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What Makes Tableau 10 So Desirable Among the IT Nerds

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What Makes Tableau 10 So Desirable Among the IT Nerds

Your data just got better, say thanks to Tableau 10. The big data geeks have worked on both the beauty and brains of Tableau, to make the analyses easier, faster and exceptionally delightful. Tableau 10 features an exciting new look and feel, loaded with cute fonts and beautiful colours to make your viz sparkle.

We are thrilled. We have been waiting for days to gauge how some of the Tableau 10 new features will synchronise with our conventional business interface for Hadoop. We have culled out some of the best features of Tableau 10, which had made us zealous and left us buzzing. And here they are ..

An extremely visually appealing new interface

Connect the little dots to get a bigger picture. The new interface pays enough attention to minute details making the entire visualization environment pleasant and engaging.

For example: When you name a worksheet tab, the same name pops up as an inline title, blending seamlessly with the viz – now isn’t that great!

atscale1

Ten on ten to Tableau’s clustering feature

Owing to Tableau’s outstanding analytics capabilities, Tableau 10’s clustering feature is the next best thing. The feature focuses on the elements of a pool of data values and visually highlights an inconspicuous number of clusters, which is found in the data. This makes it easier for an analyst to run his eyes through the data sets they need to investigate.

Spotting the zip codes was never so easy before, come and take a look at the following video.

Tableau 10’s highlighting feature helps in finding a needle from the big data haystack

While developing interesting data visualizations, we often need to present the most comprehensive view of the data, so that the consumers get the right information they are looking for, without foraging here and there. With Tableau 10’s highlighting feature, this goal is easily achieved.

In the video shared below, we can see the weekly trend of sales and numbers of customer across the states included in the sales transaction data.

Multisource data integration and blending

By using Tableau 10, the users will be able to directly connect to data stored in Google Sheets.

Scroll down:

atscale2 (1)

Develop visual representation of the most-favoured customers, based on estimated lifetime value.

005

Once the new data source is created, you can now use the data-relationships feature to let Tableau know that the Last Name column from the Google Sheets data set is mapped to the Customer Dimension column from my AtScale big data virtual cube.

004

Soon after the above relation has been accomplished, a filter is initiated on my Purchase Trends by Ranked Customers dashboard. Watch the video, to get a clearer picture.

These are some of the slivers of what’s new in Tableau 10. Want even more? Join DexLab Analytics for intensive Tableau Certification Training. This premier data science training institute excels in data analytics certification courses. Get interesting data knowledge and Tableau insights from the veterans!

 

Sources: tableau.com
 

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Data Science – then and now!

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Data Science – then and now!

  • Data Science = Statistics + Computer Science
  • emerges as a designation for stores of big data

The following timeline traces the evolution of the term “Data Science”, along with its use, attempts to define it, and related terms:

 

“The future of Data Analyses “- by John W.Turkey, 1962

 

  • More emphasis was placed on using data to suggest hypotheses to test
  • Exploratory Data Analysis and Confirmatory Data Analysis works in parallel

 

“Book on Survey – Contemporary data processing methods “– by Peter Naur, 1974

 

    • Data is a representation of the facts or ideas in a formalized manner
    • It is capable of being communicated or manipulated by some process
    • The rise of “Datalogy”, the science of data and data processes and its place in education
    • Data Science here defined as – the science of dealing with data, once established and the relation of data being delegated to the other fields and sciences.

 
1
 

“The International Association for Statistical Computing (IASC)”- Section of ISI, 1977

 

  • The mission is to link traditional statistical methodology, modern computer technology and the knowledge of domain experts in order to convert data into information and knowledge

 

Gregory Piatetsky-Shapiro, 1989

 

  • Arrival of Knowledge Discovery in Databases (KDD) workshop
  • It became the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) in 1995

 

“Database Marketing” – cover story by BusinessWeek, 1994

 

  • Companies collect mountains of information about you
  • Then crunch it to predict how likely you are to buy a product
  • Implement the knowledge to craft a marketing message precisely calibrated to get you to do so
  • Many companies were too overwhelmed by the sheer quantity of data to do anything useful with the information
  • However, many companies believe they have no choice but to brave the database-marketing frontier

 

“Members of the International Federation of Classification Societies (IFCS)”, 1996

 

  • Data science is included in the title of the conference (“Data science, classification, and related methods”)

 

“From Data Mining to Knowledge Discovery in Databases” by – Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth,1996

 

  • Historically, the notion of finding useful patterns in data has been given a variety of names,
  • Some of the names are data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing
  • KDD [Knowledge Discovery in Databases] refers to the overall process of discovering useful knowledge from data, and
  • Data mining refers to a particular step in this process
  • Data mining is the application of specific algorithms for extracting patterns from data
  • Data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data

 

H. C. Carver Chair in Statistics at the University of Michigan -Professor C. F. Jeff Wu, 1997

 

  • Asked statistics to be renamed as data science, and statisticians to be renamed data scientists

 

The journal Data Mining and Knowledge Discovery, 1997

 

  • “Data mining” designates as – “extracting information from large databases.”

