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Category: Analytics

R or SAS – What is Beneficial for a Data Scientist?

Posted on by Imarticus Learning

R or SAS – What is Beneficial for a Data Scientist?

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, conducting statistical and mathematical analyses, for research in operations and project management. SAS is one of the oldest and most trusted programming tools used by big global corporate, especially in the field of finance. Some reputed companies that use SAS are Barclays, HSBC, PNB Paribas, and 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 visualising 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.

How Much Does Machine Learning Matter in Data Science?

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How Much Does Machine Learning Matter in Data Science?

Data Science and Machine Learning are mostly used synonymously; most people also believe one is a trendy word for another.
Data Science is in some sense an umbrella of techniques used to extract information and get better insights into the available data. The range of this type of analysis varies from something as elementary as MIS reports on the one hand and on the other, an intense scientific approach where techniques such as getting inferential analysis, predictive analysis, descriptive analysis, exploratory analysis and so on are considered.
Machine Learning can be explained as an essential part of Artificial Intelligence. Machine Learning empowers the computers to get into a self-learning mode, eliminating the need for overt programming. With the help of new data being fed into the system, these computers can then learn information, adapt to the required changes, and learn and develop all by themselves. They are not human dependent for improvement. Automation of the later part of data mining can be called as Machine Learning.
Machine Learning is not a new term, it has been around for a while, some common applications include, web search, spam filters, credit scoring, online recommendation engines, cyber fraud detection or some advance recent development like the automated google car, however, the ability to automatically learn and develop and apply mathematical calculations to the big data is only currently getting impetus.

Why does Machine Learning matter?

Like all fields which aid development, Machine Learning is also constantly evolving. And as a custom approach to development comes the rise in importance and the demand. One can say Machine Learning is imperative to Data scientists as it helps them drive high-value predictions that can help arrive at better decisions and help take the right actions most importantly in real time, to be effective, and to do this with as minimal human intervention as possible. It eases the task of the data scientist in an automated process and hence is gaining a lot of importance.
Availability of massive data increases the difficulty in analysing it, hence increase in data is directly proportionate to the problems associated with bringing in predictive models that work appropriately. You see a statistical analysis is limited to understanding samples that are static, as a result with time it could give inaccurate conclusions or solutions.
As a knight in shining armour enters Machine Learning which is able to give good solutions to analysing the data in huge volumes. Machine Learning is a leap forward from other available applications like statistics, computer science, etc.., Machine Learning will help produce real results and analysis through the development of effective and efficient algorithms and data-driven models for real-time processing of data.
Machine learning and Data Science will be partners working together. This is the ability of the machine to gain knowledge from data, so without the data, there is very little that machines can learn to do. Thus, it gives a push to get valuable data in order to get valuable and accurate solutions or predictions. So the increased use of machine learning will act as a catalyst to give higher importance to data science. In future, basic levels of machine learning will become a standard operating for a data scientist.
Related Article : What’s Machine Learning all about?

Benefits of Big Data Analytics Training and Certification Program

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Benefits of Big Data Analytics Training and Certification Program

It’s no longer a question of whether an association needs a Big Data technique. It’s an issue of how soon they grasp it. IT experts are scrambling to get certified in Big Data or Hadoop, which is relied upon to end up basically the most heated tech expertise in the following couple of years. Huge Data is gradually receiving noticeable interest known everywhere throughout the world, as organisations’ overall verticals like utilities, retail, media, pharmaceuticals, vitality, and others are grasping the most current IT idea. This is also the reason why Big Data training and certifications in big data have become so popular in recent years.

