Why R programming plays crucial role in Machine Learning as a tool

How “R” plays a crucial role as a tool in Machine Learning

Machine learning is a branch of artificial intelligence and you can say it is a branch in computer science which deals with the design of algorithms, statistics that can learn. machine learning is a concept of pattern which basically focus on pattern and works on data and form it in the cluster (i.e collection of similar patterns)

Machine learning is of three types:
1. Supervised learning
2. Unsupervised learning and
3. Reinforcement learning

Supervised learning is basically the learning algorithm is presented with labelled example input where the level indicates the designed output Examples are; Classification and Regression Unsupervised learning no labels are provided and through which the learning algorithm focuses entirely on detecting structures in input unlabeled data. An example is; K means Reinforcement learning perform a task using feedback.

which they got from the environment

This is the small intro which is meant to give you some basic information before going in deep.


  • R is created in the 1990s by Ross Inaka and Robert Gentleman.
  • R language is an open-source platform which makes it highly cost-effective.
  • As we know R is one of the major languages for machine learning language used for statistical calculation, graphical representation of data and to analyse data.
  • According to the survey, most of the data scientists prefer R 49%, 30% prefer SQL and 35% prefer python.
  • R provides you with outstanding visualization features and which is important to analyse the data before acknowledging it to any automated learning.
  • Many packages for machine learning are implemented in R as a part of their development.
  • This is remarkable as R is a specialized language for data analytics.

The IEEE 2016 ranking shows R is the most popular and helpful programming language for data science as we are aware of machine learning is a part of artificial intelligence.

Objectives and prerequisites

  • Our aim is to provide you with knowledge about machine learning methods using R and application in R.
  • This course focuses on the core part which is unsupervised and supervised learning/methods.
  • Students need to download the following software and R packages before moving further Installation part.

R Studio
If you are not aware of this software we will surely go to help you and we will help you to download R packages as well,

  • Students will get to know about R syntax and basic plotting functionality.
  • There are numerous exercises and numerous projects which we will teach you during courses and also gives you hands-on practise with newly acquired material.
  • At the end of the course, students will be able to apply what they have learnt as well as they feel enough confident to explore and apply new methods.

So if you guys are ready to be a data scientist then without any doubt choose this R language because R is a perfect choice to develop your career in data science it is an ideal choice for Big data, Data Science and Machine Learning, in fact, many researchers, scientist and scholars using R for their experiment and analyzing with data science.

We have many experts as well as Data Scientist who have lots of experience in this field and we would love to help you to build your career in data science. As well as there are lots of project in R. Some tasks are like Designing Algorithm, Data Visualizing, Image Processing, Analyses data etc. If you are looking for this course and want guidance to feel free to contact us we are ready to help you out and ready to build your career in this field.

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Importance of Machine Learning in Everyday Business

Why Machine Learning is important in everyday business?


Basically it is a branch of Artificial Intelligence. It is the field of study that gives the computer the capability to learn things and gather knowledge without being explicitly programmed. In short, your machine/system is learning on its own. It works on Data, then it analyzes the data and then response and gives you the feel of real human and It really makes work easier than your expectations.

So let’s get into more brief there are some key terminologies
1. Data converts into information
2. Data can be an image, video, text file, CSV file etc.
3. It is a problem-solving tools i.e through ML we can develop tools
which cal solve any problems.
4. for example; Traveling Salesman Problems
Chess Game and many more
5. It is a combination of Computer Science, Statistics
6. Learn from data and then act.
7. Optimize performance criteria using past experience.

✔ Image Processing
✔ Voice Recognition
✔ Optical Character Recognition


Machine learning can keep an eye or keep track of apartments. For example, there are several machine learning applications in the industry of smart homes and these key allow the house owner to learn when there is movement in a house with push-up messages.

As we all aware of how Machine learning has made dramatic improvements in the past few years and we are still very far from reaching human performance and still many times, the machine needs human assistance to complete its task. But as the technologies growing/developing so fast that day is not far when it will completely
capture human minds.

