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Tag: big data

What Are The Ways Big Data Is Changing The Healthcare Industry?

Posted on by Imarticus Learning

Introduction

Big data is the new elephant in the room. One can do nothing but notice how fast its applications are increasing and talk about it. Big data has made use of such information which was collected through various systems but was never used. It is evolving with every passing day thus making itself a lucrative investment for companies who want to survive and flourish in this era of globalization and innovation. Big data analyses huge chunks of data a matter of seconds, thus drawing useful insights and also saving time, money and human effort.

The Healthcare industry

The health care industry includes everything from drugs to hospitals, diets to well being and a lot more.  The commercialization of the healthcare industry is growing at an alarming rate. It is one of the fastest-growing sectors involving almost the whole population of the world and loads of money thus forming almost 12% of the economy of any developed or developing nations. This segment has huge potential which was untapped until now.

With big data and analytics taking over the world, the healthcare industry has evolved tremendously. From medical insurance companies to drug manufacturing giants, all of them are minting money by using their data to the fullest, extracting out details one could have ever imagined.

The Healthcare industry and Big Data

The application of big data in the health care world has proved to be again for both – the parties providing services and the parties receiving the services. The big data uses the health data of millions of people to create a digitally empowered market. The big data has served its purposes by controlling harmful epidemic diseases and also curing millions of people. The major application of big data is in optimizing cost structures.

Various hospitals analyze the patients’ data available with them, finding out the intersecting points, working on them and saving millions. Various drug giants analyze their data with the help of big data thus improving their supply chain operations efficiently and increasing the reach of the medicines produced by them.

The health insurance market is a crowded space. The doctors make use of data to understand a particular patient, his medical condition and possible disease thus helping in curating the best insurance plan for that particular individual. This data also helps doctors in new findings and figuring out new innovative ways to cure diseases. Also, big data has not only made the analysis of data easy but also the collection of data a pretty convenient.

Data analytics also provide individuals like us to keep a tap on our general well-being and health. Various applications like Google fit keep calorie counts and heart rate information in data bits helping an individual to monitor his activities and also helping these big companies keep track of the lifestyle of their consumers.

Big data analytics training is helping hospitals to make staffing decisions so that they have an adequate number of people available when the hospital is oozing with patients. Also, this helps in tracking the hospital supplies and inventories like local painkillers, surgical equipment, surgery wearables, etc. It keeps the whole ‘Hospital-Ecosystem’ in check. It also helps in tracking real-time information and providing feedback on patients’ health regularly thus making the job of a doctor pretty convenient. Big data has brought the whole healthcare industry on a digital platform where details such as the medical history of a particular patient can be figured out in a fraction of seconds.

How this data is used by the Government

The government uses this data to chalk out the healthcare strategy for a particular country it belongs to.  This data helps the government in figuring out the number of hospitals, medical supplies, etc. needed by its people. This data can also be used in educating people about the benefits they can avail in terms of health care and well-being.

Conclusion

The healthcare is growing and this growth is not going to stop anytime soon. Like all other industries, big data is driving this growth and transforming the healthcare industry into a whole new world thus improving the decision making process and optimization of costs.

How Do A Big Data Help In The Insurance Sector?

Posted on by Imarticus Learning

 

Understanding Big Data

The concept of information is power puts data into the center of progress, data today is the real deal. How exactly to put together the concept of big data? Well, the name is very suggestive and builds a clear picture as to what it could be.

Big data can be understood as a big, complex & voluminous database that contains a variety of information regarding everything and anything that generates some kind of information. The data sources are ever-growing and the velocity of data from these data points is magnanimous, adding tons of data every second to this existing large database called the Big Data.

So what’s the use of collecting this data from every source available? In the digital age, we are consuming a huge amount of data on a daily basis, courtesy of the internet. Whatever we search on google is available because somebody tried to store it and upload it for the use of masses.

Now while using the internet we don’t just consume the existing data but create new data sets which could be in the form of anything ranging from our names, contacts to our web history. The three V’s important to the formation of the big data are Velocity, Volume, and Variety.

Why fuss over the big data? Well because the big corporations are ready to kill for it! Big data provides much-needed insights into customer preferences and their data history which can help them inefficient targeting of customers and better their sales and marketing revenue.