According to some current techniques, numerous associations did not have the capacity to meet the requests of the client because of the unpredictability of information broken down and prepare the information. To dodge these sorts of issues, Organisations are actualizing Big Data innovations. Overall areas of the world, 53% of the 1,217 organisations had embraced no less than one Big Data activity.
Why Big Data Certification?
The truth of the matter is, organisations are attempting to get Hadoop ability. Ventures embracing Hadoop need to be guaranteed that individuals they contract can deal with the petabytes of Big Data. The testament is proof in such manner, making a man in charge of the information.
The following happen to be the benefits of big data training, especially with Hadoop:

  • Taking after a portion of the regular points of interest Big Data affirmation offers.
  • HR managers and HR groups are chasing for aspirants having big data and Hadoop certifications. It’s an unequivocally preferred standpoint over those having no affirmation.
  • Huge information accreditation gives an edge over different experts, in regards to the compensation bundle.
  • Hadoop, as well as big data certification, helps an individual quicken vocation development amid the inside employment posting process.
  • One of the real points of interest Big Data certification gives is that it is useful for those attempting to change over to Hadoop from other specialised foundations.
  • Hadoop certification underwrites hands-on understanding of working with Big Data.
  • Confirms that an expert knows about the most recent Hadoop highlights.
  • The big data certification helps in talking all the more unquestionably about the innovation to the organisation while organising with others.

While the above are the general benefits of anyone who happens to have pursued a big data training or certification. The biggest benefit of all would most definitely be the salary packages that some of the expert Data scientists receive these days. As the world becomes increasingly data-driven, organisations of various stature have begun to depend on these big data magicians, to create their magic with numbers and help their respective firm’s progress.
Now for the important question. Where does a data aspirant go to get a certification in Big Data? Imarticus Learning happens to be the best in class, especially when it comes to certification in Big data courses that are thoroughly industry-endorsed.
The program includes comprehensive coverage of Big Data trends, HDFS architecture, MapReduce concepts, Query tools like Hive and Pig, data loading tools and several advanced Hadoop concepts, all taught by experienced industry professionals who have 15+ years of experience in this domain.
Our program is aligned to meet the needs of the industry and the focus is always on job readiness rather than being excessively academic. The curriculum and learning methodology is designed and vetted by our Analytics Advisory Council which features senior management from top Analytics firms to ensure effective learning. You will also have periodic guest lectures with industry professionals to help gain new perspectives and broaden your horizon.

Read more…
The Potential Of Big Data and Analytics
Reasons Why You Should be Studying Hadoop and Python
Who Can Learn Big Data and Hadoop?

The Emergence of Data Engineers & Data Scientist

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The Emergence of Data Engineers & Data Scientists

Data engineering and data scientist are job titles which might be new to us in recent times, however, these roles have been around for a while. Traditionally, anyone who would analyse data would be called a Data Analyst, and the person responsible for creating platforms to support the analysis is a Business Developer.
In the world of IT, the data scientist gets more visibility and praise, as they are the ones, extracting vital intelligence from big data and helping organisations make critical decisions with regard to their business swiftly. But it is important to note that the data scientist does not work in isolation, they are not capable of generating valuable information independently, and they need the constant support of Data Engineers. The engineers are the ones designing and maintaining software and platforms that operate the big data pipeline. They set the stage and keep it running.
A Data Engineer is essentially a software engineer with a decent understanding of math and statistics, he should be skilled in data warehousing, designing, data collection transformation and programming. Have the ability to transform and load operations, build data warehousing solutions, and test database architecture. He should be hands on with tools like Hadoop, Spark, Javascript etc.…,
A Data Scientist is someone who is an excellent statistician, with above-average software engineering skills. Should be primarily inquisitive, and have the skills of data visualisation and storytelling along with programming skills. His tasks would essentially be to identify the question and find answers through data, find a correlation between dissimilar data, to be able to tell the findings, hence storytelling ability, and lastly should be hands-on with tools like Julia, Python Programming, data visualisation tools like Qlik view or Tableau.
The description of Data Engineers and Data scientists can be quite obscure, there is an overlap. While these roles still maintain to be distinct data science job roles, they require different skills and experience. Some data scientists can do data engineering, while some data engineers can do data analysis and visualisation as well.
The emergence of big data has opened space for new titles and roles to come into existence. Over the past couple of years, businesses have applied all means to get individuals who have the skills to turn data into gold.
A lot has changed in the way businesses function, earlier a lot of companies were functioning in the physical world, nowadays most businesses function on the digital platform. When a company is mostly functioning online, there is a huge accumulation of data. Data about who is visiting your website, if they are choosing your competitor’s website as opposed to yours, and what could be the reason, you also get data about the statistics of the competitor’s target audience, So the possibility of the data accumulation is too big and very fast. The data are screaming information and is noisy beyond comprehension.

