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Category: Big Data in India

A few easy steps to be a SUCCESSFUL Data Scientist

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Data science has soared high for the past few years now; sending the job market into turbo pace where organizations are opening up their C-suite positions for unicorns to take their mountainous heap of data and make sense of it all to generate the big bucks. And professionals from a variety of fields are now eyeing the attractive position of data analyst as a possible profitable career move.

We went about questioning the faculty at our premiere data science and excel dashboard training institute to know how one can emerge as a successful data scientist, in this fast expanding field. We wanted to take an objective position from a recruiter’s point of view and create a list of technical and non-technical skills which are essential to be deemed an asset employee in the field of data science.

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A noteworthy point to be mentioned here is that every other organization will evaluate skills and knowledge in different tools with varying perspectives. Thus, this list in no way is an exhaustive one. But if a candidate has these songs then he/she will make a strong case in their favor as a potential data scientist.

The technical aspects:

Academia:

Most data scientists are highly educated professionals with more than 88 percent of them having a Master’s degree and 46 percent of them have a PhD degree. There are exceptions to these generalized figures but a strong educational background is necessary for aspiring data scientists to understand the complex subject of data science in depth. The field of data science can be seen in the middle of a Venn diagram with intersecting circles of subjects like Mathematics and Statistics 32%, Engineering 16% and Computer Science and Programming 19%.

Knowledge in applications like SAS and/or R Programming:

In depth knowledge in any one of the above tools is absolutely necessary for aspiring data scientists as these form the foundation of data analysis and predictive modeling. Different companies give preference to different analysis tools from R and SAS, a relatively new open source program that is also slowly being incorporated into companies is Hadoop.

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For those from a computer science background:

  • Coding skills in Python – the most common coding language currently in use in Python. But some companies may also demand their data scientists to know Perl, C++, Java or C.
  • Understanding of Hadoop environment – not always an absolute necessity but can prove to be advantageous in most cases. Another strong selling point may be experience in Pig or Hive. Acquaintance with cloud based tools like Amazon S3 may also be advantageous.
  • Must have the ability to work with unstructured data with knowledge in NoSQL and must be proficient in executing complex queries in SQL.

Non-technical skills:

  • Impeccable communicational skills so that data personnel can translate their technical findings into non-technical inputs comprehensible by the non-techies like sales and marketing.
  • A strong understanding of the business or the industry the company operates in. leverage the company’s data to achieve its business objectives with strong business acumen.
  • Must have profound intellectual curiosity to filter out the problem areas and find solutions against the same.

 

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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 Worst Techniques To Build A Predictive Model

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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:

 

Predictive Model

 

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.

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Decades On, SAS is Still the Market Leader

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In the 2016 February report by Gartner, SAS bagged the top slot in its execution ability and was once again placed in the quadrant of leaders in the Magic Quadrant for Advanced Analytics Platforms.According to the description, as provided by Gartner, advanced analytics involves various sorts of data analysis through the use of quantitative methods of great sophistication like machine learning, statistics, simulation, data mining in its both predictive and descriptive forms as well as optimization.

 

Decades On , SAS Still The Leader

 

The goal is come up with insights that are unlikely to be discovered through approaching business intelligence traditionally like query and reporting.

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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.

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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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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 And The Internet Of Things

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bigdata

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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The Role of Big Data in the Largest Database of Biometric Information

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BIG DATA

Aadhaar project from our very own India happens to on the most ambitious projects relying on Big Data ever to be undertaken. The goal is for the collection, storage and utilization of the biometric details of a population that has crossed the billion mark years ago. It is needless to say that a project of such epic proportions presents tremendous challenges but also gives rise to an incredible opportunity according to MapR, the company that is serving the technology behind the execution of this project.

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Aadhaar is in its essence a 12 digit number assigned to a person / an individual by the UIDA , the abbreviated form of “Unique Identification Authority of India” The project was born in 2009 and had former Infosys CEO and co-founder Nandan Nilekani as its first chairman and the architect of this grand project which needed much input in terms of the tech involved.

The intention is to make it an unique identifier for all Indian citizens and prevent the use of false identities and fraudulent activities. MapR which is head-quartered in California is the distributor and developer of “Apache APA +0.00% Hadoop” has been putting into use its extensive experience in integrating web-scale enterprise storageand real-time database tech, for the purposes of this project.

According to John Schroeder who is the CEO and co-founder of MapR, the project presents multiple challenges including analytics, storage and making sure that the data involved remains accurate and secure amidst authentications that amount to several millions over the course of each passing day.Individual persons are provided with their number and a iris-scan or fingerprint is taken so that their identity might be proved and queried to and matched from the database backbone to a headshot photo of the person. Each day witnesses over a hundred million verifications of identity and all this needs to be done in real-time in about 200 milliseconds.

India has a percentage of rural population many of which are yet to be connected to the digital grid and as Schroeder continues the solution had to be economical and be reliable even under low bandwidth situations and technology behind it needed to be resilient which would work even with areas with low levels of connectivity.

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For more information on big data and big data hadoop courses, peruse through the official site of DexLab Analytics. It is a major Big Data Hadoop institute in Gurgaon.

 

Source: Forbes

 

Interested in a career in Data Analyst?

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