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The Smart Life with IOT - Huge revolution of IoT today

Smart Life- The Huge revolution of IoT today

Internet of things (IoT) the internet is a network which you use to gather information and to communicate. And things means electronic device gadgets. Internet of things is influencing our lifestyle from the way we react and from the way we behave. Example the air conditioner that you can control with your smartphone, smartwatch which tracks your daily activities. The IoT promises to make things including electronic devices with conditioner fridge card and sensor part of the internet environment.

The structure the building block of IoT are sensory device, remote service, communication network and context-aware processing of events. It is a type of ecosystem which connected physical object and that are accessible through the internet. It works like devices and objects with a built-in sensor which is connected to an IoT platform. The IoT platform of so powerful by which we can use to detect the pattern and detect possible problems before they occur.

For example, if you’re running a car business and you want to know which component and product are most trending. so, here using IoT technology you can use the sensor to detect which area is most trending. Some use cases of IoT are smart home, connected cars, smart supply chain, smart farming, industrial internet, smart grids, smart city, wearables.

In future IoT will bring huge revolution, devices are becoming a part of the mainstream electronic culture and people are getting attracted towards smart devices and making everything smart.

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AI-Based Face Detection and Recognition.

The key features of AI-based Face Detection and Recognition

AI-Based Face Detection and Recognition.

In Face detection AI does involve, at least the latest technologies do, and they do so in a stylish manner. Let’s take an example, we all have used a smartphone, now back in the days, these ‘smart-phones’ weren’t as much
smart as they are today, back then Artificial Intelligence wasn’t really much involved but after the machine-brain being introduced into these lifeless robots, many industries saw a disruptive change.

Some fact might you aware of that Face recognition existed way before iPhone X was released right? Face recognition is nothing new but the technology is so advanced and after we came to know about artificial
intelligence it created a revolutionized facial recognition system. Have you ever wondered how so many social sites such as Snapchat, Instagram, Facebook, etc filters work perfectly on your face?

How do they have the exact ability to accurately map the geometry of your face?

The advancement in technology which embedded artificial intelligence to create a smart system able to do those things. Face ID spontaneously adapts to changes in your appearance. From cosmetic to glasses, makeup, glowing face or even growing facial hair. It is designed in such a way which able to work indoors, outdoors and even in total darkness. Something very interesting!

Face Recognizer searches an actual collection of data of faces and compares them with the faces detected in the scene to find a match. Face Recognizer records face from the camera and detect “people of interest” providing real-time alerts upon detecting certain faces in the scene.

Key Features of Face Recognizer Detect and log faces.

  • Recognize faces.
  • Search for similar faces.
  • It is able to detect any matches with faces on the watch list database and provide alerts.
  • It creates a log of people in the scene which can be used for forensic investigations.
  • According to public standard data set the Facial recognition accuracy is up to 99.6%.
  • Real-time watchlist detection. Identifying individuals on the spot.
  • Going to safer cities by capturing an image, evaluating it and matching it.

Face Recognition can be recognized by anyone with an arrest warrant out on the loose. Recognition and detection are possible both in real-time and offline and recognition and enrollment are available from both video and still images. Recognition in video mode is achieved by analyzing multiple images per face and in the video, mode recognition is achieved in less than 1-2 seconds. Using a mixture of AI and Deep Learning, Face Recognizer has achieved certainty benchmarks similar to or better than industry leaders like Google and Facebook. It scores the following accuracy in the leading public test databases – LFW: 99.3%, YouTube Faces: 96.6%, MegaFace (with 1000 people/distracters): 95.6% process.

It is said that technology presented us with the digital interview. However, with the beginning of facial recognition in recruiting, the process becomes quite subjective and bias. It is now possible for recruiters to use face recognition algorithm to analyze face against huge datasets sourced from social sites or any other networking sites.

Hiring managers can look you up online and use the information they learn about you to make their decisions. In the past, all you had to do was send in your application and patiently await a callback. We should thank this technology, several other auxiliary factors come to play before you are considered for an opening thus making the process flawed to some extent.

