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Tag: Data Analytics certification

What Are The Analytics Apps To Reduce Operational Costs At Production Facilities?

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What Are The Analytics Apps To Reduce Operational Costs At Production Facilities?

Introduction

Digitization has taken over the world with a storm. With numerous benefits, it has proved to be a boon for all sectors in which data is involved. Data analytics is shaping the world in which businesses are evolving. It makes data much more than stored figures. It brings out various trends and patterns of different segments of society. Data analytics courses can help businesses increase their revenues in multiples, optimize costs, Improve efficiencies, make cumbersome business processes much more simple and also using data in ways one could have never imagined.

Applications serving the purpose

Mobile applications have increased the use of data analytics. People use their mobile phones all the time thus making it easy for businesses to acquire a lot of individual-specific data daily. The app-space is huge and is coming up with innovations every second. Not a single area has remained untouched with this huge growth. From Real Estate to food, everything has analytics involved. This innovation has not left production facilities untouched. Analytics is making work at production facilities a cakewalk. Some of the major applications which have their hand in making work easy at production facilities are listed below.

  1. Dozuki

Dozuki is a standard work software that has changed the way the production space operates. Dozuki is dominating the visual learning platform by analyzing various trends and making videos on work instructions like repairing a car, opening a refrigerator, operating a piece of particular equipment, etc. This app makes use of images and video clips that are interactive and translates them into work steps and procedures. It is a very convenient application with a simple user interface. It is packed with everything you are looking for in a production-based application. It removes the complexities of a classroom training program where one can easily forget the basics of a particular procedure. The feedback option in the application is quite effective and the users can give their inputs on how they want their procedure videos.

  • Limble CMMS

The machine count in a production facility is huge. To manage all of the machines together is quite a difficult task. It is a maintenance software that a production facility needs. It monitors machines, equipment, spares and every other aspect which comes under such facilities. It keeps the machines running smoothly. Also, it optimizes the manpower cost which is involved in operating such giant facilities. This application is also rich in video content giving relevant information on manufacturing processes such as extraction, polishing, etc. It also has a simple and easy to use interface. It keeps a constant tap on the performance and productivity of machines. Such kind of applications also helps people to keep a tap on the physical maintenance of machines thus saving a huge cost which production facilities would have incurred on a sudden breakdown of any equipment.

  • Microsoft Power BI

This is a powerful analytics tool that can give insights on every aspect of a production facility. It can compile a huge amount of data on different processes and bring out relevant conclusions and process rectifications. The visuals are easy to understand and provides an in-depth understanding of various processes. It can automate the whole process by running various reports and publishing them in pre-decided time frames.

It removes the complexities of manual processes and makes production an error-free and smooth experience. This application has multi-fold benefits. It saves time and generates reports which can be easily deciphered by the people working in production facilities. Various metrics can be formed and tasks can be monitored with the help of automatic alerts. This helps people on maximizing the returns from the production function. In the end, everything is about optimizing costs and increasing revenues.

Conclusion

With every passing day, data is becoming a part of our lives. Analytics is shaping the whole world into a data-driven economy. With its increasing benefits, it is elbowing its way into all sectors and businesses. The production business is squeezing out everything it can and making the best use of data analytics in its day to day functioning and will eventually make itself a data-driven function that works on digitization and analytics.

How ISPs Are Using Analytics To Help Customers?

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The world is driven by big data

Big data is everywhere. Businesses of all tastes and flavours are getting their hands on this mega resource to make their businesses competitive and also to gain useful insights that can shoot their operations to the zenith of success. Big data is loaded with multiple benefits. It churns up the data to bring out conclusions hence, giving a huge helping hand in making vital business decisions.

What are ISPs?

ISPs are nothing but people you need for the smooth functioning of your daily necessity: The Internet. Internet service providers are companies that provide internet services across various geographical locations so that people can access and use the web. This market is dominated by both large and small local players. The big players have the internet lines managed independently by them. The big players of this market are companies like AT&T, Netcom, MCI, etc.  These days a new group known as the online service providers has also come into the picture. They manage all of their operations through online mode.

