Showing posts with label Big Data. Show all posts
Showing posts with label Big Data. Show all posts

Wednesday, August 2, 2017

Tableau - Building Effective Dashboards

When a dashboard is done right, people wonder how they ever lived without it. Why? A well-designed dashboard is a launch point for your analytics. Armed with the same powerful collection of information, your business makes faster decisions based on a single source of truth.
Read this whitepaper to discover how:
  • Thoughtful planning will allow you to become familiar with your dashboard audience, evaluate proper display size, and appropriately plan for fast load times.
  • Informed design draws from the “sweet spot” of visual cues, is critical of view and color quantity, incorporates interactivity to encourage exploration, and considers progressive formatting.
  • Refining your dashboard puts the onus on tooltips, emphasizes the story within your story, eliminates clutter, and sets you up for dashboard testing opportunities.
Read more 

Monday, January 2, 2017

How Big Data Is Being Used to Fight Infectious Disease Threats

We hear all the time about innovative and interesting things that big data can be used for, but it’s rare we actually get to experience it for ourselves. With recent events, however, that may be changing.
Medical information — or big data — extracted from health records, Internet resources, social media and even some other sources is being used to combat infectious diseases and deadly outbreaks. This is extremely important, because in the past, physical information such as laboratory test results and public health records have been the focus. However, there are some disadvantages with using traditional information.
Read more

Wednesday, February 25, 2015

Step by Step guide to learn Time Series Modelling

Hi all, thank's to Tavish Srivastava I finally found a useful post about Time Series.

here is the part 1 intro:
Regression Models, both linear and logistic are an inevitable part of Analytics industry. Take a flashback & recall, when did you built your last Time Series model. Time series models are very useful models when you have serially correlated data. In case you have never built a time series model or you struggle with some concepts of time series models, you have landed at the right page.
and this the part 2 intro:
This is the second part of the step by step guide to Time Series Modelling. In the first part, we looked at basics of time series, stationary series, random walk and Dicky Fuller test. If you have not read this article, I would suggest to go through that first.
In this article we will talk about handling time series data on R. Our scope of this article will be restricted to data exploring in a time series type of dataset and not go to building time series models.  In this article I have used an inbuilt dataset of R called AirPassengers. The dataset consists of monthly totals of international airline passengers, 1949 to 1960. This article will help you explore the data step by step and we will make predictions based on this data for the number of passengers post 1960 in next few articles.
My suggestion read and test it Part 1 & Part 2 

Regards

Thursday, January 15, 2015

My Body, My Life, My Health Data (James M. Connolly)

Interesting post about eHealth Data 
Of all the data that we produce throughout our lives, is there any that would be more important to have on hand in a single place? Let's face it, the time when bones are breaking or chest pains are, as the doctor says, "radiating" isn't the time to try to remember when you had that surgery many years ago or which of several sound-alike drugs you actually are allergic to.


(Image: Flickr)
While it can be difficult for us to remember all of our history and conditions when we are in pain, it's horrifying to have to go through that process when we are trying to answer for an incapacitated child or other loved one.