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The Most Important Algorithms Every Data Scientist Must Know

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Algorithms are now like the air we breathe; it has become an inevitable part of our daily lives and is also included in all types of businesses. Experts like Gartner has called this age as the algorithm business which is the key driving force that is overthrowing the traditional ways in which we do our business and manage operations.

The most important algorithms of machine learning

In fact the algorithm boom with uber diversification has reached a new high, so much so that now each function in a business has its own algorithm and one can buy their own from the algorithm marketplace. This was developed by algorithm developers at Algorithmia to save the precious time and money of business operators and other fellow developers and offers a plethora of more than 800 algorithms in the fields of machine learning, audio and visual processing and computer vision.

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But we as data enthusiasts in the same field with an undying love for algorithm would like to suggest that not all the algorithms from the Algorithmia marketplace may be suitable for your needs. Business needs are highly subjective and environment based. And things as dynamic as algorithms can produce different types of results even in the slightly different situations. Also the use of algorithms depends on a number of factors on how they can be applied and what results one can expect from their application. The variables on which the application of algorithms depends are as follows: type and volume of the data sets, the function the algorithm will be applied for and the industry in which the algorithm will be applied.

Hence, not always reaching for the easy option of buying a readymade algorithm off the shelf and simply tweaking it to fit into your model may not always be the most cost-effective or time saving way to go. So, it is highly recommended for data scientists to educate themselves well on the most important algorithms that must be known by them, as well as the back of their hands. A data scientist must also know how each algorithm is developed and also which purpose calls for which algorithm to be applied.

So, our experts associated with DexLab Analytics developed an infographic to let big data analysts know the 12 most essential algorithms that must still be included in the repertoire of a skilled data scientist. To know more about data science courses drop DexLab Analytics and find your true data-based calling.

 

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The Eclectic Mix of Big Data and Marketing

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Marketers with even rudimentary knowledge of Big Data are better placed to precisely reach the largest amount of potential target customers than their counterparts who are uninitiated to the world of Big Data. Good marketers know the customers they target very well. Big Data facilitates this process.

The collection of data and its storage into separate data banks is simply a process part in acquiring raw data. This data should be reproduced in such a manner that marketers are able to easily grasp. And with the impending explosion of IoT devices, the amount of data too is expected to increase by leaps and bounds.

Marketers need to analyze the data available to them very carefully. The process involved is a complicated one which requires the use of specialized software tools.

This is where a translation management system comes into play. These tools may readily be used in order to get the desired insight from the vast pool of data available. Applications like these have made the process so simple that some people who are using it on a daily basis are even unaware that they are dealing with Big Data.

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Big Data Uses in Marketing

  • Honing Market Strategy Through Monitoring Trends Online

You may use tools as simple as Google Trends to keep abreast of the latest trends in the world of the internet. With a number of ways to customize and filter the results marketers have an easy access of that is trending at any instant and associate the product in ways that let it have increased traction.

  • Define Customer Profiles With Big Data

It is a good idea to consult Big Data while drawing up your ideal profile of customers. There is no more need to make educated guesses with things as they stand of today. Through the use of Big Data marketers have access to the various details like demographics, age, work profile of the consumers they target. The case study of the Avis Budget may be cited where it was found that Big Data facilitated the formation of an effective contact strategy.

  • Engaging the Buyer at the Correct Time

Timing, according to some marketers, of the essence when it comes to marketing. This process too is facilitated by Big Data which makes relevant and timely marketing strategies possible. We may take the case of displaying mobile ads at timings when the customer is most like to be online.

  • Content That Boosts Sales

Big Data also lets marketers know the content that gives them the extra edge when it comes to marketing their products. Some of the tools used in translation management make such an analysis possible with scores on individual pieces of content. Success and efficiency of assets both may be gauged through the use of such tools. With the required information marketers will be able to pinpoint content that customers liked.

  • Predictive Analysis

If the base CRM information of a particular company and other providers of Big Data is taken into account the marketer may get a predictive lead score which in turn may be used to make an accurate prediction of the behavior of leads in the future. The end result is that marketers acquire an indication of considerable clarity on their digital behaviors and should be taken into account more when considering lead scoring.

End Words

Now, do not both of them make up an eclectic mix.

 

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The evolution of Big Data in business decision making

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The evolution of Big Data in business decision making

Big Data is big. We have all established that, and now we know that all the noise about Big Data is not just hype but is reality. The data generated on earth is doubling in every 1.2 years and the mountainous heap of data keep streaming in from different sources with the increase in technology.

Let us look at some data to really understand how big, Big Data is growing:

  • The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device
  • On an average every day almost 11 billion texts are sent across the globe
  • 8 million videos are watched on YouTube alone
  • The global number of Google searches everyday is 5 billion

But the balance has long been tipped off as we have only been creating data but not consuming it enough for proper use. What we fail to realize is the fact that we are data agents, as we generate more than 25 quintillion bytes of data everyday through our daily online activities. The behaviors that add more numbers to this monstrous hill of data are – online communications, consumer transactions, online behavior, video streaming services and much more.