 

“Mining Data for Nuggets of Knowledge” – Jacob Zahavi quoted – 1997

 

  • Conventional statistical methods work well with small data sets
  • Today’s databases, however, involves millions of rows and scores of columns of data
  • Scalability is a huge issue in data mining
  • Another technical challenge is developing models that can do a better job analysing data, detecting non-linear relationships and interaction between elements
  • Special data mining tools may have to be developed to address web-site decisions

 

Also read: The Beginners’ Guide to Data Science Jargon

 

“Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” – by William S. Cleveland, 2001

 

  • Plan to enlarge the major areas of technical work of the field of statistics
  • The benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited
  • A merger of knowledge bases would produce a powerful force for innovation
  • The statisticians should look to computing for knowledge today just as data science looked to mathematics in the past
  • The departments of data science should contain faculty members who devote their careers to advances in computing with data and who form partnership with computer scientists

 

“Statistical Modeling: The Two Cultures” (PDF) – by Leo Breiman, 2001

 

  • Two cultures in the use of statistical modeling to reach conclusions from data
  • One assumes that the data are generated by a given stochastic data model, while the other uses algorithmic models and treats the data mechanism as unknown
  • Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics
  • It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets.
  • If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools

 

Launch of Journal of Data Science, 2003

 

  • Data Science means almost everything that has something to do with data: Collecting, analyzing, modeling
  • The most important part is its applications–all sorts of applications

 

“Competing on Analytics,” a Babson College Working Knowledge Research Center report “- by Thomas H. Davenport, Don Cohen, and Al Jacobson, 2005

 

  • The emergence of a new form of competition based on the extensive use of analytics, data, and fact-based decision making
  • Beside competing on traditional factors, companies starts to employ statistical and quantitative analysis and predictive modeling as primary elements of competition

 

The National Science Board publishes “Long-lived Digital Data Collections – 2005

 

  • Data scientists are – “the information and computer scientists, database and software engineers and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.”
  • In simple terms, they are the people who work where the research is carried out–or, in the case of data centre personnel, in close collaboration with the creators of the data–and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in data base technology

 

Also read: Secrets To Clinch Victory in Global Data Science Competitions

 

Harnessing the Power of Digital Data for Science and Society, 2009

 

  • The nation needs to identify and promote the emergence of new disciplines and specialist’s expert in addressing the complex and dynamic challenges of digital preservation, sustained access, reuse and repurposing of data
  • Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science
  • These individuals are key to the current and future success of the scientific enterprise
  • However, these individuals often receive little recognition for their contributions and have limited career paths.

 

“Google’s Chief Economist, tells the McKinsey Quarterly”- Hal Varian, 2009

 

  • Quote – “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”
  • The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—are going to be the most important skills in the coming decades
  • Managers need to be able to access and understand the data themselves.

 

“The Revolution in Astronomy Education: Data Science for the Masses “- Kirk D. Borne, 2009

 

  • Understanding the data is crucial for the success of sciences, communities, projects, agencies, businesses, and economies
  • It is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce)
  • specialists must learn and apply new data science research techniques
  • Non-specialists require information literacy skills

 

“Rise of the Data Scientist”- Nathan Yau, 2009

 

  • As quoted, “the next sexy job in the next 10 years would be statisticians.”
  • By statisticians, he actually meant a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts
  • Ben Fry argues for an entirely new field, which will combine the skills and talents from disjointed areas of expertise… [Computer science; mathematics, statistics, and data mining; graphic design and human-computer interaction].

 

Also read: How is data science helping NFL players win Super bowl?!