ISPs and data analytics:

Internet service providers like any other sector are utilizing the applications of big data and making the most of it. Big data has become a profit-driving point for these companies. With the help of big data, the geographical boundaries are analyzed and then the reach of the internet and strength of signals and network concerns are also brought into the picture.

Analysis of data is done based on these parameters and then the expansion of business is carried on after several tests on feasibility, accessibility, etc. These providers use online advertisements to advertise their services, hence collect information based on clicks on the ads, their page staying time and their page abandonment time.

ISPs are using big data to have access to a gold mine full of data. Using analytics, possible customers are tracked down and then presented with an ad based on their individual preferences and search histories. These ads are curated automatically with the application of analytics. This data also helps in providing better customer experiences.

These service providers with the help of big data overcome the bottlenecks of Internet services such as network speeds, connectivity concerns, etc. Also, big data helps inefficient capacity planning and brings out operational excellence. Such mechanisms help these providers to foresee any upcoming issues and glitches which can make the users compromise on their user experience hence, solving it beforehand and providing a buttery smooth surfing experience.

ISPs are providing a next-level user experience with the application of big data and analytics. ISPs are using big data to mitigate a lot of problems and extracting the best out of what is available. The area which is compromised on a little is data security. Though the services providers are extracting out all the goodness of big data and giving an upgraded the internet experience to its users the data entered by its users are on huge servers floating, ready to be pounded on.

Data security is one of the biggest concerns which is bothering individual users. Users regularly feed information of personal importance and relevance to their smartphones and laptops. The data thus entered has no specific, safe place to go. It wanders in an unrestricted environment which can be extracted by anyone.

This problem is being tackled by big data to a certain extent but is being worked on so that the improvements can be made at a really quick pace in the area of data security and privacy of the users. Only data can protect data. Thus, these gaps are being filled up gradually by exploring more and more avenues of big data in this dynamic sector.

Conclusion

Internet service providers and big data are moving hand in hand. Both of them together are capturing consumer behaviour and extracting information out of it at an all-new level. Though there are certain limitations the ISPs and data analytics courses are jointly brought into force to eliminate such hindrances and emerge out with more upgrades. ISPs are sailing quickly and analytics is the captain of the boat driving it towards the island of growth dancing along the waves.

How Big Data Can Help Boost Sales?

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How Big Data Can Help Boost Sales?

Selling in general

Sales form the backbone of any organization. You need the revenue to soar up in order to quench up your profit maximization quest, that is what every capitalist aspires to do. No matter how good is the product or the services of a company, it will only grow the bottom line if it gets a good top line, i.e. sales.

Ever wondered how do salespeople close the deal and how do they manage to convert clients on the basis of leads? Well, the process is long and complex, it needs a lot of dedication and commitment to stay at the top of the game. If you ask a salesperson how did they manage to work wonders with a mere lead, the general response will be that they identified the needs of the customer and aligned it with the benefits provides by the products/ services they were selling.

A good salesperson knows the customer inside out and for that, you need to spend a considerable amount of time and money tracking the whereabouts of your customers and maintaining a personal relationship to some extent. You have to be a detail-oriented person who knows the nitty-gritty of things and is able to use the information to your benefit.

Sales in times of the big data

Sales in the times of the data revolution have changed drastically and more in favour of the salespeople easing their work with technology and insights. The above-mentioned approach to sales was commonplace until big data became the next big thing and technology evolved to complement this transition.

In the present scenario, whatever you need to know about your customer is available at your fingertips, thanks to big data. Name, number, email, address, etc. have become very generic and no efforts are required to gather the same. More advanced data about particular preferences, be it food habits or their personal hobbies everything and anything is available for those who use the World Wide Web and smartphones.

It doesn’t matter how insignificant a piece of information is, big data stores everything it can. Mostly the insignificant information about individuals combines to form a meaningful database that helps to obtain statistical inferences about a hypothesis.

For salespeople the big data is like a personal genie, they don’t need to wait long hours for meetings that might not even be relevant at the slightest in hope of conversion. Big data helps to boost sales by allowing salespeople to act upon the customer data available.

