While some of these techniques may be a little out of date and most of them have evolved over time greatly, for the past 10 years rendering most of these tools completely different and much more efficient to use. But here are few bad techniques in predictive modelling that are still widely in use in the industry:
1. Using traditional decision trees: usually too large decision trees are usually really complex to handle and almost impossible to analyze for even the most knowledgeable data scientist. They are also prone to over-fitting which is why they are best avoided. Instead we recommend that you combine multiple small decision trees into one than using a single large decision tree to avoid unnecessary complexity.
We will start off this post with a little bit of trivia.
The advertised median salary on offer for technically inclined professionals with expertise in Big Data, which today is a highly sought-after skill is no less than $124,000 inclusive of compensation and bonuses.
Cisco, IBM and Oracle together had 26,488 positions that were open during the previous year which required expertise in Big Data.
EMC or Dell required 25.1% of all positions in Big Data to have analytics tracks.
Data Warehousing, VMWare and developing programming expertise in Python are the fastest growing skill sets that are in demand by companies that are on an expansion of their development teams in Big Data.
According to figures released by IBM opine that no less than 2.5 quintillion bytes created on a daily basis. Also it is worthwhile to note that a whopping 90% of the total data in the world have been created only in last two years.
In simple terms data is just pieces of information. The highly prominent concept of our times owes its origins to large data amounts which are derived from all sorts of computing devices. This data is then stored, collated and combined with the sophisticated tools for analytics available today.
Big Data is helpful to a broad spectrum of people from marketers to researchers. It helps them to understand the world around them and take optimized action through insights. Students too stand to benefit from Big Data a great deal and in this post we look at two ways through which Big Data may affect the lives of students.
It Helps To Be More Effective
Teachers have always been an informed lot, using data in order to optimize the practices and methods, Big Data facilitates the creation of far more powerful ways through which teachers and students may connect. As the focus shifts towards personalized learning, teachers are in a position to utilize more data than ever before.
This may be achieved through monitoring of study materials and how they are used by students in order to deliver more targeted instruction. With Big Data teachers will be able to better understand the needs of students and adapt lessons effectively and swiftly and in the end make decisions about enhanced learning for students, driven on the basis of data.
There is a Huge Demand for Data Scientists
Data Science was dubbed as the sexiest job of this century by Harvard Business Review and with good reason. People are just beginning to explore the possibilities enabled by Big Data and the need of skilled people in the field will only continue to increase in the years to come. Data Scientists have the ability to mine through data to the benefit of their employers including but not restricted to governments, businesses and of course, the academia.
McKinsey Global Institute reported that by 2018 there will be a shortage of no less than 190,000 persons with skills in deep analytics in the United States of America alone. There is no shortage for opportunities in this field and there are numerous programs all over the world that smooth out the career transition to Big Data. Work arrangements that display flexibility, more than decent compensation packages and the opportunity to make a significant impact are the added bonuses that go along being a data scientist.
We may conclude by saying that though Big Data is still emerging it held by most experts to be the undeniable future not only for those pursuing studies in data science and making careers in the field but to all the people whose lives are changed for the better through Big Data.
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SAS experienced its 40th successive year of growth as customers vied for the software in order to stay ahead of the fraud, risk and security challenges. The operating revenue of SAS stood at 3.16 billion US Dollars which was an increase of 6.4%. The sales in software also surged to 12% indicating that the SAS popularity is unlikely to abate in the coming years and it will cement its position as the market leader in the analytics sector.
Making its Presence Felt Globally
The growth of total revenue recorded was healthy across the world. The percentage growth touched double digits in most of the regions. It was no doubt facilitated by the new abilities of SAS in fraud, risk and intelligence about security.Much of the SAS revenue was fuelled by the governmental, financial and insurance sectors. The industries that led the way with greatest growth were manufacturing, banking, services and retail.
Hadoop is being increasingly used by companies of diverse scope and size and they are realizing that running Hadoop optimally is a tough call. As a matter of fact it is not humanly possible to respond to the changing conditions in real time as these may take place across several nodes in order to fix dips in performance or those that are causing bottlenecks. This performance degradation is exactly what needsto be critically remedied in cases where Hadoop is deployed on large scales where Hadoop is expected to deliver results critical to your business in the proper time. The following three signs signal the health of your Hadoop cluster.
The Out of Capacity Problem
The true test of your Hadoop infrastructure comes to fore when you are able to efficiently run all of your jobs and complete them within adequate time. In this it is not rare to come across instances where you have seemingly run out of capacity as you are unable to run additional application. However monitoring tools indicate that are not making full use of processing capability or other resources. The primary challenge that now lies before you is to sort out the root cause of the problem you have. Most often you will find them to be related to the YARN architecture that is used by Hadoop.YARN is static in nature and after the scheduling of jobs the process of adjusting system and network resources. The solution lies in configuring YARN to deal with worst case scenarios.
To put it simply Business Intelligence is the action of extracting and to derive information that may be of use from the available data. As might be evident the process is a broad one where the quality and the source of the data structure is variable. Transformations like this might in technical terms be described as ETL or extract, transform and load in addition to the presentation of information that is of use.