 

Troy Sadkowsky, 2009

 

  • Created the data scientists group on LinkedIn, complementing his website, datasceintists.com (which later became datascientists.net)

 

”Data, Data Everywhere“- The Economist Special Report – Kenneth Cukier, 2009

 

  • A new kind of professionals has emerged – the data scientists, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data

 

“What is Data Science?”- Mike Loukides, 2010

 

  • Data scientists combine entrepreneurship with patience, along with the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution
  • They are inherently interdisciplinary
  • They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions
  • They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here’s a lot of data, what can you make from it?’

 

Also read: What Sets Apart Data Science from Big Data and Data Analytics

 

“A Taxonomy of Data Science” – Hilary Mason and Chris Wiggins – 2010

 

  • Data scientist, in roughly chronological order: Obtain, Scrub, Explore, Model, and Interpret
  • Data science is clearly a blend of the hackers’ arts
  • Statistics and Machine learning and the expertise in mathematics and the domain of the data for the analysis to be interpretable
  • Requires creative decisions and open-mindedness in a scientific context

 

“The Data Science Venn Diagram”- Drew Conway, 2010

 

  • Simply enumerating texts and tutorials does not untangle the knots
  • Data Science Venn Diagram – hacking skills, math and stats knowledge, and substantive expertiseData_Science

 

“Why the term ‘data science’ is flawed but useful “- Pete Warden, 2011

 

  • The people tend to work beyond the narrow specialties that dominate the corporate and institutional world, handling everything from finding the data, processing it at scale, visualizing it and writing it up as a story
  • They also seem to start by looking at what the data can tell them, and then pick interesting threads to follow rather than the traditional scientist’s approach of choosing the problem first and then finding data to shed light on it

 

“Data Science’:  What’s in a name?”- David Smith, 2011

 

  • Many companies are now hiring ‘data scientists’, and the entire branch of study is run under the name of ‘data science’
  • Yet some have resisted the change from the more traditional terms like ‘statistician’ or ‘quant’ or ‘data analyst’
  • However, unabashedly ‘Data Science’ better describes what we actually do, which is a combination of computer hacking, data analysis, and problem solving

 

“The Art of Data Science” – Matthew J. Graham, 2011

 

  • To flourish in the new data-intensive environment of 21st century, we need to evolve new skills
  • We need to understand what rules [data] obey, how it is symbolized and communicated, and what its relationship to physical space and time is.

 

“Data Science, Moore’s Law, and Moneyball” – Harlan Harris, 2011

 

  • Data Scientist runs the gamut from data collection and munging, through an application of statistics, machine learning and related techniques for interpretation, communication, and visualization of the results
  • Data Science is defined by its practitioners, as a career path rather than a category of activities
  • People who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense.Data-Science-Teams

 

“Building Data Science Teams”- D.J. Patil, 2011

 

  • Jeff Hammerbacher shared the experiences of building the data and analytics groups at Facebook and LinkedIn
  • He realized that as their organizations grew, they need to figure out what to call the people on their teams
  • ‘Business analyst’ seemed too limiting
  • ‘Data analyst’ was a contender, but they felt that title might limit what people could do. After all, many of the people on their teams had deep engineering expertise
  • ‘Research scientist’ was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM
  • However, they felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams
  • Instead, the focus of the teams was to work on data applications that would have an immediate and massive impact on the business
  • The term that seemed to fit best was data scientist: those who use both data and science to create something new

 

“Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review – Tom Davenport and D.J. Patil, 2012

 

Join DexLab Analytics for intensive Online Data Science Certification Gurgaon. A top-notch data science online learning institute, DexLab Analytics feel honoured to host a wide array of training sessions, both online and in-class for data aspirants.

 

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How Predictive Analysis Could Have Saved the World from Ransomware

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How Predictive Analysis Could Have Saved the World from Ransomware
 

Kudos to you, if you have stayed offline for the last couple of days, so you could actually spend the weekend well with your family and loved ones. The world is reeling under the shattering news surrounding WannaCry Ransomware this weekend. The situation was worse on Monday, after the offices opened. Going by the figures, revealed out on Monday evening by Elliptic, a Bitcoin forensics firm, which is keeping a watch overall – $57,282.23 in ransom has been shelled out to the hackers of Ransomware malware attack, who took over hundreds and thousands of computers worldwide on Friday and through the weekend.

Continue reading “How Predictive Analysis Could Have Saved the World from Ransomware”

After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

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After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

Two decades ago, if someone asked me to write a computer program that played tic-tac-toe, I would have failed horribly. Now being an accomplished computer programmer, I know the desirable tricks to solve tic-tac-toe with the help of “Minimax Algorithm”, and what it takes is just about an hour to jot down the program. No doubt, my coding skills have evolved over the period of time, but also computer science technology has reached unattainable heights.