Big data Analytics Courses help salespeople by providing the required database to analyze past data and optimize their pricing given the demand and supply of the product or the services. It also aids in providing better visualization of the data to put in the right context. Other uses of big data in sales can be understood by the compelling case studies of the consumer.

What’s necessary for better conversion? Well, a good marketing stint of course. Marketing has evolved drastically over the past decade, with big data marketing that has eased the pain for salespeople through better targeting and advertising of the product and services offered.

From making predictions about what can sell well to designing the products that the existing customers need, big data helps a whole lot to make sales easier and more predictable than ever. Big data is also used to shape customer preferences.

The big data serves a special purpose for companies that are starting up and early in the process, it helps the companies to analyze data generated by the peers in the industry who already have been there for a significant amount of time and then helps to identify the loopholes and design their products and services better.

Conclusion

Sales are the backbone of every organization. You need revenue and profits to continue and grow your business empire, most of the expenditure related to operations is financed by the revenue generated through sales. In today’s scenario, big data plays a crucial role in increasing sales and in turn profitability of an organization. It helps the salespeople to target the right customer and design products more aligned with the needs of regular customers through its data insights.

How Can Data Analytics Help Insurance Companies Perform Better?

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It is the question that has already been asked after it was on everybody’s mind for a long time. How can big data help insurance companies – a heavily regulated sector in India and everywhere else in the world – and make them perform better? Especially when it comes to preventing frauds, system gaming, and other illegal activities that are prevalent.
With the competition only rising in the insurance sector, what company makes headway in properly using data analytics and acts a role model remains to be seen. So, in order for us spectators to do that, we will need more information.
How exactly can analytics help insurance companies serve their customers better? There are four major ways.

Use of Data Analytics in the Insurance Sector

Apart from gaining customer insights and helping in risk management, data analytics training can also help understand if it’s worth handing out an insurance policy to a person based on his social stature. How does his social media presence look like? What are his hobbies and adventurous choices? Has he lied in his application? All of this can also be extracted through the proper use of data capturing and analytics tools.
It seems extremely lucrative for the companies but it also poses a risk for us customers who stare at a possible invasion of our personal space and privacy. Having our social media accounts stalked by HR professionals for the purpose of employment is one thing (not a decent task, nonetheless). Having strangers do the same so that they can deny you insurance – which let us remind you is a basic necessity in today’s times – is a big event. It is not to say that this will be the majority outcome but it is what is on the mind of insurers when they consider big data.
Let us look at those four different ways in which analytics can help insurers:

Managing Claims

This is by far the biggest reason why insurers are pushing for use of predictive analytics in the sector. As you can imagine, it can help companies create a database of customer information that can then be used to compare new policy buyers and see if they fall in a bracket of people who might commit fraud such as wrongly filing for a claim.
The insurer can feed the model with past data and then use it to classify its new customers. Since the approval or rejection of a file is more or less under the authority of the insurer, this can help them denying insurance to a possible fraudulent applicant.

Generating Claims Based on Data

This involves checking the profile of a person while she applies for insurance. For example, in the case of house insurance, data can help insurers understand if this specific house is vulnerable to natural incidents; is it closer to the fire station; what is the history of the locality for the past twenty years as far as mishaps are concerned. When we talk about data, there is a lot of scopes.
And when it’s time to act, this collection of data can be extremely helpful to weed out fraudulent applications and other types of scams. It can also help them set better premiums if denying insurance is not an option.

Better Customer Support

Have you ever been in a situation where you have had to get your call to the customer care rerouted a couple of times before you finally got your problem heard? The call first moves to the respective section of the insurance (example: car insurance versus medical insurance), then it goes to the redressal section, and then finally you get someone on the other line to speak. It is extremely annoying for a customer. Even more so when she is in a situation where she needs urgent medical insurance support.
Big data can assist in this process by automatically understanding the issue of a caller and routing it to the respective section. This is possible based on the preliminary details that the customer has to fill in. The analytical model which is attached to the database of policies can better bridge the gap so that the customer gets her information quickly.
On the other hand, this can also help insurers keep a track on a particular customer. How many times in the past five years has she filed for insurance? What does her lifestyle look like now compared to when she bought it? This last piece of information can aid in guiding her should she decide to buy another policy with the same company.