In present face recognition, the conventional pipeline consists of four stages:

  1. Detection,
  2. Align,
  3. Represent,
  4. Classify.

Many networking sites often visit both the alignment step and the descriptions step by step employing explicit 3D face modelling in order to apply a piecewise affine transformation, and which further derive a face representation from a nine-layer deep neural network. It has a facial recognition research project called DeepFace.

DeepFace is now almost as accurate as of the human brain. DeepFace can
see at two photos, and irrespective of lighting or angle can say with 95.35% accuracy whether the photos contain the same faces or not. There are lots of advanced shape and colour based algorithms for face detection in special use-cases. One advanced API which is based on the accuracy and consistency of results is a Chinese company called Face++.

Nowadays, many people are showing their interest in this field and if you’re interested in industrial applications of face detection such as biometric logins, or advanced attendance or security applications.

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How data science is useful for banking professionals?

How Data Science is helping banking professionals for building effective strategies

How data science is useful for banking professionals?

Data science in the banking industry is causing a sweeping transformation as per the changing consumer behaviours and expectations, more strict regulatory guidelines and highly competitive environment bind a potential success in the area of banking.

In Standard Chartered, Data Scientists tend to convert raw data to useful insights into business decision-making. This is achieved by leveraging tools and techniques, such as Machine Learning, Qlikview for visualizations, Hadoop for handling Big Data, R & Python programming for building data models, etc. Banks have always been a custodian of customer data, but they lack the technological and analytical capability to derive value from the data.

Data science will be the linchpin of future personalised banking services, Bank of Ireland’s “Colin Kane”. Data science is already a core fixture at Bank of Ireland, where various teams of data scientists and data experts are involved in analytics functions such as risk analytics, anti-money laundering (AML) fraud, marketing and customer experience.

The analytics market in India is growing in leaps and bounds. “The analytics business in India has created progress in terms of adoption across industries, nonetheless it’s obscurity on the brink of its potential being a broach, applying analytics to banking and finance is crucial, profitable, and intensely appreciated, each for organizations and professionals. That Decades went when a typical bank client would walk into a bank and be greeted by a banker who knew his name, his personal backgrounds and the way best to serve his personal banking desires.

This can be quite a recent model wherever banks have non-inheritable and preserved the customer’s trust and served them for a protracted time. However, things modified. individuals usually are engaged in multiple assignments and travels to completely different geographical locations. If at some point he stays in the national capital, the immediate next day he might get to visit Paris on his business assignments. In such conditions, it’s difficult for a banker to trace his personal preferences and whereabouts to satisfy his desires. Massive knowledge offers insight into several complicated areas of individual’s life together with their manner, needs, and preferences of their customers so it’s simple for banks to personalise services to the requirements of every individual.

For a protracted time, the banks miserably didn’t utilize the data generated by their own business. the massive knowledge has become a game-changer in remodelling their business method and conducts to spot business opportunities and potential threats. Generally, banks and money establishments notice massive knowledge from sources like knowledge, transactions, helplines, emails, social media, external feeds, support, audio, video and a few alternative sources.

Data, as we know, has been the driving issue for several sectors and organizations. And one sector wherever it adds extreme worth is that the one that deals with capital – banking and finance. Finance features is a crucial role in boosting the worth and growth of a business and banking permits quality and wealth generation. As key business and economic functions, banking and finance overlap with analytics as they inherently cope with the information.

Applying Analytics to money information opens whole new avenues of insights and understanding of the business, the market, their performance, and their growth. The ongoing transformation is opening doors to several opportunities. Banks must ensure that they can cost-effectively acquire new customers while retaining existing ones. And to expand their reach and profitability, they must also tighten their focus on the expanding digital world. Analytics, big data and data science can unlock a world of new possibilities.

With the proper use of data science, banks can better understand prospect/customer relationships by exploring ever-changing transactional and interactional behaviours. New digital marketing technologies, such as Web sites, e-mail, mobile apps and social networks, are helping banks better target their customers and improve engagement.