Computers paved the ways for a startled innovation. When in 1997, IBM introduced a chess-playing computer, known as Deep Blue, which eventually beat world-renowned Grandmaster Garry Kasparov in a six-game match, people remained in awe for years. Following the trend, in 2016, Google’s London-based AI Company, DeepMind launched AlphaGo – and it mastered over the ancient board game Go. Computers have outplayed the best human players in the games of chess, draughts and backgammon, now it’s time for Go.

Also read: Infographic: How Big Data Analytics Can Help To Boost Company Sales?

The technology goes on thriving, beating humans at games. In late May, AlphaGo is all set to take on its human rival Ke Jie, the best player in the world during the Future of Go Summit in Wuzhen, China. Games, which solely relied on human intelligence, wit, intuition, discern is now excelled by the AI, which is powered by improved engineering and computer superiority.

Also read: Top Databases of 2017 to Watch Out For

Don’t you think this is great! Where AI is driving our cars, looking for ways to cure deadly cancer and helping us in everyday work, winning at Go takes AI a step ahead. It not only makes the games more fun and exciting, but endlessly enjoyable.

The strategy explained

In the eastern part of the world, notably in China, Japan and South Korea, Go is extremely popular and many celebrities indulge in it. The game developers showed interest for long in the complexity of this game. However, the rules are simple – the main objective is to secure the maximum territories by placing and capturing black and white stones on a 19×19 grid.

Also read: Shadowing a Data Architect for a Day!

Chess is less complicated than Go; in the latter, the chances of recognising wins and losses is relatively tougher, as stones possess equal values, and ensures understated impacts throughout the board. To play Go, AlphaGo program implemented deep learning in neural networks – a brain-stimulated program. The connections formed here runs in-between layers of simulated neurons, further strengthened by examples and experiences. Firstly, it analysed 30 million positions from expert games, while gaining abstract information about the state of play from the board data, just like other programmes that classify images from pixels. After all this, finally it played against itself over 50 computers to improve its performance, with each iteration and this came to be known as reinforcement learning.

Go-02 (1)

The round of applause

“AlphaGo plays in a human way”, says Fan – DeepMind’s program AlphaGo beat Fan Hui, the European Go champion. He further added, “If no one told me, maybe I would think the player was a little strange, but a very strong player, a real person.” “The program seems to have developed a conservative (rather than aggressive) style”, adds Toby Manning, a veteran Go player and a referee.

You can now get a superior quality Data Science Certification from the experts in Delhi and Gurgaon. Tune into DexLab Analytics for regular updates on business analytics certification.

 

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Trends to Watch Out – Global Self-service Business Intelligence (BI) Market 2017

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Gartner says – By 2020, the global BI and Analytics market is expected to flourish to USD 22.8 billion.

 

Trends to Watch Out - Global Self-service Business Intelligence (BI) Market 2017

 

The Global Self-Service Business Intelligence (BI) Market Research Report 2017 provides a comprehensive, detailed analysis of Self-Service BI industry, including the present Self-Service BI market trends and norms. It mainly focuses on the market of big continents, like North America, Europe and Asia, coupled with countries like Germany, US, China and Japan.

Continue reading “Trends to Watch Out – Global Self-service Business Intelligence (BI) Market 2017”

Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

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Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

You have employees? And they bring smartphones to work? Is everything right? Or wrong?

Period.

The moment an employee carries a personal mobile device, be it a smartphone or a tablet, to work, a merger of personal and professional is bound to happen. And this could definitely give a rough time to the employer. If not handled properly.

Also read: What Sets Apart Data Science from Big Data and Data Analytics

Of late, there has been a lot of furore, thanks to our effervescent, ever-efficient media about messaging apps. But the headlines took e negative bend when a London- based banker was fired and fined by FCA for exposing crucial confidential data through WhatsApp. Though he defended himself by stating that he simply wanted to MAKE AN IMPRESSION on his friend, he was booked under cybercrime sections.

mobile-1024x683

Over the past few decades, the communication forms have undergone a magnanimous evolution. Once a mail-driven society is now a bustling centre of myriad high-on-function communication apps, the apps includes personal, social and enterprise-oriented apps.  However, with new technologies materializes new challenges. The best way to manage such personal apps is by ensuring safe and secure mode of communication, instead of banning them completely. Embrace the BYOD culture but with due protective measures.

