There is a lot of confusion in the data science Job, as it is relatively new profession. We have got a lot of queries about Data Scientist salary and there career path. In this blog will talk about the how data scientist came into a picture and what is the starting salary for this job.
Statistics state that history’s most unbalanced demand and supply ratio is seen today in the Big Data Industry. It is known that in the U.S.A there would soon be a shortage of around 140,000-190,000 professionals, with the required skill set for data analytics. With a tsunami like amount of information being generated by firms on a daily basis, it becomes difficult to for them to make sense of it.
This is where the Data Scientist or the Data Analyst comes into the picture. These are individuals equipped with a certain skill set, who can take all this information or more popularly known as data and make sense of it. They work with great volumes of data sets, study them and generate various insights which help the company prosper.
As this is a fairly new thing, there are a lot of areas which are clearly out of focus. There has been no clear distinction between the two terms ‘Data Scientist’ and ‘Data Analyst’ and people still haven’t had any clear cut idea about what is meant by either Hadoop or SAS Programming and so on.
As this field needs a specific skill set like statistics, an eye for drawing out the patterns, being great at analysis and exceptional at programming knowledge; makes the number of professionals apt for this job very limited. The fact that there has been a rising demand in the firms for Data Scientists, states that the career prospects in this field have grown exponentially.
Glassdoor placed it in the first position on the 1st, as a Best Jobs in America list. According to IBM, demand for this role will soar 28% by 2020.
It is believed that the field of Data Analytics would be further divided into three different categories. These would be for professionals who would be good at coding and creating languages to sort the data, people possessing exemplary statistical skills and those who have an eye for drawing traits and patterns from the same.
With the Data Analytics Industry becoming dynamic by the day, the prospects for someone looking to make it their career are really high. The average salary of a Data Scientist starting into this industry can range from 3lakh-4lakh and can go onto 12lakh- 20lakh per annum.
There are a lot of courses offered in Data Analytics today, whereby any aspirant can get trained in various data analytics tools like R Programming, Python, SAS Programming, Big Data Hadoop and many others.
At, Imarticus Learning we offer various short term and long term courses in Data Analytics and the tools therein.
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Data Scientist or an Analytics profession is the calling of recent times. This is a new breed of professionals who possess the technical skills required to solve the complex problem and also are inquisitive enough to come up with problems that need a solution. This, in turn, assists corporate to come up with predictive analysis to spot trends and help come up with realistic solutions. Data scientist or analyst work with high volumes of data to drive to conclusions. They are part mathematicians, part computer scientist, and trendsetters. Huge volumes of unstructured data or Big Data cannot be ignored but is considered as a gold mine that helps increase the revenue of organisation across different fields like, financial, IT, retail, Hospitality, education, in short, the whole spectrum.
Earlier data scientist started their careers as either statisticians or data analysts. But with the evolution of big data, there has been a growth in their roles too. Data is no longer just associated with IT, it requires systematic analyses a creative curiosity and most importantly, knowledge of tools to translate great ideas and information into a simple presentation for the non-technical audience responsible for taking the decisions.
And thus, for an individual who is in the process of advancing his career in data science, comes the struggle to choose the right tool for the job. It is an ongoing battle as to which programming language is best suited for data analysis. And although in recent times there are many options that are available, the traditional question is primarily always between SAS or R, with Python as the new entrant which cannot be ignored.
Main difference between both programming tools –
Open v/s Closed – SAS is a closed source; it requires licences and approvals, hence it does not support transparent functionalities. R programming language and even Python coding program are open sources and as opposed to SAS contains detailed transparency of all of its functionality.
Cost – SAS is one of the most expensive tools to existing. Since R is an open source software, it can be downloaded for free by anyone.
Learning – SAS is fairly easy to learn, especially if one has the basic SQL knowledge, it has a stable GUI interface. Tutorials of SAS are also available on various sites. R is a low-level programming language and hence it requires complex codes for shorter procedures, one needs deeper insights of coding in R.