Moreover, advanced segmentation strategies are helping them boost their marketing effectiveness by identifying niches based on consumer behaviour. Many banks are just beginning to consolidate and utilize the internal data elements at their disposal, such as debit and credit transactions, purchase histories, channel usage, communication preferences, loyalty behaviour, etc. And when it comes to big data, banks have collected large amounts of information from a variety of sources, such as transaction details and spending behaviours. The addition of newer sources, including Web server logs, Internet clickstreams, social media activity and mobile-phone call details, has opened the floodgates on the data sets that can be mined for meaning.

Introduction of massive knowledge in banking has destroyed several ground rules of business and remodelling the landscape of the money services business. With an enormous volume of information gushing from innumerable transactions, the banks try to seek out innovative business concepts and risk management solutions.

Every set of data gathered over an amount and tell a novel story and show the post for a particular future amount so a house will take advantage of this information to realize a competitive edge up the market. This massive analytics knowledge will improve the extrapolative power of risk models employed by banks and money establishments, analytics also can be employed in credit management to find fraud signals and same will be analyzed in real-time victimization AI. On a significant note, banking and finance business cannot understand knowledge analytics
in isolation.

Alongside distinguishing business opportunities, they must establish security threats, the prevalence of fraud and potential remedies. Further, they must arrange to connect analytical knowledge across division and structure silos. In several of the normal banking entities in India haven’t nonetheless begun their massive knowledge activities. The banks have a footing in capitalizing on those opportunities.

Therefore, massive knowledge science not solely brings new insights to the banks, however, it conjointly allows them to remain a step previous the sport with advanced technologies and analytical tools. As an early warning system, data science solutions can help banks quickly identify potentially fraudulent behaviour before the fraud becomes material. For example, individual cardholders are creatures of habit.

Cardholders have “favourites “ or recurrences over a wide variety of objects in their transaction streams. These objects might include favourite ATMs that are close to work or home or gas stations along a daily commute, as well as preferred grocery stores and online sites for shopping. An analytics technique that could be used to improve fraud management is to identify cardholder favourites, in order to distinguish between “in-pattern,” or normal, customer spending and “out-of-pattern” suspicious transaction activity.

This enables faster fraud detection at much lower false-positive rates (declines on legitimate transactions). Text analytics of unstructured data can help banks identify patterns of information that indicate the likelihood of fraud. Text mining of insurance claim descriptions (written and recorded) provided by bogus claimants uncovered some very interesting facts. It turns out that certain phraseologies (the use of “ed” rather than “ing” on the end of verbs, for instance), are extremely indicative of
fraudulent claims. This is due to the different ways in which people rely on stories they actually experienced vs. those they concocted; for instance “I
was walking” is indicative of someone recounting an actual experience whereas “I walked” often turns out to be indicative of someone describing
a fictitious event.

DBS Bank, Southeast Asia’s largest bank by market capitalisation, is driving a digital transformation process with data at its heart, with goals including the growth of its customer base in India from 1.8 million to 5 million by 2021. DBS will look into improving its data analytics set up to enhance areas like robotic process automation – 8 per cent of customer interactions are handled by bots – and other more advanced applications such as anti
money laundering: ït’s still early days but we will be enhancing analytics to drive a lot more productivity and more added-value services and processes to the bank.

For instance, machine learning can be used to understand customer sentiments.

All calls to the bank’s call centres are recorded. They can be converted to text and then machine learning algorithms can be used on the analytics platform to understand the sentiment. Problems can be flagged so that the bank can reach out to the customers.

Ultimately behavioural information and machine learning, in combination with biometrics could even enable ‘invisible authentication’, where a customer no longer needs to provide any supporting documents or use a physical device for transactions According to Gartner report, Yes Bank carried out the successful deployment of data analytics for enhancing its business impact, integrating data science in their core model showcasing the urge to create data science-driven decision making.

Techniques to gain insights into customer behaviour, services and predictive models using different types of customer transactional and behavioural data. Impacting the revenue growth with high margins, controlling risk management and enhancing customer experience. Axis, third-largest private-sector banks in India, recently launched a hackathon to solve a signature recognition model.

