Since the start of this year, new development in the field of technology has been the hottest topic of discussion at several science symposiums. This blog post sheds some light on what can be expected for 2017, based on 2016 evolutions in Data Science and Machine Learning.
Continue reading “Top 2016 Trends Expected to Turn Fruitful in 2017”
Data Visualization can be your bow and arrow provided you know the exact way to use it.
In modern day scenario, data visualization has become the crux of efforts – raw data in various forms and statistics tends to be incredibly powerful, but only if you decide to work with them as a whole. After all, it’s not just the numbers but the story behind those numerical figures that reveals something. So, you require data visualization to brush up these notions and turn them into something more compelling to target audience. Data Visualization makes your messages more attractive, lively and enhances the impact, along with keeping your audience hooked.
Continue reading “What to Do and What Not to Do With Data Visualization”
In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.
Help yourself with credit risk analysis course online at DexLab Analytics.
Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.
For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.
While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.
Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:
In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.
Get more insights on credit risk management including articles, research and other hot topics, follow us at DexLab Analytics. We offer excellent credit risk management courses in Delhi. For further queries, call us today!
.
Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.
As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.
Continue reading “Classifying Bank Customer Data Using R? Use K-means Clustering”
Artificial Intelligence projects, applications and platforms are being churned out from every corner of the world. A majority of them now possess the ability to break loose lab life and hit mainstream trends, making an appearance in myriad online tools, open source APIs and mass gadgets.
Though the machines are yet to take over our lives, they are filtrating their way into our lives, influencing day-to-day activities, be it work or entertainment. From personal assistants like Alexa and Siri, to self-driving vehicles powered by predictive modeling and more intense and fundamental machine learning technologies, a wide set of applications of AI are in use of late.
Feed yourself with Machine Learning Using Python technology, only from DexLab Analytics.
We perused through a handful number of AI apps so that we can enlist the ones that are more practical and thus really deserving! Let’s leverage piles of data with these effective applications:
As per Creative Strategies report, 70% of iPhone users have used Siri at least for once or sometimes, but everyone has tried it at least. We are here to tell you don’t hire a personal assistant, instead implement Siri.
This voice-powered virtual assistant makes business operations smoother and hassle-free, while making your workday more productive. The software is activated by voice, and it is at present available in 20 languages.
Developed and powered by Amazon for Amazon Echo intelligent speaker, Alexa, a robust voice service was launched in 2014. It can help you in ordering supplies, translating and controlling office’s vacuum.
However, connecting your Echo to IFTTT may allow you to coordinate with services that aren’t supported originally by the Echo, while allowing you to integrate multiple actions into a single command to the Echo.
This is one of the most popular artificial intelligence applications. Google Now functions by keeping a tab on your calendar, mail, web searches and lot more, along with sending relevant alerts and news on your device as and when detected. It can also carry out tasks, and answer queries, based on voice commands.
The best part of this application is that you don’t have to log in to use it. Just set up alerts that will be sent to the device, and that’s all. At present, it is available in English and is considered a tailing rival of Siri.
If you know the exact way to maneuver it, Cortana would be the most effective AI personal assistant. It can perform all sorts of things, right from dictating and sending emails, tracking flights to searching something on the internet or checking weather forecasts. The more time you spent on it, its functionality gets better and better.
Even, the company is so impressed by its services that it has integrated the service into Power BI, its most intuitive BI tool.
Brain Artificial, aka Braina is self-regulating software, which enables easy hands-free operation in your computer to perform basic tasks by listening to voice based commands in English language.
Braina enjoys a certain edger over its run of the mill competitors as it can precisely work with a variety of accents, which is not so common. The pro version is equipped with a bonus of deep learning – it is programmable as well as observes user behavior over time.
Hope, AI applications serves the humanity well!
Check out some more interesting stuff on Machine Learning at DexLab Analytics. We offer world-class machine learning courses in Delhi for all your data aspirations. Come, explore!
Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.
Machines are going to eat our jobs.
AI is handling insurance claims and basic bookkeeping, maintaining investment portfolios, doing preliminary HR tasks, and performing extensive legal research and lot more. So, do humans stand a chance against the automation apocalypse, where everything, almost everything will be controlled by robots?
What do you think? You might be worried about your future job opportunities and universal basic income, but I would ask you to draw a clearer picture about this competing theory – because, in the end, this question might not even be a plausible and completely valid question. Why, I will tell you now.