Offering New Services

According to IBM, data analytics can also provide insurers with tools to market new products based on their requirements. Today, retargeting techniques and cold calling are used to push products to customers, but when companies have valuable data in their hand, they can easily club it with their marketing and advertising and even sales departments to better retain customers and make them buy more products.
This will require a lot of integration on the part of insurers, but the current market and the high competition say that companies will be willing to take the jump if they see there’s any scope to grow their customer base and tackle the menace of continued competition.
According to us, newer companies will be more desperate to try these systems out than incumbent ones that have functioned in the same way for years and even decades.
While we have talked about the scope in general tone, it makes sense to understand what specific tools will be of most use. Out of this, content analytics, discovery and exploration capabilities, predictive analytics, Hadoop, and Stream computing are some essential models that will pave the way forward for insurance companies.
Of course, all of this cannot be switched on one fine morning without the approval of IRDAI. The regulatory body is yet to come up with proper guidelines, and insurers will need to abide by those rules before they can start executing them.

How To Encourage Your Children To Learn About Big Data And Modern Technologies?

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How To Encourage Your Children To Learn About Big Data And Modern Technologies?

Phrases like Deep Learning, Neural networks, Machine Learning or Artificial Intelligence can be a big put-off for those parents who get easily overwhelmed by the changes in digital technology which seems to change minute-by-minute. The exponential growth of data is powering it and big data analytics courses are fast becoming essential.

This is what your children inherit and grow up in. It is crucial to have them trained early on if they need to be technologically equipped to handle their daily lives and become contributors to the growth of both the economy and society at large. There is no dearth of the ignorant in places of power who have no clue regarding the present technology let alone the future technologies that are already happening!

Every country has its share of shame and court cases on the misuse of technology which stems for a complete lack of understanding of the underlying science and principles of technology. You can change that and we shall look at certain pointers that can help you along to make the future of your kids in big data analytics courses an educated and well-equipped one.

Understanding the basics:

Kids understand concepts very easily if the examples are right. Just as they learn to walk, talk in any language, and interact with others based on their experiences of watching and doing, so also complicated concepts are simpler to explain than you may think. After all, technology has existed over generations and it is those who learned to question early that became the next generation’s Einstien or Newton.

Your involvement is vital:

One of the easiest ways to update your knowledge would be to get involved in your child’s learning. Parents are the role model on which the child bases his/her behavior. Taking an interest in the learning data analysis and how modern technology will not only help you explain the simple concepts of Deep Learning, Neural networks, Machine Learning or Artificial Intelligence but will also help you understand better and make better choices as technologies advance. Ex: Buying a smartphone today involves understanding what they can do for you with Google Assistant or Amazon’s Alexa. Go ahead and discover your gadgets.

Rewards are motivators:

The simple science of getting kids to thrive in their learning is to create a task list and reward the completion of tasks with simple child-friendly rewards like a party, movie, or treat of their choice. Starting such a system helps them inculcate discipline, cleanliness, and innovation in thinking through their tasks. Rather than play for hours on end, kids find it more interesting to learn to handle gadgets like the computer, smartphones or home theatre. They not only feel grown-up but also start skilling themselves early.

Use authentic training resources:

Teaching children to Google their questions opens up Pandora’s box when un-monitored. However, the internet has some very interesting videos on YouTube, simple beginner’s courses depending on the age of your child, websites for learning kids, games that explain concepts behind what appears complicated technology and child-friendly apps that are invaluable for both you and your kids. Why not consider a few big data analytics courses online?

Learn from mistakes:

Part of the learning lies in its being used and that’s where mistakes are bound to happen. Just like kids fall and learn to walk better, complicated subjects will come with mistakes and errors that should be treated as part of the process. The parent’s role in encouraging and handling rejection due to mistakes is just the same as in subjects like mathematics, science or any other. Just as long as the child enjoys the process and no stress is created they will learn if you are sensible about their failures.