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7 reasons to better understand the processes that included in Digital marketing

Understand the detailed processes included in the Digital Marketing

Digital marketing refers to advertising and delivering information through digitally such as search engines social media website mobile etc and by which the term covers a wide range of marketing activities. there is no doubt that in today’s modern landscape a huge part of marketing strategies done digitally it playing a big role in today’s digital life.

Nowadays everything is done through the digital process and people are also adapting things digitally and
businesses are almost done or happening online, the most of the time spent on making their website attractive so people will show interest in their website and this is the market strategy how to attract people by your website. Here, Digital Marketing strategy play an important role it is a series of actions which helps to achieve you your
aim, your goal of company through selecting online marketing channels and whereas these all channels including on paid and owned media and people are also so advanced they keep checking new things online and if they like it they keep on sharing as well. Digital Marketing is the biggest platform to start over and spread your business all over
the world.

Digital marketing is a very low cost. It can assess a massive audience. It is cost-effective if the customer database is well managed but at the same time, digital marketing is going to consume a lot of time. Response rate varies enormously, campaigns are very easy to copy but you can personalize the marketing message.

Types of online digital marketing include;

1. Search engine optimisation.
2. Search engine marketing.
3. Content marketing.
4. Social media marketing.
5. Affiliate marketing.
6. Email marketing.
7. Influencer marketing.
8. Mobile phone advertising

Now, what is search engine optimisation it is a process of growing your online visibility organic search engine results which means the results appear in a list and ranked using the search engines algorithm? Every engine needs little oil, using targeted keywords in your content to rank your site in web search results.

SEO process includes:-
a. Research and analysis.
b. Identifying keywords.
c. On-Site coding and implementation.
d. Copyright.
e. Speed & site performance.
f. Marketing.
g. Ranking report and tracking.

Search engine marketing is a market where it promotes your website by increasing the visibility of your website in search engine result page.

Content marketing is a type of market in which you will find information, it involves the creation and sharing materials online so that people can read your information from your website and upvote it.

Social media market helps to promote a product or service on a social media platform.
so that everyone can watch it.
Affiliate marketing is a type of performance-based marketing where it process of earning a commission by promoting other people’s product and service and in which a business rewards affiliates for each visitors or customers.

Through Digital Marketing research and practice is improving with the advancement of technology. It helps you to keep track of your customers or you can aware of your customers about your products and available 24/7. If your goal is to target a large amount of people then digital marketing is a good platform. You can spread or promote your websites on the online platform.

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Why should you know the importance of Cloud Computing in Data Storage?

What is the importance of Cloud Computing in Data Storage


As per the technology day by day new innovations are coming up and we are seeing technology moving to the cloud. Everyone is talking about the cloud. So what is exactly cloud computing??

Cloud computing is a term which means anything that stores data and transferring hosted service over the internet.

The interesting part is that the name of cloud computing was influenced by the cloud symbol that often used to represent the internet. Cloud computing made up of several networks and users it gives services to all over the world.

Cloud computing is not only for storage purposes it is more than that. we use Dropbox, Google drive box extra to store our data we don’t need to buy any hard

disc this is the most interesting part of cloud computing. Here you will get services different applications even so many companies out there which depends on cloud computing. We can easily scale up and scale down in cloud computing.

It provides your database so that you can store lots and lots of data in your cloud. It
provides you services from the internet.
Some features of cloud computing are there which really helpful in several ways.
Broad network access.
On-demand self-service.
Resource pooling.
Multi tenacity.
Rapid elasticity.
Measured service.

With the help of cloud computing, you eliminate those headaches that come up with storing your own data you don’t need to manage lots and lots of data. In Fact, you don’t need to keep the records of data cloud computing will keep everything record as well as provide security to your data and day by day our life getting easier. The traditional business application has always been very complicated and expensive. There are few benefits of cloud computing
Low cost.
Software update.
Data loss prevention.

