Do these following questions clog your mind?
I aspire to become a business analytics professional, but I don’t know what skills to possess?
I am sceptical; which training should I opt for in order to establish my career in the sphere of business analytics?
I am looking forward to switch my career into data analytics, but I don’t know which skills to imbibe for better prospects?
Answer: Yes, they do.
Continue reading “Take Small Steps With Big Feet of Business Analytics”
Erik Schmidt, a Senior Scientist at Pandora is going to propose an insight of recommendations and deeper challenges involved with Pandora at the Machine Intelligence Summit. This global tech event will take place in San Francisco on 23rd and 24th of March 2017. Continue reading “Pandora: Blending Music with Machine Learning”
Business Intelligence combined with Big Data analytics is stimulating the progress of Enterprises across the globe, along with pulling whooping amount of investment within the Big Data community. The infographic given below on big data in shaping everyday lives elucidates people about how Big Data is making our lives better.
At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.
In the slides he made, one can see the following:
A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.
One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.
Mr. Ooi also notes that SAS does contain various options for modelling variance like for instance, SAS PROC MIXED, PRIC NLIN. However, none of these are as flexible or powerful as R. The main difference as per Ooi, is that R modelling functions return as object as opposed to returning with a mere textual output. This however, can be later modified and manipulated with to adapt to a new modelling situation and generate summaries, predictions and more. An R programmer can do this manipulation.
Read Also: From dreams to reality: a vision to train the youngsters about big data analytics by the young entrepreneurs:
The primary analysis is done in r programming within 2 minutes usually, in comparison to SAS versions that actually took 4 hours to run, and frequently kept crashing due to lack of disk space. As the data is stored within SAS; SAS code is often used to create the source data…
While an R script can be used to automate the process of writing, the SAS code can do so with much simplicity around the flexible limitations of SAS.
Read Also: Dexlab Analytics' Workshop on Sentiment Analysis of Twitter Data Using R Programming
Mr. Ooi’s R programming code has a neat trick of creating a matrix of R list objects, which is fairly difficult to do with IML’s matrix only data structures.
He also discussed some of the challenges one ma face when trying to deploy open-source R in the commercial organizations, like “who should I yell at if things do now work right”.
And lastly he also discussed a collection of typically useful R resources as well.
For people who work in a bank and need help adopting R in the workflow, may make use of this presentation to get some knowledge about the same. And also feel free to get in touch with our in-house experts in R programming at DexLab Analytics, the premiere R programming training institute in India.
Ref – https://www.r-bloggers.com/how-anz-uses-r-for-credit-risk-analysis/
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“Big data analytics will help bridge India’s tax gap.” Economic Times
“India’s Growing Big Data Future.” NDTV
“The Big News About Big Data.” NASSCOM
“Big data to boost job openings in 2017: Report” Tech Circle
“Big Data for the next green revolution.” Hindu Business Line
Once again, good news is in the air for our very own ‘Big Cats’. The very recent reports on Tiger Census have proudly announced the incredible rise in the number from 1,706 to 2, 226 since 2010, when the counting started.
The previous years have seen the major downfall in the number owing to reasons like poaching, environmental degradation, dwindling habitats and of course man- nature conflict . But in contrast, the combined efforts put forwarded by local communities, conservationists and the Government has resulted in the upliftment, as stated by Marco Lambertini, Director General of WWF International.
Continue reading “Tigers will be safe in the hands of Big Data Analytics”
As per the latest research from strategy analytics, the global smart watch shipments of Apple has grown by 1 percent annually to hit the major record of 8.2 million units in the 4th quarter of the year 2016. The growth of apple watch drove and got dominated with 63 percent in global smart watch share of market and Samsung still continues to hold its second position.
Neil Mawston, the Executive Director at Strategy Analytics stated on the issue by saying – the global shipments have grown by 1 percent annually from the pre-existing 8.1 million units in quarter 4 in 2015 to 8.2 million in quarter 4 in 2016. The market shows a marked growth in the fourth quarter for growth in smart watches industry after the past two consecutive quarters for declining volumes. The smart watch growth is also seen to be recovering ever so slightly due to new product launches from other company giants. Moreover, there is a seasonal demand for these gadgets, and a giant such as Apple is launching stringer demand in the major developed markets in the US and UK. Hence, the international smart watch shipments grew by 1 percent annually; from the previously existing 20.8 million in full-year 2015 to a record high of 21.1 million in 2016.
Continue reading “Apple Watch’s Strategy Analytics, Return to 1% Growth”
Did you know that with Excel you can now automate tasks by writing so called programs macros. In this tutorial, we will learn how do so, by learning to create a simple macro, which will executable after clicking a command button. To begin you must first turn on the developer tab:
Do the following steps to turn the developer tab on:
In this blog post, we will learn how to simulate multiple samples efficiently. In order to keep the discussion, easy we have simulated a single sample with ‘n’ number of observations, and ‘p’ amount of variables. But in order to use the Monte Carlo method to approximate the distribution sampling of statistics, one needs to simulate many specimens with the same regression model.
The data steps in SAS in most blogs have 4 steps mentioned for so. However, to simulate multiple samples, put DO loop around these steps that will generate, the error term and the response variable for very observation made in the model.
Continue reading “How to Simulate Multiple Samples From a Linear Regression Model”