Get Assistance:

Rather than venture into the unknown territory alone, there are ample resources that you can exploit to teach your children such as teachers, tutors, and short-term beginner courses at colleges that can help. Scour your neighbourhood for students who have done big data analytics courses and would be willing to orient your kid for a very reasonable hourly fee while keeping them well-attended to and busy learning something new.

Parting notes:

So, how can proactive parents encourage children to acquire knowledge and skills in big data and modern technologies? Well, the answer is simple. It is all about the training of the mind to form a basic skill set that is curious and learns by itself. At Imarticus Learning you can learn and also enrol your children in professional courses like big data analytics courses that help build appropriate skills in the field of emerging technology. You will be getting them a quick start in their careers that could prove invaluable in time.

The Importance of Big Data Analytics in The Banking and Financial Services Industry

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In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
Today, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security informant data-driven and other financial organizations much simpler.
There are several use cases in which Big Data Analytics has contributed significantly to ensure the effective use of data. This data opens up new and exciting opportunities for customer service that can help defend battlegrounds like payments and open up new service and revenue opportunities.
For example, in October 2106, Lloyds Banking Group had become the first European bank to implement Pindrop’s PhoneprintingTM technology for detecting fraud. Their technology used AI to create an ‘audio fingerprint’ of every call by analyzing over 1300 unique call features – such as location, background noise, number history, and call type – the o highlight unusual activity, and identify potential fraud.
It cracks down on tactics like caller ID spoofing, voice distortion, and social engineering without any need for customers to provide additional information. Subsequently, Lloyds Banking Group went on to win the Gold Award for ‘best risk and fraud management program’ at the European Contact Centre & Customer Service Awards 2017.
Danske Bank uses its in-house start-up, advanced analytics to evaluate customer behavior and determine preferences, as well as to better identify fraud while reducing false positives.
JPMorgan Chase also developed a proprietary Machine Learning algorithm called Contract Intelligence or COiN for analyzing various documentations and extracting important information from them.
Big Data is also used for personalized marketing, which targets customers based on the analysis of their individual buying habits. Here, financial services firms can collect data from customers’ social media profiles to figure out their needs through sentiment analysis and then create a credit risk assessment. This can also help establish an automated, accurate and highly personalized customer support service. Big Data also helps in Human Resources management by implementing incentive optimization, attrition modeling, and salary optimization.
The list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The huge increase in the amount of data to be analyzed and acted upon in the Banking and Financial Sector has made it essential to incorporate increase the implementation of Big Data Analytics.
Knowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Big Data is the next step in ensuring highly personalized and secure banking and financial services to improve customer satisfaction.

Career Opportunity in Data Analytics

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We are in a technology-driven age and are ever managing the growing needs of the companies and consumers with regards to the same. In such a scenario the role of data analysts becomes very perilous to manage the demands. A data analyst is someone who is in charge of collecting and analyzing the data, responsible for performing statistical analysis on the data. It is not essential that the skills of a data analyst are as evolved as a data scientist, a data analyst can or cannot create algorithms. Although they share the same goal of discovering insights from the data and strategically use them to create solutions.
Usually, data scientist works with the IT teams, data scientist or the management, to define organizational goals, data mining, identifying new trends and opportunities, designing and creating databases. Now, these skills come handy when considered as a base to progress in diverse directions in the analytics field.
There are various professional possibilities that can be easily handled by a data analytics professional. If you are a newcomer in this filed, or are trying to explore the field of data analytics, and are wondering about the future options for either career progression, or any alternatives to the analytics job, then this article will help you gauge the opportunities in a Data Analytics field.

Data Management Professional

Affiliated with the role of a database administrator, this role is a possibility but has nothing in common with the data analyst role. One does not need proficiency in programming languages like R or Python. SQL orientation is, however, a plus. This is an IT role, where the person manages data and the infrastructure that manages IT.

Data Engineer

While as a data management professional you will manage data infrastructure, as a data engineer, you will design and implement the data infrastructure. A step up in complexity from the data management professional, a data engineer is a non-analytical big data career opportunity. You cannot say one of the two is superior, it is your knowledge, skill, and preference that should be the deciding factor. Both these roles are similar in the technologies and skills to an extent. However, the application and complexity of the same are different.