There are 3 services which are widely used in cloud computing.
1. Software-as-a-service (SaaS)
Provides software. Example:- Google/Microsoft.

2. Platform-as-a-service (PaaS) Provides platform. Example:- Google app engine.
3. Infrastructure-as-a-service (Iaas)
Example:- Amazon web services.

In conclusion, cloud computing has many benefits that it provides to its users and businesses. It is recently new technological development that has the potential to have a great impact on the world.

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Data science career insights that you must know

Top Career Insights to know in Data Science

Much has been written about ‘data is the new oil’ and ‘data science’ is one of the hottest emerging technology areas. Yet many of the aspirants who would like to get into this sector have many questions about the skill requirements, opportunities in the industry, job openings etc. In this article, we shall try to answer a few of the commonly asked questions and clear the ambiguity around the required skills and job roles.

● Almost 48% of analytics job openings are looking for a B.E./B.Tech graduate degree in the incumbent.
● 18% analytics job openings are looking for a postgraduate degree which is not MBA or M.Tech. This is a decrease from 26% a year ago.
● 8% of analytics jobs specifically require an MBA/PGDM degree.
● Just 13% of recruiters are looking specifically for graduates with no/ non-B. Tech degrees, up from 10% last year.
So here’s the question in everyone’s mind that then how do a fresher crack this industry?
I am interested in getting into data science as a career. What do I need to learn?

In the area of data science, broadly understanding of three areas are required:
1. Statistical /machine learning techniques & algorithms
2. Computing tools/languages
3. Business understanding

For the first two areas, skills need to be built through learning and training.
Business understanding is a function of exposure in the business and industry.
One can prepare by learning through examples, case studies, projects etc.

It will lead you to become an expert in data science. A data science expert is someone who can analyze huge amount of unstructured and structured data using advanced tools and machine learning algorithms to solve a real-world business problem.

Skills that are needed to get an entry-level job in this field.
1. Basic of statistics.
2. SQL
3. Knowledge of basic problem-solving.
4. Having a portfolio of projects.

In statistics, you need some basic knowledge like
1. Sampling.
2. P-value.
3. Hypothesis testing.
4. Experimental design.

Try to implement these concepts using python on small data sets, so that you can tell anyone that you are you have some practical knowledge. By doing this you will have an edge over other candidates who may just have a theoretical understanding of these concepts.

SQL will help you
1. To retrieve data from relational database
2. It stores data in your databases.
Many data analytics professional spend their time on writing SQL scripts.
You may get various job descriptions like data scientist, business analyst or big data this might be confusing for you you but do not get confused by different job titles by companies providing.
In reality, they go in the path of data analytics after B.Tech graduation in this

Python, R, MySQL or Excel are mandatory skills for this job. They are also looking for candidates who have a good knowledge of the algorithm and if you know the basic concepts in machine learning, then it would be like “icing on the cake”.

What are the job roles in data science?
Data Science Aspirants wonder about the kind of roles that are there in the industry for them. There are many roles and opportunities in organizations. It is pertinent to remember that data science or big data analytics can not be done with one person. It is a team that works on such projects and there are various functions
and roles that are needed. Let us decipher a few of such job roles and designations that you may come up with.

1. Data Scientist
A data scientist is like the master chef! He is expected to work with big sized and complex data to derive meaningful patterns/visualization for the business to benefit. He should know statistical and machine learning
algorithms, how to manage complex data sources as well as have a strong understanding of business. A tall order of requirements from a single person but a data scientist adds tremendous value to an organization’s data science and analytics journey.
2. Data Analyst
Data Analysts are curious people who have an analytical mind. They sift through tons of data to find out if there is a relationship by running statistical analyses or find out the root causes of an event happening, like, why the sales of a division in a geography is going down for a product line or developing a model for predicting fraudulent claims for a health insurance company.

3. Data Engineer
Data Engineers are the people who love to play with large scale databases and systems and typically come with a software engineering background. ln, a data science context they operationalize the analytics model developed by data scientist and data analyst team and, automate, deploy and make it run for clients/business.