In order to find a way in this data, one needs to sort this in two ways,
Firstly, to create a database to process the data and to store it and the second would be the need of people to comprehend the data and know-how to ask relevant questions and research the data in a method that the concerned business can take informed pointers from it. This stored data needs people who know statistics and know how to write code, in order to get insightful information.
Data scientists and Data Engineers are these people; they are the need of the hour. To know how to process data using various platforms, and more importantly, we need them to be around, These people also know how to make sense of the information, and how to analyse it. They don’t only plot graphs from data collected from a spreadsheet but also create statistical models that over a period of time affect the business and products with effective ways to increase the revenue.
The data available could be stand but smart and appropriately skilled people are the ones who help find that needle in the haystack.

Read more…
Having Technical Knowledge Is Not Enough For Data Scientists
Five Reasons Why Big Data is The Right Career Move for you…
The Future of India in the Field of Big Data Analytics

Why Learning Financial Modelling is a Great Decision?

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Why Learning Financial Modelling is a Great Decision?

Do you ever wonder what does a Financial Modeller do? Some of you may aspire to succeed as Financial Modellers. So today, let us learn about Financial Modelling and Valuation. 

The global economy is ever-evolving, so it’s good to be on top of your game, a step ahead of the others. Get a career boost by learning financial modelling. It will jumpstart your career in incredible ways.

Learn the concepts and tools required to get an edge in the ultra-competitive job market. Financial modelling is one of the most sought-after skills in today’s corporate world.

What is Financial Modelling?

Financial modelling analyses the company’s performance on relevant economic factors.

The analyst intends to forecast an organisation’s capability in potential earnings.

 Various theories exist. A financial model is a spreadsheet created in Microsoft Excel. It projects the future financial performance of a company. It usually captures all variables for a particular event. Financial analysts use financial models to understand the company’s performance. This helps them to predict its future.

Building a financial model is a prerequisite for jobs in investment banking.

One might be a business school graduate, and one could understand financial modelling. But there is a gap in learning financial modelling and application. Most of the learning in B-schools is not relevant to the latest developments. 

 So if you are having experience, you might know what to do; yet, you would not know how to do it right with the best technique. For example, you might know about cash, debt paydown, and ways a company can raise revenue. But what big companies want you to know is to determine the fair stock price of a company given all their financial statements.

Learn Financial Modelling the Right Way

When you understand financial modelling you will understand the company’s fundamentals. 

In recent years financial modelling has become integral for career advancement. Also, most corporate finance roles need knowledge of financial modelling. This means if you know financial modelling, it also opens many career choices for you.

The reason it is so multipurpose is that it assists in any job role involving analysing. Few people know how to build a financial model. Hence, a specialised course will give you an advantage over others.

A financial modelling course is for anyone pursuing an MBA. Also, those who have done their CA, CFA, or plan to, as it will add to the theoretical learning in a practical way. Additionally, working professionals will get an in-depth understanding and an edge over others. 

Imarticus Learning has created a Financial Modelling program for careers in Corporate Finance. It helps develop a fast-paced career path, which is rewarding. 

Contact us if you have any questions about the batches or investments needed to enrol in this course. They would be more than happy to guide you at each step. We hope you found this blog to be quite enlightening. Are you prepared to develop your abilities and become a Certified Financial Modeller?