R Programming in Business Intelligence
Some R Programming Experts hold that R is fully able to take on the role of the engine for processes related to BI. Here we will focus only on the BI function of R i.e. to extract, transform load and present information and data. The following packages correspond to indicated processes in Business Intelligence.
Extract
Extraction
RODBC
DBI
data.table’s fread
RJDBC
In addition to these, there are several other packages that support data in a variety of formats.
Transform
data.table
dplyr
Load
DBI
RODBC
RJDBC
Prsentation
Presenting data is a wholly different ball game than the previously mentioned process of ETL. Never fear, it may be outsourced with ease to tools of BI dashboard with ease by populating the structure of data according to the expectations of the particular data tool. R is able to create a dashboard of a web app directly from within itself through packages like:
shiny
httpuv
opencpu
rook
These packages let you play host to interactive web apps. They have the ability to query the data in an interactive manner and generate interactive plots. The basis for all of these is an R session engine and is able to execute all functions of R and may leverage the capabilities of statistics of all packages in R.
Extras
The above mentioned packages serve as the core whose functionality may be simplified through the use of the packages mentioned below:
db.r
ETLUtils
Sqldf
Dplyr
shinyBI
dwtools
The following factors are critical while R is adopted by businesses:
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MS Excel needs no introduction as spreadsheet program. As part of the MS Office suite it has been a regular software skill expected from employees across the globe regardless their roles or levels. But the utility of MS Excel in the world of Big Data is not so widely acknowledged due to the lack of awareness. But that does not rob it of any of its sting as a Big Data tool to advanced Excel users.
So if you are keen to know more about the emerging technology that elite techies cannot stop raving about, a solid grounding in MS Excel will serve you well. Accordingly DexLab Analytics has scheduled a symposium on the topic of Designing MS Excel Dashboards as an introduction to the Big Data capabilities of Big Data to aspiring data analystand data scientists. The symposium is going to be MS Excel Experts who also instruct students of DexLab Analytics most of whom have been advanced users of MS Excel for more than a decade.
The main speaker of the symposium is an industry expert who is currently attached with a leading Multi-National Company for over 5 years. He will bring with himself invaluable information regarding the latest developments in data science. We will cover the following topics in the meet scheduled to be held on the 26th of January:
MS Excel functions overview like V Look Up, Match, H Look Up, Address, Match, Countlfs, Indirect, Sumlfs amongst many others.
Introducing the world of recording macros and building VBA.
Introducing Advanced Excel with abilities in Dynamic Referencing and pivot.
Hot to make use of Excel and VBA in order to generate KPI dashboards.
The interactive session with industry professionals with many years of experience and help you acquire invaluable exposure to the basics of MS Excel so that you get a foretaste of what lies in store for you in this new and exciting world called Big Data.
Note: It is assumed that the participants of this event have a basic understanding of the rudiments of statistics.
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Since a long time we are providing Big Data Hadoop training in Gurgaon to aspirant seeking a career in this domain.So, here our Hadoop experts are going to share a big data Hadoop case study.Think of the wider perspective, as various sensors produce data. Considering a real store we listed out these sensors- free WiFi access points, customer frequency counters located at the doors, smells, the cashier system, temperature, background music and video capturing etc.
While many of the sensors required hardware and software, a few sensor options are around for the same. Our experts found out that WiFi points provide the most amazing sensor data that do not need any additional software or hardware. Many visitors have Wi- Fi-enabled smart phones. With these Wifi log files, we can easily find out the following-
The data that is derived from the Internet of Things may easily be used to make analysis and performance of equipment as well as do activity tracking for drivers and users with wearable devices. But provisions in IT need to be significantly increased.Intelligent Mechatronic Systems(IMS) collects on an average data points no fewer than 1.6billion on a daily basis from automobiles in Canada and U.S.
The data is collected from hundreds of thousands of cars that have on board devices tracking acceleration, the distance traversed, the use of fuel as well as other information related to the operation of the vehicle.This data is then used as a means of supporting insurance programs that are based on use.Christopher Dell, IMS’s senior director recently stated they they were aware that the data available were of value, but what was lacking is the knowledge on how to utilize it.
But in the August of 2015, after a project that lasted for a year, IMS added to its arsenal a NoSQL database with Pentaho providing tools related to data integration and analytics. This lets the data scientists of the company increased flexibility to format the information. This enables the team of analytics to make micro analysis of the driving behavior of customers so that trends and patterns that might potentially enable insurers to customize the rates and policies based on usage.
In addition to this the company further is pursuing an aggressive growth policy through asmartphone app which will further enhance its abilities to collect data from vehicles and smart home systems making use of the Internet of Things.Similar to the case of IMS, organizations that look forward to analyze and collect data gathered from the IoT or the Internet of Things but often find that they need an upgrade of their IT architecture. This principle applies to enterprise as well as consumer sides of the IoT divide.
The boundaries of business increasingly fade away as data is gathered from fitness trackers, diagnostic gears, sensors used in industries, smartphones. The typical upgrade includes updating to big data management technologies like Hadoop, the processing engine Spark,NoSQL databases in addition to advanced tools of analytics with support for applications drivenby algorithms. In other cases all it is needed for the needs of data analytics is the correct combination of IoT data.
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