Also read: How To Stop Big Data Projects From Failing?

Let’s talk about Data Mining

Mobile Device Management (MDM) is the key

MDM is the best way to ensure productivity from the employees, while administering their mobile devices. It allows the employees to access data and meaningful information without posing any threat to company data. By implementing MDM, companies can keep a tab on corporate data segregation, corporate policies, secure emails and confidential documents, and integrate and manage mobile devices. Sometimes, a company can go a step higher by restricting users from using WhatsApp on their company provided device, and in its place give them some secure and safe team messaging solution.

Launch a secure team messaging app

For safekeeping of confidential company data, make sure you provide your employees an efficient messaging app. Choose an app that ensures better control over the information that is to be accessed or shared by the users.

The app should be used by the team admin to keep an eye on the team’s activities and the content that they are sharing. They are the ones responsible to control who can or cannot join the team, along with blocking external domains.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

It is advisable to select a tool that provides its users advanced controls, from basic channel level. Flock is developed on these mechanisms and empowers the channel admin to delete any content, and add/remove members from the team. These ways are good to go in restricting the leakage of confidential data through company professionals.

Awareness and compliance helps

Security Business People Team Teamwork Success Strategy Concept

Make your employees, your strength and not weakness. They are the best defence against any attempt of breaching crucial data. So, ensure compliance by conducting frequent safety awareness audits and workshops. Also, make sure that not every employee has access to sensitive company data, as it enhances the risks of becoming a victim of cybercrime.

Still wondering, what have you done to secure your company’s confidential data?

For more tips and advices, keep updated via DexLab Analytics. The prime Big Data training institute feels honoured to offer a wide spectrum of intensive courses on Data Science Online training in Gurgaon for aspiring students and industry professionals.

 

Interested in a career in Data Analyst?

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Using Predictive Modelling to Determine How Well Michael Jordan Performed Under Pressure

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Using Predictive Modelling to Determine How Well Michael Jordan Performed Under Pressure

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How to Determine the Size of a SAS Data Set

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How to Determine the Size of a SAS Data Set

When program codes, applications and SAS data sets are developed, enough attention is often not given to EFFECIENCY, especially during the initial phases of development. Since, data size and system conduct can influence a program or an application’s functioning, SAS users need to access information about a data set’s size and content. To ascertain how much disk space a data set is using, users can easily do a few calculations to learn to access metadata content and attain the important information. Determine, estimate and understand information with this following tip, which helps improve SAS performance and fine-tuning of techniques.

Also read: How to Code Colour Values Within SAS Enterprise Guide


Implementing PROC SQL and DICTIONARY.TABLES

The SAS system accumulates valuable information (also known as metadata) about all-familiar SAS libraries, indexes, data sets (tables), system options, views, catalogs, macros and an assemblage of other “read-only” tables called Dictionary tables and SASHELP views. TABLES, a particular Dictionary table and its SASHELP view equivalent, VTABLE, consists details about a SAS session’s data set. Check the following PROC SQL code as its specification will help us get access to the contents of four columns observed in the TABLES Dictionary table, namely BNAME, MEMNAME, MEMTYPE and FILESIZE to exhibit the size of the CARS data set.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips


PROC SQL and Dictionary.TABLES:

PROC SQL ;
  TITLE ‘Filesize for CARS Data Set’ ;
  SELECT LIBNAME,
         MEMNAME,
         FILESIZE FORMAT=SIZEKMG.,
         FILESIZE FORMAT=SIZEK.
    FROM DICTIONARY.TABLES
      WHERE LIBNAME = ‘SASHELP’
        AND MEMNAME = ‘CARS’
        AND MEMTYPE = ‘DATA’ ;
QUIT ;

Results

Size-of-SAS-data-set1

Analysis

The above results show that the CARS data set filesize is 192KB.

Nota bene: If the SIZEKMG.format is mentioned in a format=option, SAS ascertains whether it should apply KB for kilobytes, MB for megabytes or GB for gigabytes, and divide the filesize value with the help of one of the following values:

KB           1024

MB          1048576

GB           1073741824


Using PROC PRINT and SASHELP.VTABLE

In the following example, the provisions of a PROC PRINT are explained to access the constituents of three columns found in the VTABLE SASHELP view, particularly LIBNAME, MEMNAME and FILESIZE to exhibit the size of the CARS data set.