Big data scientists gather specific information and the technical know-how while preparing to enter the industry of data science, they use big data for providing valuable insights to the firm. The big data analytics training has helped boost the career prospects of people from the IT space. One of the reliable programs is the big data Hadoop training course which is curated by Hadoop industry experts.

Implications in the Insurance industry

If you had the power to predict something with high probability on the basis of the past track records wouldn’t it be fruitful? That’s how big data help every industry in general that needs a past track record to implement changes in the future functioning. Broadly, insurance ranges from general to automobile & healthcare which is further broken down into sub-segments depending upon the industry.

The most obvious use of big data in insurance is customer insights based on the information gathered from the customer since this is a generic one applicable to every industry it’s easily understood. Let’s take an example of the automobile insurance so the information relevant to pricing the insurance policy premiums and add-ons depends on various factors like safety level in the buyer’s vicinity to their historical driving record. The insurance firm can accordingly charge different buyers with different rates as the degree of safety is very subjective and also the driving habits of people vary.

Where do insurance companies fail? If you go in a little deeper into the subject you’ll find that the level of fraud related to an insurance claim is paramount resulting in loss to insurance providers. Now the case of moral hazards is very prevalent and people often see insurance as a total safety net so they don’t even bother to maintain a minimum safety standard.

Big data steps in to identify a probable false claim based on the history of the party claiming an insurance amount, the level of fraud has come down drastically owing to big data in insurance.

What all can go wrong for any particular scenario of insurance? These scenarios are also developed with the boon of big data. This helps in better premium pricing and reduce the chances of a surprising claim for unaccounted factors.

Conclusion

The term big data is very suggestive of the work it performs and what it holds in its reals. Containing the massive amount of databases from each and every data point, big data paves the way for future based on the historical records of things. Among the numerous applications of the big data, the Insurance industry seems to be gaining a whole lot from the insights that this mammoth entails. From reducing the cases of insurance fraud to pricing the premiums of various insurance policies given the subjectivity of the user, the big data is shaping the insurance industry for a better future and better profitability.

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 Predictive Analytics Help Troubleshoot Network Issues?

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Ten years ago, if a person had suggested a predictive model to prevent a network failure occurring due to a planned breach, people would have not believed him. Today, that has become a reality thanks to predictive analytic tools and different technologies including big data and statistical modeling.

In simple words, a predictive system looks for irregularities or patterns in data and identifies issues in a network or a server before they transform into bigger problems. This piece of information can then be used to troubleshoot it. An example would be a network outage due to failure in the power supply that can be predicted by looking at the irregularities in the flow of power supply in the days before the outage occurred. The possibilities are immense and that is why it looks so promising.

To make this clearer, let’s understand the basics first.

Analysis of Network Behavior

The basic premise of using predictive analytics to troubleshoot network issues is to let it analyze the behavior of the network. For example, analyzing the flow of data in a communications line can help it understand if any loopholes in it could create a possible entrance for a data breach.

This information can then be used as a preventive measure; a defensive mechanism can be laid out even before the breach is attempted, thereby safeguarding the data available in the line. This not only helps in the security but also in network management and policy setting. To know what is happening inside a server network and monitoring it is real convenience for network managers. It halves their daily maintenance work.

Additionally, analytics can also give out trends and insights to organizations. If a certain type of communication mechanism is known for overloading, companies can avoid creating similar structures and instead opt for better versions or entirely different infrastructures. This information can then be utilized during infrastructure development, especially when it comes to the development of server rooms for IT organizations where data breach and upper thresholds need to be monitored by the second.

Predictive Analytics in Action

Experts suggest that such technologies should be put to use in those sectors where issues can cause discomfort to a whole crowd of people. They are referring to healthcare and other emerging sectors like power distribution and aviation traffic management. Network management in these sectors will help increase safety and security and minimize issues/accidents.

Healthcare systems actually need this technology because it can help hospitals better care for their patients who require 24×7 technical support and are continuously connected to the hospital’s server.

Other than looking at the historical data provided by the network, other parameters like weather conditions are also taken into account. There can be a possibility that a thunderstorm could switch off a hospital’s network because of a power supply failure. If the effect of weather on the network can be predicted, then alternatives can be put in place just in time. Although such alternatives are already in place for emergencies, what such models will help in is better implementation and preparation.