The numbers of 2012 suggest that world generated more than 2 Zetabytes of data. In simpler terms that is equal to 2 trillion gigabytes. What’s more alarming is the fact that by the year 2020, we will generate 35 trillions of data. To manage this growing amount of data we will need 10 times the servers we use now by 2020 and at least 50 times more data management systems and 75 times the files to manage it all.

The industry still is not prepared to handle such an explosion of data as 80 percent of this data is mainly unstructured data. Traditional statistical tools cannot handle this amount of data, as it is not only too big, but is also too complicated and unorganized to be analyzed with the limited functions offered by traditional statistical analysis tools.

In the realm of data analysts there are only 500 thousand computer scientists, but less than 3000 mathematicians. Thus, the talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds prepared to untangle the complex knots of intertwined data hiding useful information.

But to truly harness the complete potential of Big Data we need more human resource and more tools. For finding value we need to mine all this data.

Then what is the solution to this even bigger problem of tackling Big Data? We need Big Data Analytics. This is more than just a new technological avenue, but on the contrary this is fresh new way of thinking about the company objectives and the strategies created to achieve them. True understanding of Big data will help organizations understand their customers. Big Data analytics is the answer behind where the hidden opportunities lie.

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A few advanced tools that are currently in use in the data analysis industry are: SAS, R Programming, Hadoop, Pig, Spark and Hive. SAS is slowly emerging to be an increasingly popular tool to handle data analysis problems, which is why SAS experts are highly in-demand in the job market presently. To learn more about Big Data training institutes follow our latest posts in DexLab Analytics.

 

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How Can Big Data Impact the Lives of Students?

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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.

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

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

Following a massive explosion in the world of data has made the slow paced statisticians into the most in-demand people in the job market right now. But why are all companies whether big or small out for data analysts and scientists?

Companies are collecting data from all possible sources, through PCs, smart phones, RFID sensors, gaming devices and even automotive sensors. However, just the volume of data is not the main factor that needs to be tackled efficiently, because that is not the only factor that is changing the business environment, but there is the velocity as well as variety of data as well which is increasing at light speed and must be managed with efficacy.

Why data is the new frontier to boost your sales figures?

Earlier the sales personnel were the only people from whom the customers gathered data about the products but today there are various sources from where customers can gather data so people are no longer that heavily reliant on the availability of data.

Continue reading “Infographic: How Big Data Analytics Can Help To Boost Company Sales?”

How Data Scientists Take Their Coffee Every Morning

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How Data Scientists Have Their Coffee

To a data scientist we are all sources of data, from the very moment we wake up in the morning to visit our local Starbucks (or any other local café) to get our morning coffee and swipe the screen of our tablets/iPads or smart phones to go through the big headlines for the day. With these few apparently simple regular exercises we are actually giving the data scientists more data which in-turn allows them to offer tailor-made news articles about things that interest us, and also prepares our favorite coffee blend ready for us to pick up every morning at the café.

The world of data science came to exist due to the growing need of drawing valuable information from data that is being collected every other day around the world. But is data science? Why is it necessary? A certified data scientist can be best described as a breed of experts who have in-depth knowledge in statistics, mathematics and computer science and use these skills to gather valuable insights form data. They often require innovative new solutions to address the various data problems.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

As per estimates from the various job portals it is expected that around 3 million job positions are needed to be fulfilled by 2018 with individuals who have in-depth knowledge and expertise in the field of data analytics and can handle big data. Those who have already boarded the data analytics train are finding exciting new career prospects in this field with fast-paced growth opportunities. So, more and more individuals are looking to enhance their employability by acquiring a data science certification from a reputable institution. Age old programs are now being fast replaced by new comers in the field of data mining with software like R, SAS etc. Although SAS has been around in the world of data science for almost 40 years now, but it took time for it to really make a big splash in the industry. However, it is slowly emerging to be one the most in-demand programming languages these days.What a data science certification covers?

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

This course covers the topics that enable students to implement advanced analytics to big data. Usually a student after completion of this course acquires an understanding of model deployment, machine language, automation and analytical modeling. Moreover, a well-equipped course in data science helps students to fine-tune their communication skills as well.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Things a data scientist must know:

All data scientists must have good mathematical skills in topics like: linear algebra, multivariable calculus, Python and linear algebra. For those with strong backgrounds in linear algebra and multivariable calculus it will be easy to understand all probability, machine learning and statistics in no time, which is a requisite for the job.

More and more data-hungry professionals are seeking excellent Data Science training in Delhi. If you are one of them, kindly drop by DexLab Analytics: we are a pioneering Data Science training institute. Peruse through our course details for better future.