Also read: SAS Still Dominates the Market After Decades of its Inception


PROC PRINT and SASHELP.VTABLE

PROC PRINT DATA=SASHELP.VTABLE NOOBS ;
  VAR LIBNAME MEMNAME FILESIZE ;
  WHERE LIBNAME = ‘SASHELP’
    AND MEMNAME = ‘CARS’ ;
  FORMAT FILESIZE SIZEKMG. ;
  TITLE ‘Filesize for SASHELP.CARS Data Set’ ;
RUN ;

Results

Size-of-SAS-data-set2


Using DATA _NULL_, SASHELP.VEXTFL and CALL SYMPUTX

Lastly, a DATA_NULL_ is depicted to approach the contents of the VEXTFL SASHELP view with a FILENAME statement. An assignment statement is specified to determine the FILESIZE value for the size of the CARS data set. The CALL SYMPUTX left supports and chops off the trailing blanks from the digital FILSESIZE value of 196608.

Also read: Things to judge in SAS training centres


DATA_NULL_and SASHELP.VEXTFL

filename myfile 'C:\Program Files\SAS9.4\SASFoundation\9.4\\CORE\SASHELP\Cars.sas7bdat' ;
DATA _NULL_ ;
  SET SASHELP.VEXTFL (WHERE=(FILEREF=’MYFILE’)) ; 
  /* Calculate the Filesize in MB */
  FILESIZE = FILESIZE / (1024 ** 2) ;
  CALL SYMPUTX (‘FILESIZE’,FILESIZE) ;
RUN ;

Results

Size-of-SAS-data-set3

 

Learn more about SAS Predictive Modelling by taking up SAS certification courses in Delhi and Gurgaon. DexLab Analytics offers excellent SAS analytics course for data enthusiasts.

 
This post originally appeared on – blogs.sas.com/content/sastraining/2017/04/25/determining-the-size-of-a-sas-data-set
 

Interested in a career in Data Analyst?

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Artificial Intelligence: What the Future Holds for India, Next to US

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Is artificial intelligence outgrowing human intelligence? Is AI becoming smarter than we are?

Artificial Intelligence: What the Future Holds for India, Next to US

In the United States – the world’s unchallenged superpower, a new strange issue has popped up, which is being discussed on all the major interactive platforms, like books, talk-shows, YouTube, etc. but to no avail. The issue-in-question is addressed in the beginning of the blog.

Also read: Learn to Surf on the Three Waves of Artificial Intelligence

Will computer intelligence exceed human intelligence?

In India, this doesn’t seem to bother us much. For us, computers are electronic devices that we control, that we command. Our smartphones and tablets are treated as our servants and not our masters. But I wonder for how long will this persist? How long will we be able to refrain ourselves from being influenced by the West? Well, that’s another question to answer and let’s keep it for another day!

Also read: What Makes Artificial Intelligence So Incredibly Powerful?

In the US, some of the tech pundits are working tirelessly on the newer realms of AI each day and speculating what will happen when computer programmes finally overhauls human brain in thinking abilities. Intelligence is a set of information, and the potential to know how to use it.

Also read: How Machine Learning Training Course and AI Made Lives Easier

Since 1990, computer technology has evolved substantially and has become nifty in intelligence. Let’s take the example of self driving cars ploughing the American roads. A fully independent, self-driving car is no more a VFX-induced scene from a sci-fi fantasy movie; in two years or more, they will be found dominating the streets of the US and trust me they will be a reality! Sit in your car, read a book or sleep while your car reaches its destination on its own. This car will perform all those functions that you used to do, giving you a hiatus from driving!

Also read: DexLab Analytics’ Take on the scope of Artificial Intelligence: Its Humanity vs. Algorithms

The boons of AI don’t end here, it’s thriving and improving faster. Why? Because, humans need to address a whole lot of problems with the help of technology. From conducting complicated surgeries to developing hi-tech BI tools, the scope of computer intelligence is vast and still increasing.


Another crucial factor is that the human brain is limited and can only contain a fixed amount of cerebral cortex and related substances that helps us think and remember. Beyond that, there is no scope for expansion, but in computer technology, the sky is the limit. It is possible to create a computer as large as a 3-storeyed building and store humongous amount of data in it.

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The research says within the next 25 years, computer intelligence will become so efficacious that it will leave behind man’s intelligence in every way. So, what are you waiting for you? Give your career a robust boost with R programming courses. Reach us at DexLab Analytics, a leading R language certification institute for more queries.

 

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