A popular example is the use of predictive analytics in emergency services is how General Electric Power uses AI to manage its power grids in the US. According to this example, the predictive model helps the company get rid of the scope of manual errors in its system. It says that simple errors made at the service provider level can sometimes cause outages in the whole sector. This can be avoided if the data entry is taken online and passes through a filter that is connected to such a model.

Any mismatch in the data as compared to what is expected of it will trigger an alert and the response teams can quickly get in action. This is already being executed by GE Power even as it finds ways to make the entire grid system automated. This does not necessarily mean the absence of service engineers, but just the absence of potential errors that they are sometimes bound to make.

All of this is possible only because of the presence and availability of historical data. Without it, the predictive analytics models cannot compare the new tasks. This is one of the challenges that new sectors face as they do not have sufficient data to work on.

Some Challenges in this Field

Predictive analytics don’t fare well for environments that are rapidly changing. This means that environments where the relationship between two actions is quick, the model finds it difficult to grasp it and thereby ignores it and moves to the next action. This can pose a problem because it can lead to incorrect prediction, or worse, even dangerous predictions.

Availability of data, as noted above, is another hurdle but not something to be worried about. For sectors like healthcare, power, and retail manufacturing, there is abundant data. The challenge then is to source and save it properly which can be used to create the models.

Experts also point to the lack of implementation on the part of engineers. Scientists are continuously toiling to create predictive models that help in error detection but on-field engineers and workers are not supporting the system by providing or utilizing data. This can be a field engineer working on a local transformer for GE Power or a systems engineer at the grids network office not willing to listen to the model’s alerts. This shows that there is also a need for awareness among workers and engineers. This is definitely a radical change in how things work but embracing them is the only way to make it serve us better.

Predictive analytics, despite its active use in detecting and troubleshooting network issues, is still at a nascent stage in the global scale. While some countries and corporations are innovating in the field with ample help from scientific organizations, the practice will only strengthen when it comes to the mainstream. And that might take some more time.

What are the Use Cases of Big Data in Real Estate?

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Catching up on big data & real estate

Real estate is comprised of assets such as property, land, houses, and buildings. Real estate is a budding sector where properties are dealt with every now and then. Real estate agents facilitate the buying and selling of homes, land, etc. on the behalf of the parties whose interests are vested in it.

Big data is a common term that is widely accepted for large sets of data which is analyzed using various computer software to bring out trends and other insights to understand consumer behavior and several other aspects of the economy.

How is big data related to real estate?

Big data has transformed the way data is perceived these days. It has facilitated a smooth analysis of data and the extraction of vital information. Real estate involves a huge client base thus involving a huge amount of data. There are buyers, sellers, financial institutions and a lot of other parties who require data chunks to cater to their specialization.

Real estate is moving to an electronic mode thus becoming more data-centric. People are buying and selling properties using mobile platforms thus collecting huge amounts of data. The real estate agents through these application data can easily get to know about the properties which are in huge demand and thus control the rates of the already volatile market.

Real estate should have hands-on big data so that they can reap out the benefits of the huge data resource available. Buyers are moving to a mobile platform where they can assess various property options at the same time and improve their search experience. Realtors will also know their clients better and serve them in accordance with their needs. This data is really valuable.

The biggest challenge in the real estate industry is that technology touches this sector at a very slow pace but the roots of technology are growing so fast that the real estate sector has also got a good taste of it.

Influence of big data in the real estate sector

Big data plays a real role in fixing the prices of tangible properties. Also, people who have an intention to buy get to know about the prevailing market rates. The realtors can analyze the cash flows which can take place in the future on the basis of demand. When an interested party visits a real estate website he knows what he is searching for. He has his specific parameters in place thus giving the app controller user-specific data.

The big data analytics training the realtors with a lot of information about an individual such as his age, region to which he belongs, what kind of house does he require, etc.

Such information helps the realtors to make notifications and emails more personalized thus winning the trust of the consumers. Big data also gives an insight into people who are interested in taking properties on rent. These real estate giants have access to a database of millions of people.

With the help of big data, real estate companies are able to market their products efficiently and smartly. Big data is being used by the realtors in marketing their products and also reaching their prospective clients with the help of various marketing campaigns such as email marketing, influencer marketing, celebrity marketing, etc.

Big data also helps in improving the decision-making process for these companies and also for the individuals who are visiting the application. With a plethora of options available, an individual could get all sorts of information on a particular house such as the locality it is in, how old the property is, how far the market is and so on.