Accessibility – Almost all advanced features need licenses for their new products on SAS, increasing the cost and accessibility. Whereas R allows to access or upgrade to the advanced features easily.
Graphical Capabilities – SAS has basic graphical capabilities, but it is only functional. With reference to this factor, R has the best graphical capabilities when compared with the SAS.
Let us further understand the Description of the Tools –
SAS – SAS is considered to be the leader in the data analytics field, it is an integrated software solution. This software also has a lot of good features like GUI and excellent technical support. It is generally used to perform tasks such as data entry, retrieval and management, for report writing, to conduct statistical and mathematical analyses, for research in operations and project management. SAS is one of the oldest and trusted programming tool used by big global corporate especially in the field of finance. Some reputed companies that use SAS are Barclays, HSBC, PNB Paribas, Nestle etc…,
R – R is a programming tool for statistical computing and graphics, it offers a wide range of techniques. Since it is an open source tool, it is highly extensible. It is a simple and effective programming language and it is more than just a statistic system. It is generally used to perform tasks such as visualise data, Machine Learning etc…, R is also used by reputed companies but is usually popular with startups and mid-sized organisations.
So to conclude if one has a goal to become a business analyst professional and is planning to join a bank or the financial services where the company is using SAS and might want to fund the course or will partially fund the learning, then you should take up SAS and maybe later learn R once comfortable with SAS. Remember learning SAS programming course might be fairly easy, but is very expensive and if one wants to join a start-up where SAS is not used, then to have the skill is of little use. In such a scenario it is better to learn R, also it is advisable to learn R if you have a statistic or a programming background.
Having knowledge in R and Sas is imperative if you want to excel in the profession of Data Science.
The field of Artificial Intelligence seems to working on a winning streak. In the year 2005, the U. S Defence Advance Research Project Agency, held the DARPA Grand Challenge, which was supposedly held to spur development of autonomous vehicles, basically in order to make self-driven, smart cars. This challenge was taken up and successfully completed by 5 teams. In the year 2011, in a great competition of Jeopardy, the IBM Watson system, was successfully able to beat two long time, human champions of the same legendary game. Another great win of technology over the human race would be in the year 2016, when Google DeepMind’s AlphaGo system was able to successfully defeat the world champion of Go Player, who was reportedly the world champion for 18 consecutive times.
While these feats of technology over the human brain are extremely commendable, today the long surviving dream of humans, which basically revolved around developing technology to control their surroundings, has finally come to fruition. This has resulted in the form of Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa. As a result of all of these AI (Artificial Intelligence) powered virtual assistants, people are able to make greater use of technology in order to live better lives.
Artificial Intelligence is considered to be a field of computer science, which is entirely devoted to the creation of computing machines and systems, all of which are able to perform operations that are similar to human learning and decision making. According to the Association for the Advancement of Artificial Intelligence, AI is, “the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines.” While these intelligence levels can never be compared to those of the humans, but they can certainly vary in terms of various technologies.
Artificial Intelligence includes a number of functions, which include learning, which primarily includes a number of approaches such as deep learning, transfer learning, human learning and especially decision making. All of these functionalities can later help in the execution of various fields such as cardiology, accounting, law, deductive reasoning, quantitative reasoning, and mainly interactions with people, in order to not only perform tasks, but also to learn from the environment.
While the recent changes may be extremely mind blowing, the promise of AI has always been existing since era of electromechanical computing, this began in the time period, after the World War 2. The first conference of Artificial Intelligence was held at the college of Dartmouth in the year 1956 and at that time, it was said that AI could be achieved within the time period of summer. Later on, in the 1960’s there were scientists, who claimed that in the next decade, it would be possible to see various machines controlling human lives. But it was in the year 1965, when the Nobel Laureate, Herbert Simon, who is supposed to have predicted the words, which would have some substance and which were, “In the next 20 years, it would be possible that machines would be able to do any work of labour that man can”.
With Artificial Intelligence, going in full fervour, the field which it has affected most in the field of Data Science. And as there are many who believe that there is a great to achieve in this field, have begun to get trained in the same by approaching professional training institute – Imarticus Learning.