4. Data Architect
In the context of a large organization with disparate sources of data, the role of data architect is very important. He creates the blueprint and map for data sources and data mart which is crucial for any data science or analytics
5. Business Analyst
A business analyst, on the other hand, is more business savvy and looks at problems from the angle of business rather than the technicalities of data, algorithm, architecture, management etc. He is the one who will have a
better business/domain knowledge and uses data to support business processes. He may use different visualization and analytics tools/products for his findings and recommendations. Needless to say, all the above may start at entry-level and grow to senior levels and, also there could be other roles and nomenclatures that you
may come up with like data integrators, database administrators, data warehouse specialists, big data consultants etc. and there are always some overlaps in functions among these roles. NASSCOM has identified six areas of
specialization in the Big Data Analytics domain that is expected to drive growth in the sector: business analysts, solution architects, data integrators, data architects, data analysts and data scientists.

Where are the job opportunities?
Data Science job scenario in India is constantly evolving. Companies are looking to hire professionals who are looking to hire professionals who are well trained with new tech concepts such as analytics, big data, data science, artificial intelligence and machine learning among others.

● Around 62% of analytics requirements are looking for candidates with less than 5 years of experience.
● 17% of analytics jobs are for freshers.

Opportunities in data science are no more limited in only the technology or consulting companies.
It is finding greater acceptance in traditional businesses as the interest increases in leveraging data for decisions. The number of opportunities of data scientists and data engineers can be found in companies like Uber, Amazon, Ola, Google etc. as their business models are heavily data-dependent as well as in consulting companies like PWC, KPMG, Deloitte etc.

10 leading organizations with the most number of analytics openings this year are – JPMorgan, Accenture, Microsoft, Adobe, Flipkart, AIG, Ernst & Young, Wipro, Vodafone & Deloitte.
Then there are user companies in healthcare (hospitals, pharma etc.), retail, telecom, utility, BFSI (banking, financial services and insurance) etc. where there is a large number of opportunities as data analysts, business analysts, reporting specialists etc. The startup companies are also coming up with new product and services using analytics and artificial intelligence which are creating new job opportunities.

Top designations advertised are Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant & Statistical Analyst, Data Scientist.

l am a fresher. Where do I start?

Innomatics Technology Hub offers quality-driven Data Science courses in
Hyderabad, India. We at Innomatics provide very comprehensive courses at
Hyderabad. People from different part of cities come across here to get knowledge
in specific domain i.e Data Science. We Innomatics provide hands-on an experiment

with 100% of placement once you are done with your course.

Nowadays every fresher graduate is interested in analytics as an industry. Why is
everyone moving towards data analytics jobs? Why there are more interested in
this field?
Because analytics is one of the fastest-growing industries in the world today. The
demand so vastly surpasses the supply of data analysts that companies find it
difficult to hire trained professionals in analytics. Innomatics Technology helps
freshers as well as experienced candidate with highly qualified trainers to help you
land on a data science job with full placement support. Innomatics will also explain
analytics concepts in a simple way to you, while also demonstrate how these
concepts are implemented in a series of projects, which will make your learning
process effective, easier and hassle-free.

Can IT professionals be reskilled for these new-age job opportunities?
With the rising demand for data-driven technologies across the globe, IT companies
are reskilling their large pool of IT professionals in these new-age technologies like
predictive analytics, machine learning, data mining, deep learning, cognitive
analytics, artificial intelligence (A), IOT based analytics etc. Best SAS, SPSS, Cognos,
R, Python, IBM Watson, Hadoop, Spark etc., – proprietary or open
Apart from training aspirants and practitioners incorporates in business
analytics, big data, data science etc., Innomatics also offers new courses like
cognitive analytics using IBM Watson, machine learning with Python etc. Many of
our students have successfully moved on from being an aspirant to successful
practitioners in the industry.
Startups and young organizations are also using these new technologies to come up
with disruptive solutions and business models. This is also adding to the
opportunities for existing professionals for lateral shifts.

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What’s the big deal about ‘big data’ , what’s changed?