Conclusion

This shift in the outlook of real estate businesses has just begun. The more these companies analyze the data available, the more it becomes lucrative. The process of implementation of big data in the dynamics of real estate business is a little slow but all good things take time. Also, they have already started to make the best out of the data available by slowly unwinding the treasures hidden in the layers of the so-called complex data.

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.

What Are An Interesting Careers To Explore In Big Data?

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What Are An Interesting Careers To Explore In Big Data?

Big Data is no longer a future capability but is already in use in a variety of sectors and industries. Some of the uses are as diverse as taxis in Sweden using data to cut back on traffic and emissions to Barcelona building a smart city based on data and farmers worldwide using data to reinvent farms. The benefits of Big Data applications and data-driven strategies have thrown open the doors to a variety of careers which are satisfying, always in demand and pay very well.

Doing a big data course is one of the best options to hone your skills on the current demands of the emerging technologies in Big Data and allied fields like machine learning, artificial intelligence, deep learning, and neural networks among others.

Let us explore the top careers and the requirements to make a career in this lucrative area. Salaries are as reported in Payscale.

  • DATA SCIENTIST: These are the experts who produce meaningful insights and work with Big Data volumes using their technical and analytical skills to clean, parse and prepare data sets from which an analyst can apply algorithms to get business insights. Their salary is in the range of 65,000 to110,000 USD.
  • BIG DATA ENGINEER: These engineers evaluate, build, maintain, develop, and test big data solutions created by solutions architects. Their salaries lie between 100,000 to 165,000 USD.
  • DATA ENGINEER: The engineer is responsible for data architecture and the continuous data flow between applications and servers. Their salary range is 60,0945 to124,635USD.
  • ML- SCIENTIST: They work with adaptive systems and algorithm development and research. They explore Big Data and train the big data course to automatically extract trends and patterns used in demand forecasting and product suggestions. The average ML scientist’s salary is 78,857 to124,597 USD.
  • DEVELOPER-DATA VISUALIZATION: These people are responsible for the development, design, and production of interactive data-visualizations. They are the artists who bring to life reusable graphic/data visualizations. Their technical expertise is valued and the salary range is 108,000 to130,000 USD.
  • SPECIALIST- BUSINESS ANALYTICS: This specialist assists in testing, supports various activities, performs research in business issues, develops cost-effective solutions and develops test scripts. Their salary range is 50,861to 94,209 USD.
  • BI- ENGINEER: These engineers have business intelligence data analysis expertise and set up queries, reporting tools etc while maintaining the data warehouses. Their expertise earns salaries in the range of 96,710 to138,591 USD.
  • SOLUTION ARCHITECT- BI: These architects deal with solutions that aid sensitive timely decisions for businesses. The salary range for this role is 107,000 to162,000 USD.
  • SPECIALIST- BI: These people also are from the BI area and support the framework across the enterprise. The salary range for these is in the range of 77,969 to128,337 USD.
  • ML ENGINEER: This important aspect of ML develops solutions aiding machines to self-learn and autonomously run without human supervision. ML engineer’s draw a salary of 96,710 to138,591 USD.
  • ANALYTICS MANAGER: This manager deals with the design, configuration, support and implementation of analysis tools and solutions from huge transaction volumes. Their salary range is 83,910 to134,943 USD.
  • STATISTICIAN: These people are tasked with gathering, displaying and organizing numerical data used to make predictions and spot trends. The salary range for this role is 57,000 to 80,110 USD.

The skills required:

The basic attributes required for these jobs is:

  • Knowledge of Apache Hadoop, NoSQL, SQL, Spark, and other general-purpose programming languages.
  • Skills honed in a regular big data analytics course.
  • Adept in ML, data mining, quantitative analysis, data visualization and statistical inferences.
  • Personality attributes like being a team player who is adroit in creative and analytical thinking, innovative approaches and creative problem-solving.

The importance of certifications: 

Certifications endorse your skills and validate that you have the knowledge to practically apply your skills. Certifications in the below subjects will stand you in good stead when at interviews and improve your career prospects. Do go in for certifications in

  • Hadoop, SAS
  • Microsoft Excel
  • Python, R, and the Java suite
  • Pandas, MongoDB
  • Apache Spark, Scala, Storm, Cassandra, etc
  • MapReduce, Cloudera, and HBase
  • Pig, Flume, Hive, and Zookeeper.