Since the time its popularity hit the roofs, there’s one statement about Big Data that’s remained a constant. “It isn’t about what you know, it is mainly all about what you do, with what you now.” While this may seem as a bit of gibberish to some, industry experts claim that it happens to be a valuable lesson, that companies across the globe will soon end up learning, in the coming years, especially when it comes to the field of Big Data.
Innumerable industry experts among us claim, that 2017 will be the ‘It’ year. The year when data science and big data are bound to go mainstream. Did you know that there are a number of teenagers out there, who are entirely dependent on Google analytics to monitor their brands, regardless of their size. There are a number of parallels drawn between, the thriving start-up culture on one hand, and the increasing developments in the field of predictive analytics and target marketing.
As we are well into the year of 2017, it can be noted, that there are a number of changes in store for Data Science. There are signs of a meaningful shift, gradually taking place when it comes to business and big data. It probably would be the first time, when data analytics, would be the driver of a number of business operations. This change will be a very rewarding proposition for all of those working in the data science industry. While on the other hand, those companies who are lagging behind in this race of technology, could be in for some serious liabilities.
According to Harvard Business Review, “A majority of business outfits today, are nowhere close to recognizing the value and benefits, that data analytics can bring to their firms.” Industry experts believe there happen to be a number of reasons for the same. From lack of communication to absolute absence of a proper, well-designed plan could result into businesses, being entirely oblivious to kind of benefit data analytics can bring. While this news may lead you to panic, there are still a number of things that you, as a business entrepreneur can do and you, as a data science professional can be well aware of.
When it comes to gathering the generated data, almost every single person in the company must buy into the value analytics. If your firm fails to do so, it runs the risk of your company ending up with data, that is either worthless, or enormous amounts of data insights, which will rarely be put to use. Every company and firm out there, needs to make a proper action plan, especially when it comes to the professionals, who are responsible for managing data, reporting it, gathering information, inputting the data and most importantly, who analyzes this data. If these processes aren’t outlined properly, your data will almost never pay.
As the whole world comes to terms with the potential of Big Data and data analytics, there is an increasing need for trained professionals, who are adept in working with data analytics tools. A number of data enthusiasts have begun to look for institutes like Imarticus Learning, which will offer them industry endorsed training programs, in various data analytics tools like R, SAS, Python, Hadoop and so on.
Data Science as a concept has existed for quite some time, but it’s come into the limelight in very recent times. The whole world is witness to the kind of magic and power, that data analytics generally exudes, as a result of which, it is imperative for every business out there to be able to acknowledge this phenomenon. Regardless of the size, manner, focus area or revenue of a firm, it is essential for it, to understand the dynamics, behind the enormous amount of data, that it generates due to its clients and the maintenance of the same. While there are field where spreadsheets still hold the place of power, but they have long become redundant and obsolete, all because of the emergence of data analytics tools. These data analytics tools are essentially the very important cogs of the proverbial machine, which help data scientist accomplish absolute feats with predictive analytics. So when it comes to the go to tools of data analytics, there ensues an intense debate, so as to which one could happen to be the best or the most efficient aid.
While many believe that SAS programming (mainly due to its time honored presence in the industry and its huge client base), is the tool to go for, lately the younger generation has been differing opinions. Many believe that the best programming language right now is the R Programming language, one of the main reasons cited here, is the fact that R, is an open sourced programming language, which means that it is easily accessible as well as free to be downloaded. Being free of cost, over time, R has generated its own community of users, which includes numerous data scientists, who have all the liberty to develop updated beta versions and to fix the bugs. It has become the hot favorite of all those data analysts and data scientists, working to analyze huge amounts of information and being able to formulate new breakthroughs, in various business fields.