 

Five Reasons Why Big Data is The Right Career Move For You

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Five Reasons Why Big Data is The Right Career Move For You

You cannot achieve tomorrow’s results using yesterday’s methods, and this line is the need to understand and accept the impact of BIG DATA and the growing demands of the business.
Big data means access to big information, leading to abilities in doing things you could not do before.
Case in point – a small exercise…
Before you read ahead, check the posture you are sitting in, now look around and check the posture of others sitting beside you, observe… what do you see? Someone is slouching. Someone has their legs crossed, elbows resting on the armchair, palms pressed against their chins, constant moving of the limbs. Not everyone sitting around you has the same posture. What if we put many sensors on the chairs around you to help record and create an index, which is unique to you, which says person X sits in these particular postures during specific intervals of the day?
In addition, say what if this data was sold to car manufacturers as an anti-theft design to help recognise that the person sitting behind the wheel is not the same and say until he keys in a password the car even though entered would not start.
Now imagine what if every single car in the world had this technology, think of the benefits of aggregating this data. Maybe we would be able to predict which postures while driving would lead to an accident say in something as close as the next 7 seconds do, and alert the driver to change position or take a break from driving.
Now my dear reader THAT is the power of BIG DATA. It can help record, collect, understand, predict, and prevent events from a random collection of information.
So if that is so useful where lies the problem? Why is the world not already a better place?
Because there is a gap between opportunity and demand in skilled professionals to help comprehend and present valuable insights into specific relevant trends, big or small from the available data.
Big data is all around us and there is a calling need to preserve all the generated data for the fear of missing something that could be important. Hence, comes the need for big data Analytics, Big data is crucial to do better business, to take accurate decisions and to always be a step ahead of your competitors. Therefore, if you are a professional in the Analytics domain there is a sea of opportunities waiting for you to dive in.
Big data Analytics is Unfathomable and depending on the environment one can choose from
Prescriptive Analytics
Descriptive Analytics
Predictive Analytics
The Big Data Analytics market is predicted to surpass $125 billion between 2015 – 2020, which in turn in some sense means handsome pay brackets for the skilled individual.

Salary Aspect

To improve the performance of the organisation, most companies have either already implemented or are in the process of implementing Big Data Analytics, as they already have the data at their disposal.

All Organisations adopting some form of Data Analytics = Growing Market

There is a big gap in skilled professionals who are able to convert the data available. The ability to see small trends from the pool of big information. Which ultimately advances the organisation in the right direction. There are two types of talent deficits. Data consultants – who have the ability to not only, understand but also use the data at hand in the appropriate way and the other is Data Scientists who can perform analytics.

Skill Deficit

Big data analytics is not restricted to any specific Domain; it can be and in recent times is being used in Healthcare, Automobiles, Manufacturing and more, creating a massive global demand.

Global demand across industries

Analytics becomes a competitive resource for organisations, according to certain studies Analytics has already become the most important asset in current times. Because we are emerging from the undeveloped analytics trends to more advanced forms. It is undeniable that Analytics plays a vital role in decision-making and taking strategic initiatives for the business.

Importance of Analytics for Better Decision Making in Organisation

So in deduction to the above, Analytics no matter how advanced is human-dependent. These are exciting times for skilled people who can comprehend data and give valuable insights from the business point of view. A trained individual with the right Analytics insight can master an ocean of big data and become an indispensable asset to the organisation becoming a springboard to the business and their career.

 

 

Who Can Learn Big Data and Hadoop?

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Who Can Learn Big Data and Hadoop?

Did you know that top tech firms like IBM, Microsoft, and Oracle have all successfully incorporated Hadoop, as their software programming environments last year? While this may be definitely enlightening, these aren’t the only firms vying for professionals with expertise in Hadoop.

Some of the other big names looking for Hadoop professionals are Amazon, eBay, Yahoo, Hortonworks, Facebook, and so on. No wonder professional training institutes like Imarticus Learning, which provides excellent, comprehensive training courses in Hadoop are becoming well sought after lately.

This may be because Hadoop happens to be among the top ten job trends in the current time period.

While anyone who is an intelligent technologist, can very easily pick up the skills for Hadoop, there happen to be certain pre-requisites that a candidate must fulfill. While there happens to be no hard and fast rule about knowing certain tricks very well, but it is kind of mandatory for a candidate to at least know the workings of Java and Linux.

This does not mean an end of the career, for those who aren’t well versed in this programming software. A candidate can very well be a multitasker and learn Big Data and Hadoop, at the same time spending a few hours in learning the ropes of both Java and Linux. While knowing Java is not strictly necessary, but helps you gain a profitable edge over your contemporaries and colleagues.