Parting notes:

It is best to do the big data course at Imarticus Learning as they train you to be career-ready with skills on the latest technologies like the ones mentioned above. Their certification is well-accepted in the industry. So, why wait? Start on your career journey today!

How Is Data Analysis Used In Supply Chain Management?

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Supply chains today have gone global and spurred the growth of challenges and opportunities for suppliers, manufacturers, and others in the engineering industry (fondly called OEMs).To stay competitive lean and mean appears to be the name of the game. The rapidly growing automotive industry’s supply chain can be taken as an example of a supply chain discussed for the simple reason that its rapid growth makes it an excellent test case to study the impact of big data analytics courses.
The trend today is to perceive globalization, both as a challenge and opportunity and tweak its supply chain to create more agility, transparency, and visibility. Data analytics and that implies very big volumes of data and its analytics on a global scale, has been successful in managing and meeting deadlines of deliveries and production.
The supply lines stand improved, more efficient and productive through the use of Big Data Analytics Courses and invaluable insights garnered from data in assessing and decision-making, by efficiently gathering data, cleaning the huge volumes of databases, analyzing the required data sets and deploying the predictions and foresight offered by data.
To stay competitive in a variant-rich data-driven supply chain it is imperative that the supply-chains remain competitive while being productive and efficient on a global scale. However, even in 2017, the main issues with doing so was that the managers and planners were still unable to analyze, evaluate, and act on the data which was being generated and readily available to them. To leverage the benefits of a lean-and-mean supply chain is to effectively use and analyze data!
How does data impact the way a manufacturer, an OEM, or supplier works in the global supply-chain grids? What happens when data-driven supply streams are created and used well? How does strategy, based on data impact the operations of the company? How do big data analytics courses contribute? Let us briefly explore.

Greater organizational-wide insight

Looking at the big-picture and macro levels help organizations in data-based coordination, sharing and gathering for a pan-organization insight and context in decisions. Effective increase of touch-points, better contextual insights, and real-time monitoring has meant effective objectives, production benchmarks, outcomes, and goals. An increase in silo-making, collaboration, and communication in the supply chain adds value to the diversification and developmental expansion plans and ambitions of the automotive industry as a whole and occurs in real-time globally thanks to data analytics.

End-product quality maintenance:

Optimal production processes help data-driven supply-chains to produce better end-product efficacy and volumes. Data analytics has put the key to effective utilization in the hands of managers and planners to leverage resource allocation strategies, demand planning, scheduling, and inventory management. Developments like Industry V4.0 in Big data has also meant that OEMs can identify and monitor potential quality-control issues, access data-production and data details of processes, inspect in real-time the deliveries in-transit, and even check on scheduling and transit details of deliveries in progress. Thus risk-mitigation, improved efficiency, and greater productivity can be anticipated.

Surfing supplier networks:

The automotive supply-chain world over comprises of huge supplier networks that OEMs need to navigate, especially with the rapid proliferation of autonomous driver-less vehicles, smart-cars and electric vehicles gaining prominence and popularity in the rapidly fluctuating automotive manufacturing segment.
A data-driven supply chain allows for iterations in the complex OEM supplier networks while catering to its customers and evolves better products. Data analysis can also aid the S and OE level strategy making, allocate efficient production programs, link the facility capacity, and work around the production-floor restraints in dealing with ways and means of the inter-communicating process to ensure timely deliveries and smooth production.

Comprehensively treating supply-chain management:

Data analysis has the connective ability of disparate functions in supply-chain management which helps the planners and analyst to impact critical areas and cascade the effects up or down the supply lines. Thus data reporting can be effective in a ring-like interconnected structure where the impact and data analysis is successfully transmitted across the value chain. This also helps eliminate the barriers between disparate elements or functions and makes the supply chain more wholesome and vulnerable to change, holistic operations and data analysis.
For example, in the modern automotive supply-chain the various departments, services, and functions are effectively coordinated as a wholesome operation. Data analysis has helped in container management strategies, logistics, deployments, allocations, job-scheduling, routing platforms, inventories, and stock-management, etc and has successfully made the processes more efficient, productive and visible.
Conclusion:
Just as in the above example, you can also find your own value-adds to your specific supply chain by doing big data analytics courses at Imarticus Learning Academy. Grab the proposition to add value to your supply-chain and career. Hurry!
For more details in brief and for further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a simple form or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

From Computer Science to Big Data Analytics: How Imarticus Learning Helped In Specialization?