Apart from being a great tool for use in data analytics, R programming comes to be of major use when it comes to business analytics. This programming language basically, makes it very easy for any business to go through its entire data, in the most hassle free manner. It primarily scales all the information, so that numerous parallel processors, are able to work at the same time. As many computers don’t have sufficient memory, to handle and deal with enormous amounts of data, R programming offers ScaleR, which is a part of the application that does the job of trying to re-purpose great amounts, into smaller chunks of information, so that it can be processed on a number of servers, at the very same time. As R allows the users to analyse statistical information in the most sophisticated of manner and in literally a matter of minutes, which most of the other languages cannot really accomplish; this makes R a force to reckon with in the world of business analysis. Rising popularity of R has led to quite a number of people opting to get professional trained in this language, for which majority of them look for institutes offering certification courses like Imarticus Learning.
The phenomenon that is well known as Big Data Science has literally gone on to spread all over the globe in a similar fashion as that to a raging wildfire. The world of business and commerce has remained no stranger to this concept and field and in fact has embraced it more than any other. Big Data has been the catalyst in some of the most remarkable discoveries, especially in the field of HR. While the technical aspect dictates that the various valuable insights provided by big data have made for amazing growth and development of a number of firms, it has also helped a number of HR managers in targeted recruitment as well as employee enhancement. As a professional in the HR industry, regardless of the position, there are a certain number of things and concepts that one has to abide by. As surprising as it may sound, it has been proven recently that these few concepts still hold great value and importance, especially when it comes to big data in HR.
The first basic thing, that all HR professionals need to remember, is the massive difference that there is between “story telling” and “story selling”. This is basically only in the context of how a professional perceives a certain data set. It is important for these professionals to be able to distinguish between a very neutral interpretation of data and on the other hand, a data set that is used to derive a certain expected solution. This skill becomes very important in a market, where every vendor and seller is out there to promote their business solutions, entirely on the basis of data and numbers. When faced with numerous such vendors, it holds in positive stead for you to be a little skeptic.
Another basic concept here is not confusing correlation to causation, which is the one of the primary attributes of statistical analysis. This simply put in layman terms, goes on to state that just because two things are related to each other, it does not mean that they are also the cause of each other. This is very important especially in the world of Human Resources, because HR services are most often related to positive business results. This would only be possible, when the professionals are able to realize the difference between certain variables, that can actually cause similar kind of impact, thus statistically stating the connection between the various HR activities and profitability. The most basic trick to know here, is when you would not require causation at all, when only correlation would be enough to provide the required results. While these days, the data is very much required by the HR professionals, in order to determine the correlation and causation, as well as, evaluation of results and making the decisions. It is also important to know when and how the sample size, taken into account is sufficient.
Thus we can infer that HR is soon becoming the latest avenue for all the data scientists out ther to test their abilities. There is no surprise hence, in the increasing popularity of various institutes. Imarticus Learning, provides excellent professional training in a number of data analytics tools like R, SAS, Hadoop and so on.
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2016 was almost a breakthrough year for the field of Big Data Science. From the concept of Artificial Intelligence being just an imaginative theory in movies, to its real use in understanding customers; from predictive analytics, being able to provide exactly what the customers are looking for, to employee enhancements technologies in HR, disease tracking interactive maps in the field of medicine to the various futuristic, interactive microwaves, T.Vs and refrigerators and so on. 2016 as a year has been a great one, barring a few minor debacles in terms of the functioning of big data.
As the new year has been ushered in, everyone’s in the I.T sector is waiting with bated breath of what new changes, will the new year of 2017, bring into the field of data science.
One thing that is a definite prediction in terms of big data, is the fact that it will soon be entering into the mainstream of IT and technology. This is owing hugely to the popularity of Hadoop and Spark among the various other data analytics tools. There is sure to be a rise in the number of firms and companies, which would entirely rely on the various functions of Hadoop and SAS, to help them in sorting the data.
We have only touched the tip of the iceberg, especially when it comes to all the data analytics tools in the market. For instance, SAS programming, was one software which was the default software used by a number of companies, across industries for everything apart from Data Analytics. While in the earlier scenarios, the use of cloud was to serve the purpose of storage, but today and the future trends point out to the fact, that there will be a number of companies which will start making the use of cloud for not only data storage, but also for data processing.