Continue reading “Automation Doesn’t Necessarily Make Humans Obsolete, Here’s Why”
Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.
However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:
Continue reading “Top 10 Nifty Tools to Manage Big Marketing Data for Companies”
Analysts at Facebook promptly shut down the Artificial Intelligence system over concerns they might lose control over the system. Recently, Facebook had developed a new Artificial Intelligence program, which could create its own language with the help of code words to make communication easier and effective. The researchers took it offline, when they understood the language used is no longer English.
Though this isn’t the first time that AIs went a step ahead to take a different route instead of the oh-so-regular training in English language to develop their own more productive language, the recent Facebook incident made us wary about Elon Musk’s warnings about AI. “AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk, co-founder, CEO and Product Architect at Tesla once stated at the meet of US National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late,” he further added.
Continue reading “Facebook Shut Down AI amid Fears of Losing Control”
The INTCK and INTNX functions in SAS helps you compute the time between events. This technical blog is based on the timeline of living US presidents, sourced from a Wikipedia table. The table data shows the number of years and days between events.
So, let’s start.
To calculate the interval between two dates, you can use these two SAS functions:
The INTCK function returns the number of time units between dates. The time unit can be selected in years, months, weeks, days, or whatever you feel like.
The INTNX function helps you compute the date that is 308 days away in the future from a specific date. This was just an example to help you understand what it means. The INTNX function returns a SAS date that is particular number of time units away from a particular date.
These two functions share a complimentary bond: where one calculates the difference between two dates, the other entitles you to add time units to a specified date value. Also, the INT part in both the functions denotes INTervals, and the terms INTCK and INTNX means Interval Check and Interval Next, respectively.
DexLab Analytics offers intensive SAS certification courses for candidates..
These two prime functions tend to be useful in counting the number of anniversaries between two dates along with calculating a future anniversary date. Use the ‘CONTINUOUS’ option for the INTCK function and the ‘SAME’ option for the INTNX function in the following manner:
The ‘CONTINUOUS’ option in the INTCK function helps you count the number of anniversaries of one date that occur before a second date. For example, the statement
Years = intck('year', '30APR1789'd, '04MAR1797'd, 'continuous');returns the value 7 because there are 7 full years (anniversaries of 30APR) between those two dates. Without the ‘CONTINUOUS’ option, the function returns 8 as 01JAN occurs 8 times between those dates.
The statement
Anniv = intnx('year', '30APR1789'd, 7, 'same');returns the 7th anniversary of the date 30APR1789. In some ways, it returns the date value for 30APR1796.
The most exciting part about these two functions is that they automatically handle leap years! Yes, you read that right. If you ask for the number of days within two dates, the INTCK function will show leap days in the result. If an event takes place on a leap day, and you ask the INTNX function to reveal the anniversary date, it will report 28FEB of the next year to the next anniversary date.
Go through the following algorithm to calculate the number of years and days between dates in SAS:
Here are the data steps that enable you to compute the time interval in years and days between the first few US presidential inaugurations and deaths.
data YearDays; format Date prevDate anniv Date9.; input @1 Date anydtdte12. @13 Event $26.; prevDate = lag(Date); if _N_=1 then do; /* when _N_=1, lag(Date)=. */ Years=.; Days=.; return; /* set years & days, go to next obs */ end; Years = intck('year', prevDate, Date, 'continuous'); /* num complete years */ Anniv = intnx('year', prevDate, Years, 'same'); /* most recent anniv */ Days = intck('day', anniv, Date); /* days since anniv */ datalines; Apr 30, 1789 Washington Inaug Mar 4, 1797 J Adams Inaug Dec 14, 1799 Washington Death Mar 4, 1801 Jefferson Inaug Mar 4, 1809 Madison Inaug Mar 4, 1817 Monroe Inaug Mar 4, 1825 JQ Adams Inaug Jul 4, 1826 Jefferson Death Jul 4, 1826 J Adams Death run; proc print data=YearDays; var Event prevDate Date Anniv Years Days; run; |
In a nutshell, the INTCK and INTNX functions are consequential for calculating intervals between dates. In this blog, I discussed about two-less-popular options inn SAS, for more such SAS training related blogs, follow us at DexLab Analytics.
Interested in a career in Data Analyst?
To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.