There happen to a few tools like Hive or Pig, which are built on top of Hadoop and they happen to offer their very own language in order to work with data clusters. For instance, if a candidate wishes to write their own MapReduce code, then they can do so in any programming language like Python, Perl, Ruby or C. The only requisite here is that the language has to support reading from standard input and writing to standard output, both with Hadoop Streaming.

There also happen to be high-level abstractions, which are provided by the Apache frameworks association, like the aforementioned Pig and Hive. These programs can be automatically converted to MapReduce programs in Java.

On the other hand, there are a number of advantages to learning Java. On one hand, while you can reduce the functions in your language, but there are some advanced features that are only available via the Java API. Often a professional would be required to go in deep into the Hadoop code, to find out why a particular application is behaving a certain way or to find out more about a certain module. In both of these scenarios, knowledge of Java comes in really handy.

There are a number of career opportunities in the field of Hadoop, from roles like Architecture, Developers, Testers, Linux/Network/Hardware Administrator, and so on. Some of these roles do require explicit knowledge about Java, while others don’t. To be considered an expert in Hadoop, and be recognized as a good data analytics professional, you must learn the inner workings of Java.

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Salary Trends in Big Data Industry

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Salary Trends in Big Data Industry

Big Data Industry has become the modern equivalent of the hot cross buns of the mid ages. This industry acts drives people like a moth to a flame, especially those in the field of information technology. If you happen to be one such enamoured individual then as a part of being a data aspirant, you are sure to have a number of questions regarding the field. Your questions will range from any of the following:

  • What salary should one expect in this field?
  • What are the skills that a person needs to acquire in order to get an entry in this industry?
  • What are the many locations where the most amount of opportunities make up a huge chunk?

So if you do happen to have these many questions and if you are shaking your head vigorously in appreciation, then this very article is for you. Read on to dispel all of these questions and find the most proper answers to them.
Recently a very esteemed and astute industry report was released, which spoke about all the latest trends and the insights in the field of big data science, including which tools are very much in demand, to what kind of salary is drawn by some of the most famous of professions and positions.
So far as the report goes, machine learning happens to one of the skills that takes away the cake. It is undoubtedly the best paying skill on the market. At the same time if an individual also happens to have perfect big data analytical skills, then the combination is a lethal one in terms of securing a high paying job. As this trends seems to be one which will have a sizable impact on the future, many are recommended to take note of it.
Earlier days were replete with the rule of licensed data analytical tools like SAS ruling the roost, but today it is the open source analytical tools like Hadoop, R Programming and so on that are gradually coming to power. Tools like Python and R Programming have effectively replaced SAS as the key player and ensure more pay to those with expertise in these tools. Investing more time in these and getting trained is a great choice to follow.
software poll
Another trend that is rapidly being taken over by various big guns of the industry like Google is hiring of candidates who have dual expertise in the data analytical tools like SAS and Python, R and Python and so on. Mumbai continues to be the city where data analysts are paid the most, which is followed by Bengaluru and Delhi. Increment and promotions are usually dependent on your educational background or the fact that you’ve done some course or the other. This is why many individuals today have begun to opt for professional training courses in various data analytical tools which are offered by institutes like Imarticus Learning. These courses help them become entirely industry endorsed and jumpstart their careers.

The Promise of AI: Application in Education and Health Care Sector

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The field of Artificial Intelligence is experiencing great advances, both rapidly and on a large scale. The various fields that have successful applications of AI to make them better are namely the accessibility sector, agriculture, business operations, consumer convenience, disaster prevention, response to disasters, education, energy, environment, health care, prevention and screening for any health discrepancies as well as, treatment and monitoring of the same, industrial operations, public safety, social good, transportation and so on. All of these fields have enhanced applications of AI presently and will have more in terms of the future as well. But the two fields here of greater importance would be health care and education as they happen to be determinants of the global development. This is why this article will be talking about the various applications in the above mentioned fields.
Let’s begin with education. One of the very primary applications of AI would be dealing with personalization of the maths. Let’s take for instance the IBM, which seems to have developed a new age tool known as the Teacher Advisor. Based on the Watson cognitive computing platform, this tool will reportedly help all the ninth grade math teachers. It would help in developing personalized lesson plans, by analysing common core education standards and student data. This way it will be able to assist the teachers in tailor making personalized instructional material, which would be based on the intelligence levels of the particular students. Other various applications of AI will possibly involve the method of predicting which students would be potential drop outs in particular years, or transforming teacher assistants in automated machines. Artificial Intelligence would seemingly make it easier for all the students to learn new languages as well as, would improve them by providing feedback in real time.