Business Analyst

If you thrive when working with big data frameworks, analysis and presentation, creating dashboards, querying of databases is your forte, then this is the perfect career opportunity for you. The above two options will help you manage data and designing data, the role of a business analyst will be extracting information from the data other than what it already says superficially. There are unique skills requirements which can be learned if you wish to pursue in this field.

Machine Learning

Investigating data is the base of a role as a practitioner in machine learning, in addition to this capability you will also need to be hands-on with proficiency in statistics, writing machine learning algorithms, etc…, this is where big data becomes sophisticated, insightful, where tools and experience are used together to leverage data. Therefore, statistics and programming both become essential assets for a machine learning professional, if those are your interests then go for it as machine learning integration in technologies is going to be huge over the next couple of years.

Data Scientist

This term means nothing specific in general but uses all the roles and technologies listed above. From fluency in programming languages to querying and statistical capabilities, to extracting, managing and designing, and conducting initial exploratory analysis, and deciding which machine learning algorithm to use to perform predictive analysis, from visualizing the results to giving the presentation to the management with the end result, all comes under the job responsibilities of this role in addition to having the domain knowledge.
The options mentioned above are only a few of the possibilities but will serve as a good starting point for anyone exploring to understand options available to a data analyst.

What is Meant By Data-Driven Innovation?

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What is meant by data-driven innovation?

Do you know why almost 40 per cent of business models fail every year? The reason behind them is not using data analytics or data-driven innovation. 

Let’s understand what data-driven innovation is.

It is a concept where we use data and analytics to achieve the organisational goal by revamping the process, product and business module etc.  As per Gartner hype cycle 2021, data-driven innovation is the most crucial technique for business growth. Not just for organisations, but data-driven innovation is helpful in the education, social media, healthcare sectors etc. 

What is the role of data analytics in data-driven innovation?

Without analysing the data, data innovation is not possible. So, both data analytics and data innovation go hand in hand. Data-driven innovation can be done after we club and analyse the data.

There are a few types of data innovation. We will name a few here.

Business model data innovation

If your business is not doing up to the mark or you want to grow your organisation, business model data innovation can play a pivotal role. This technique collects the data and analyses it. Businesses then take the necessary information and innovate the business model to improve and raise their growth bars.

Data-driven innovation for products 

Companies can revamp their products after analysing the data from the past. Data analytics is used to interpret the information about the merits or demerits of the products, and then changes can be made accordingly. Specifically, product-based industries or solution design companies can experience the wonders of data-driven innovation. 

Process innovation

Sometimes, everything in the product is good, but the profits are not looking appreciable in the reports. That’s when data is analysed, and data-driven innovation improves the process. These scenarios are prevalent in consumer-centric industries. Their customers like the products but sales were not reaching the targeted numbers. Data-driven innovation comes into the picture here. Data helps to know the leaders where the process is lagging and creating hurdles for the profits. Then, necessary actions can be taken to add or delete the steps in the process, and the problem can be addressed effectively. 

Either company can hire professionals who are good at data analytics to interpret the data and do data innovation, or existing employees can opt for data science and artificial intelligence courses. 

Data-driven innovation is doing wonders in the education sector too. The most popular data-driven strategy is used to add value to the students in the education sector.  

Let’s look at the benefits of data innovation in the education sector.

Customised learning

Students can benefit from artificial intelligence and data science by getting customised learning courses. There are education tech companies that design courses as per the learning needs of the students. Students can take different courses per their learning needs and opt for their desired careers. 

Support teachers and growth of students

Student’s detail can be captured efficiently. Data like the number of lectures, attendance, lesson plan etc., can be recorded and shared with the students and other stakeholders. Students can work on the lagging areas and know their strengths and weaknesses. Data innovation will increase the student’s interest in studies and raise the performance graph.

So, we have understood what data-driven innovation is. How it benefits various sectors, including the educational domain. Therefore our courses in Data Science and Machine learning might interest you. You can learn data science and drill complete information from the data. Remember, data is the new fuel; you can only fill this fuel in the growth by interpreting it to the fullest. 

The Promises of Artificial Intelligence: Introduction

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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.