Now that the whole world is very well adjusted to the introduction of big data technologies, the rate at which it will be adopted, is definitely going to multiply.
There is a prediction that Big Data is bound to grow at a rate of 11.3 percent annually. There will be a great rise in the investments, which will basically be able to ensure the fast speed of data solutions, as people now don’t just want data, they are looking for fast data.
Predictive Analytics is bound to be the next big thing in terms of the various technologies, as these would be able to perfectly target the needs of the customers and provide perfect recommendations.
In terms of a career, Big Data Science is bound to increase in popularity and would be ever more so rewarding for almost all the data aspirants.
R is a programming language, mainly dealing with the statistical computation of data and graphical representations. Many data science experts claim that R can be considered as a very different application, of its licensed contemporary tool, SAS. This data analytics tool was developed at Bell Laboratories, by John Chambers and his colleagues. The various offerings of this tool include linear and non-linear modelling, classical statistical tests, time-series analysis, clustering and graphical representation. It can be referred to as a more integrated suite of software facilities, for the purpose of data manipulation, calculation and data visualization. The R environment is more of a well-developed space for an R programming language, inclusive of user-defined recursive functions as well as input and output facilities. Since it is a relatively new data analytics tool in the IT sphere, it is still considered to be very popular amongst a lot of data enthusiasts.
There are a number of advantages of this data analytics tool, which make it so very popular amongst Data Scientists. Firstly, the fact that it is by far the most comprehensive statistical analysis package available totally works in its favour. This tool strives to incorporate all of the standard statistical tests, models and analyses as well as provides for an effective language so as to manage and manipulate data.
One of the biggest advantages of this tool is the fact that it is entirely open sourced. This means that it can be downloaded very easily and is free of cost. This is mainly the reason why there are also communities, which strive to develop the various aspects of this tool. Currently, there are about some 19 developers, including practising professionals from the IT industry, who help in tweaking out this software. This is also the reason why most of the latest technological developments, are first to arrive on this software before they are seen anywhere else.
When it comes to graphical representation, the related attributed to R are extremely exemplary. This is the reason why it is able to surpass most of the other statistical and graphical packages with great ease. The fact that it has no licence restrictions, makes it literally the go-to software, for all of those who want to practice this in the earlier stages. It has over 4800 packages available, in its environment which belong to various repositories with specialization in various topics like econometrics, data mining, spatial analysis and bioinformatics.
The best part about R programming is that it is more of a user run software, which means that anyone is allowed to provide code enhancements and new packages. The quality of great packages on the R community environment is a testament to this very approach to developing a certain software by sharing and encouraging inputs. This tool is also compatible across platforms and thereby it runs on many operating systems as well as hardware. It can function with similar clarity for both the Linux as well as Microsoft Windows Operating Systems. In addition to this, the fact that R can also work well with other data analytics tools like SAS, SPSS and MySQL, have resulted in a number of takers for this data analytics tool. Imarticus Learning The Data Science Prodegree powered by Genpact is one such course which offers both SAS and R along with the opportunity to be a Data Scientist at Genpact.
Big Data is defined as data that is too large and too complex for data tools to for data tools to capture, store and analyze. Experts in the data science industry, identify data with the three V’s which are, volume, variety and velocity. For instance, the amount of shares traded on the stock market in the US amount to about 7 billion, the number of tweets on twitter daily amounts to 400 million, the number of likes generated by Facebook on a daily basis is about 3 billion and about 10 terabytes of data is generated in one flight from NY to London; all of these come under the purview of Big Data. Studies and research have stated that in the last two years about 90% of the world’s data has been generated.
While no doubt there is a huge amount of data present in the virtual space all around us, but just this data on its own is not really of any use to us, unless there is someone who can make sense out of it.This is where data analytics comes into play.