The revolution and segregation of Big Data in the modern era?

What’s the big deal about ‘big data’ , what’s changed?

Catering on-demand video services involves handling large amount of data, which was previously stored as gigabytes of data onto an optical disc. Big data uses the whole spectrum of data available as it transforms meeting organisational goals and service. A two-way communicationover a conventional broadcast. The data helps the company identify who is watching what,when and where. Modern systems can cross-indexmeasures of a viewer’s interests, along with their feedback. As viewers we see the outcome of this analysis as per the recommendations Netflixmakes, and sometimes they seem odd, because the system are attempting to predict thelikes and dislikes of a individual in particular. The ability to understand the potential audience for a new series of Sacred games 2 depends on big data but that’s not the only way. Judiciously using data for instance, different trailers for the series could be made available to different segments of audience.

Big data is not all about business, though, it has the potential to understand customers and their behaviors and preferences, animate a still photograph, perform diagnostic analysis, predictive analysis, prescriptive analysis, monitor responses of a campaigns and find out segments which respond most and least. These analysis are pivotal for running a business.

Don’t doubt it – big data is here to stay, making it essential to understand both the benefits and the risks.

Data ever since had been a backbone of civilization, as it evolved in the 17th and 18th Century but was restricted by the narrow scope of data available. Now it’s readily available and opening up a new world.

Tech giants like Amazon, Inc., a Seattle, Washington-based, the largest e-commerce marketplace and cloud computing platform in the world uses big data to identify personal interest by what a customer
previously likes specifically items which a user potentially might want to buy.

Amazon’s version of big data can be described in terms of data management challenges that – due to increasing volume, velocity and variety of data – cannot be solved with traditional databases. As the column says the important concept is commonly known as “three V’s” of big data.

Volume: Ranges from terabytes to petabytes of data
Variety: Includes data from a wide range of sources and formats (e.g.
web logs, social media interactions, ecommerce and online transactions, financial transactions, etc)
Velocity: Increasingly, businesses have stringent requirements from the time data is generated, to the time actionable insights are delivered to the users. Therefore, data needs to be collected, stored, processed, and
analyzed within relatively short windows – ranging from daily to real-time

Amazon boasts with different tools which itself addresses the entire data management cycle, big data technologies make it feasible, not only to collect and store larger datasets, but also to analyze them in order according to data flow.

Collect. Collecting the raw data – transactions, logs, mobile devices and more – is the first challenge many organizations face when dealing with big data. A good big data platform makes this step easier, allowing developers to ingest a wide variety of data – from structured to unstructured – at any speed – from real-time to batch.

Store. Any big data platform needs a secure, scalable, and durable repository to store data prior or even after processing tasks. Depending on your specific requirements, you may also need temporary stores for data in-transit.

Process & Analyze. This is the step where data is transformed from its raw state into a consumable format – usually by means of sorting, aggregating, joining and even performing more advanced functions and algorithms. The resulting data sets are then stored for further processing or made available for consumption via business intelligence and data visualization tools.

Consume & Visualize. Big data is all about getting high value, actionable insights from your data assets. Ideally, data is made available to stakeholders through self-service business intelligence and agile data visualization tools that allow for fast and easy exploration of datasets. Depending on the type of analytics, end-users may also consume the resulting data in the form of statistical “predictions” – in the case of predictive analytics – or recommended actions – in the case of prescriptive analytics.

As per sources there have been, more data created in the past two years than in the entire previous history of the human race.

1. Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% according to Wikibon.

2. Data is growing faster and there have been a rapid growth of about 1.7 megabytes of new information created every second per human being on the planet.
3. In Aug 2015, over 1 billion people used Facebook in a single day.
4. By 2020, we will have over 6.1 billion smartphone users globally.
5. By 2020, at least a third of all data will pass through the cloud

6. The Hadoop market is forecast to grow at a compound annual growth rate 58% surpassing $1 billion by 2020.
7. Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.
8. Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child.
9. According to Dell Technologies, the introduction of 5G networks will have us “livin’ on the edge,”

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