill down capabilities had been added to the dashboard to go down to the lowest granularity if needed)
To test my Tableau knowledge, I attempted the Tableau product certification and got the “Tableau Desktop 8 Qualified Associate” certificate.
Take a look at the following chart, do you see any issues with it?
Notice that the month values are shown as “distinct” values instead of shown as a “continuous” values and it misleads the person looking at the chart. Agree? Great! You already know based on your instincts what continuous and discrete values are, it’s just that we will need to label what you already know.
In the example used above, the “Date & Time” shown as a “Sales Date” is a continuous value since you can’t never say the “Exact” time that the event occurred…1/1/2008 22 hours, 15 minutes, 7 seconds, 5 milliseconds…and it goes on…it’s continuous.
But let’s say you wanted to see Number of Units Sold Vs Product Name. now that’s countable, isn’t it? You can say that we sold 150 units of Product X and 250 units of product Y. In this case, Units sold becomes discrete value.
The chart shown above was treating Sales Date as discrete values and hence causing confusion…let’s fix it since now you the difference between continuous and discrete variables:
To develop effective data visualizations, it’s important to understand the data types of your data. In this post, you saw the difference between continuous and discrete variables and their importance in data visualization.