 

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5 Online Sources to Get Basic Hadoop Introduction

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Basic Hadoop Courses

Big data Hadoop courses are hitting it big in the world of business whether it is healthcare, manufacturing, media or marketing. Data is generated everywhere, and Hadoop is a readily available open source Apache software program that can be utilized to crunch and store Big Data sets.

As per reports from the Transparency Market Research the forecast shows a promising growth opportunity from the existing USD 1.5 million back in 2012 to USD 20.8 million within 2018. These promising growth numbers suggest that there will be an increased need for human resources to manage, develop and oversee all the Hadoop implementations.

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DexLab Analytics Presents #BigDataIngestion

Many experts believe that one can learn any new subject by simple self-study if only you invest enough time and sincere predisposition towards a topic. After all self-study is actually what a person does to acquire knowledge about any given topic. Be it how to fix a leaky faucet or learn a new language or learn strum a guitar. Studying is on one’s own in any case. But to be an expert in a given field, you have to study on your own while you also need to invest your energy in the right direction. And to know the right direction, you need a mentor or a guide to lead the way.

But if you want to test the waters, and tinker with Hadoop to understand its basics, you can go through the wide range of documents available at the Apache Hadoop website for your perusal. Also try downloading the Hadoop open source release to get the feel of the program while tinkering with different features.

Here are 5 online sources where you can seek some basic introduction to Hadoop for big data:

  1. IBM’s open sources, Hadoop Big Data for the Impatient is a good option to go through the basics of Hadoop. It also offers a free download of Hadoop image (you might need Cloudera) to help you work with examples of Hadoop-based problems. You will also be able to get an idea of Hive, Oozie, Pig and Sqoop. The course is available in Vietnamese, Chinese, Spanish and Portuguese.
  2. Cloudera offers a Cloudera essentials course for Apache Hadoop. Apache Hadoop chapter wise video tutorials are available with Cloudera essentials. But this course is mainly targeted at administrators and those who are well-acquainted with data science, to update their skills on the subject.
  3. YouTube also offers a long list of videos on Hadoop topics for beginners. Some are good while others may not be so helpful for the Hadoop virgins. Simply type Hadoop and you will find a never-ending list of videos related to Hadoop. Some are quite useful for clarifying simple doubts related to Hadoop.
  4. Udemy is another site where you can get some free videos as well as a few for a fee. Simply put Hadoop free on the search bar at their homepage and see what comes up.
  5. Udacity was developed by Silicon Valley giants like FaceBook, Cadence, Twitter and the likes. They offer a 14-day free trial with free course materials. But you will need to pay for the course if you do not finish the course within 14 days.

 

Seeking a good and reliable Hadoop training in Delhi? When DexLab Analytics is here, why look further! Being a recognized Big Data Hadoop institute in Gurgaon, the courses are truly interesting.

 

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Big Data Strikes The Healthcare Industry With Carolinas Healthcare

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A representative from the administrative section of Carolinas Healthcare recently revealed that they are huge fans of Big Data and are extensively harnessing this convenient technology to leverage the quality of facilities they offer their patients.

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Carolinas healthcare is a Charlotte – based firm which is extensively using this new form of data analysis technology at their very own data warehouse to evenly distribute its population of medical treatment seekers. This is helping them make the right choice in finding the most unique cases for their patient base. They are now able to take into consideration various segments that were ignored initially due to the systemic lack in the infrastructure of data management. Now they are counting segments like,environmental and geographical conditions in relation to diseases and are dividing them into segments for better efficiency in determining trends and patterns.

They hope to draw useful conclusions from such studies and to be able to make predictions so that they can minimize readmissions, inappropriate use of emergency aids and take care of the hospitalization procedures.Dr. Michael Dulin M.D. spoke on their latest venture by saying, “It is our firm belief at CHS that to deliver the best hospitalization and healthcare facilities to our patients, we need to make appropriate use of the huge amounts of data that is generated in the healthcare industry”. He is the chief clinical officer at the firm, Dickson Advanced Analytics Group which goes by the name DA2. The unit which was launched back in 2012 and is in its budding years currently. But already comprises of 130 experts who are all working together to make better use healthcare data which is a mountainous amount to begin with. It is of no doubt that Big Data is being of good use to the healthcare industry and there are glorious future prospects for experts concerned with this field, globally.

Dr. Michael further added, that “Taking into considerations the data on genomics, environmental poisons, lab results, demographics, physician’s notes and other data generated based on patients will provide them with the much needed insight required to tighten and personalize the world of health care.

Deep Learning and AI using Python

Current statistics suggest that the data generated in the healthcare world every two years is almost doubling every two years. This is the same for CHS. Thus, it is evident that CHS and other healthcare organizations will require using advanced data mining tools and use statistical methods to cope and thrive in the market.

 

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Big Data And The Internet Of Things

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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.

Deep Learning and AI using Python

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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Join DexLab Analytics’ Big Data certification course and kick start your career in the rapidly developing sector of data science.

 

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