Analytics can be defined as the scientific process of transforming data into insight, which aids in making better decisions as well opening up new avenues of opportunities, all for a competitive advantage. Analytics thus branches out into business analytics and data analytics. Business analytics does not really make use of tools and techniques as much as it makes use of valuable insight, that is derived from unstructured data and further on leads to a business strategy. The role business analytics thus is analyzing the performance of the firm in the past and providing insights, as to how a certain firm should go about its future business performance. This is basically achieved through data and statistical models, quantitative analysis and evidence based management. Today, with the rapid generation of large amounts of data mainly for business transactions, availability of advanced data storage sets as well as the availability of advanced tools to analyze the same; has all resulted in the growing need for analytics in the business sphere.
Analytics branch out into three main types namely, prescriptive analytics which basically deal with enabling smart decisions based on data, predictive analytics which deal with predicting the future on the basis of historical patterns and thirdly, descriptive analytics which means basically mining the data to provide business insights. In simple terms the branch of prescriptive analytics would deal with something like the reason behind air fare hikes every year, predictive analytics would deal with how these e-commerce websites hand you discount coupons, which you actually end up using and descriptive analytics would deal with how certain movie websites, recommend just the right movie to users. Data Analytics in the current year has resulted in people using smart and integrated everyday gadgets, advanced gadgets which could enhance the performance of an individual in a work place and the boom that has been caused by the advent of Virtual Reality.
Thus analytics is soon to become one of the most highly rewarding careers and companies have already begun to demand more and more trained professionals. With the announcement of careers in this field, being considered as the sexiest careers of the 21st century, a lot of people are opting for a career in this field. This has in turn led to a growth in institutes, which excel in offering tailor made courses so as to ensure the generation of the sought after skill set among candidates. Imarticus Learning offer courses in the various tools and technologies of data analytics, while aiming to bridge the gap between academics and industry at the same time.
With the announcement that, Data Science is one of the sexiest careers of the 21st century and the subsequent success of e-commerce giants with the help of data analytics, the world has seen a rise in the demand for data scientists. These professionals who are also known as Data Analysts are responsible for extracting this data, mining it, analysing it and drawing insights so as to add value to the firms. A lot of companies across different fields have been hiring these professionals due to their specific skill set and the ability to turn numbers into growth and success. These professionals usually work with one or many data analytics tools like, SAS Programming, R Programming, Hadoop and Python and so on. Of these, R Programming is one tool, which has recently seen a lot of popularity and increased number of users, due to the fact that it is open sourced and easily accessible.
All of these data analytics tools are used by the professionals, depending upon what kind of job they want to accomplish.
R Programming is most popular choice, when it comes to thoroughly understanding data, by using various graphs and statistical methods. This tool becomes especially important when it comes to machine learning spaces, mainly due to its several packages and advanced implementations, useful for the top machine learning algorithms, which every data scientist is familiar with. All of these packages help in different kind of functions and are highly sought after, because they can be downloaded free of cost. Being an open sourced platform, R Programming has a huge community of contributors, the world over, who regularly add technical updates, which can easily be added to your projects.
These packages that are a part of R Programming can perform various specific functions like, take care of the missing values, allow you to partition your data, classify and combine, and find out the hidden layers in your data and so on. This vastness and variety in packages, makes for R’s strongest suit. Furthermore it is able to offer rich functionality, for the developers so as to enable them to build their own tools and analysing methods. Being open sourced, has given R a lot of leverage over the other data analytics tools, as the users can very well extend it without the need for any permission. What increases R Programming’s importance in the space of machine learning, is the fact that any new research in the field of data science, has a compatible package of R with it.
R Programming since its inception in around the late 90’s, has only seen major growth; and now, it is being assimilated into various commercial fields such as Oracle, IBM, MATLABS and others. There is a long list of companies in the data analytics industry, which have already declared their adoption of the platform. Adding to its popularity, it has been declared as the most popular platform for any successful practicing Data Scientist. As its popularity increases, there are a lot of institutes offering certification courses in this data analytics tool. Imarticus Learning is a leading education institute, offering industry-endorsed courses in R Programming, in both classroom and online format.
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