Data Science Course
Let’s move on to health care, the various applications of AI here would include, prevention of loss of vision in Diabetes patients, analysing speech in order to predict Schizophrenia, successfully figuring out how to prevent pancreatic cancer, transforming a microscope in order to diagnose malaria, developing diagnosing techniques in order to diagnose voice disorders and so on. Other applications would be helping of all the diabetes patients in taking smart decisions, streamlining the discovery of drugs, developing robot surgeons in order to make stitches even safer, using artificial intelligence in order to speed the recovery stages of a patient, as well as to speed radiotherapy and lastly increasing participation of people in Clinical Trials, as it would be safer and side effect free.
AI has already begun its work especially in the health sector as a certain stat-up has developed an app, exclusively for patients with Type 2 diabetes. This app basically analyses all the medical records of the patient and then goes on to make personalized recommendations about alterations in their diet, which would help them deal better with their disease. Such developments would ensure a better, healthy and disease free tomorrow for our entire world. These developments have urged many to take up a career in this field. This is why we see a number of data aspirants pursuing professional training courses offered by Imarticus Learning.

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The Promise of AI: The Value of Artificial Intelligence and its Applications

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Read the previous part here.
When it comes to predicting the value of AI, things can get a little vague and confusing. This is mainly because of the wide applications as well as the rapid evolvement of Artificial Intelligence. It is estimated by the Data Corporation, that the market for technologies of or related to Artificial Intelligence, is bound to increase up to $40 billion by the year 2020. While this market would deal with all those technologies that help in analysing unstructured data, it is believed that it will be generating productive improvements, which promise to be well over $60 billion worth of the United States market per year.
Consequently, a majority of investors have begun to discover and realize the true value of AI and as a result of this, more and more investments have begun to take place in the venture capitalist markets. Let’s talk about figures, the year 2013 saw about $757 million, being invested in AI start-ups, 2014 saw about $2.8 billion, 2015 saw around $2.3 billion and if these figures are anything but telling, it is guaranteed that the investments will only be growing in proportion for the coming years. The Mckinsey Global Institute has estimated that all the automating knowledge work, with Artificial Intelligence, is bound to generate anything around $5.2 trillion to close to $7 trillion. It is believed that the advancement in advanced robotics by primarily relying on Artificial Intelligence will generate anything up to $2 trillion. All of these estimates are made for up to the year 2025, which is barely a decade away.
Thus, we can conclude that the value of Artificial Intelligence is not only humongous, but it bound to increase manifold soon enough. It is believed by many experts that the AI has so much power, that it can go ahead to solve global issues like climate change and food insecurity. Artificial Intelligence already has and is bound to have a varied number of applications. Mapping poverty with satellite data, measuring literacy rates, cracking down on human trafficking rackets, preventing abusive internet tools from taking up actions are some of the social applications of AI that will definitely benefit the society in the near future.
When it comes to public safety, artificial intelligence could very well be applied for pinpointing the various happenings during crimes, for prediction of crime hotspots, for disposing of car bombs autonomously, for predicting the fire or earthquake risks of building accurately, for ensuring that every type of security screening is less invasive and so on. Industries would benefit majorly from AI as it would ensure the prevention of breakdowns or power cuts before they even take place, everything from intelligent manufacturing to automated factories could be very possible, improvement in terms of the dairy supply chains with the help of market forecasting would be a real thing and much more.
Thus AI shows a lot of promise in terms of changing the world for the better tomorrow and as a result of this, it would make a great career. There are many institutes in India like Imarticus Learning, which offer a number of comprehensive courses in Data Science, that would help an individual further their career in AI.

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