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Chirag shares his journey with Imarticus learning that led him to become a successful Data Analyst!!

Even though Data Analytics is one of the most sought-after fields today, it is not at all an easy specialization to pursue. Chirag a Bachelor in Computer Science shares his journey with Imarticus learning and how Imarticus helped him get a prestigious job as a Data Analyst.

Tell us a bit about yourself and your background

I am Chirag Soni, I am a Computer Science graduate from Pune University and I joined Imarticus Learning for a Big Data Analytics Course. I am really happy to tell you that today I am placed in M Technologies through help and guidance from Imarticus. After my graduation in Computer Science, I was looking for a  career as a data analyst and was considering a Big Data Analytics course or a Financial Modelling Course. After reaching the website of Imarticus Learning, I reached out to the counselors and applied for the course.

Tell us about your experience with Imarticus Learning

After referring to Imarticus Learning reviews on the Internet, I was really confident about the place I was going to, but the people here surpassed all my expectations. The standout for this course for me was the co-operative and warm faculty who helped me learn and master various programming languages like SAS, R, and Python due to their excellent in-depth lectures which provided all this knowledge in a structured and organized way.

What Changes did you notice since joining Imarticus Learning for your Data Analytics Course?

There has been a lot of change that I can notice within me especially in terms of confidence and professionalism. The well-structured courses and thoroughly professional faculty members provided me with the perfect environment to transform myself into a professional coder with attributes like high order thinking skills, conversational skills, and stress management skills that companies really look forward to having in their employees.

Imarticus Learning changed me from an immature to a thorough professional within days thanks to all the faculty and staff plus the supremely designed course that focuses on skills that are beyond the range of textbook teaching.

Do you recommend others to join the Analytics course at Imarticus? If yes, Why?

I would definitely recommend anyone looking for a course in analytics to join Imarticus primarily due to the exceptional faculty that this institution has. These professionals really have the in-depth practical knowledge of their respective domains which enables them to teach the curriculum in the best way that is beyond textbooks and according to the students’ needs.

These inclusive courses and teachers together don’t make you feel left behind even if you don’t have the prior knowledge of the domain and the extensive doubt clearing sessions always ensure that you are up to date with your syllabus without any doubts and difficulties.

What do you think about Imarticus Learnings’ Placement Services?

The people involved with placements at Imarticus are some of the hardest working individuals who work hard to ensure that the students get their dream jobs with the best companies possible. Not only do these people attract good companies, but also they assist the students in getting their dream jobs. Whether it’s working on our resume or preparing us for the most important interview in our lives, these people ensure that you are trained and equipped for everything that is to come.

It is only through the dedication of the faculty and the Placement services combined that Imarticus has been able to deliver such excellent placement results time after time and I definitely recommend anyone wanting a successful career opportunity to join Imarticus.

Interested in an analytics course? You can directly visit – Imarticus Learning and can drop your query by filling up a simple form on the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

 

How Is Big Data Analytics Used For Stock Market Trading?

Posted on by Imarticus Learning

Data drives decisions. The successful use of data-based applications already exists and is hugely popular too. Big Data Analytics is the decisive factor when you compete against the master traders on the stock market. A career in Data Analytics is highly satisfying and lucrative too! Most markets, verticals, and industries have inducted the applications of big data analytics to improve their marketing decisions, product selection, and competitive strategies.

The online stock market trading is no exception and is the one area where data analytics allows an on-par competitive platform of the finance domain which uses analytical strengths and strategies to its monetary advantage. There have been a large number of training institutes offering Big data analytics courses which can help you understand the nitty-gritty of data analytics as applied to the stock market trading.

How big data analytics is used for trading:

All, Big data Analytics Courses start with the importance of data, how it evolved into big data and the interconnection of big data analytics with AI, ML, programming techniques, and such topics. Across the board, companies, startups, and organizations use data analytics for forecasting, getting market insights, gauging market trends, business modeling and effective decision making.

Fields like healthcare, fintech startups, financial services, blockchain-based technologies, insurance, banking, and marketing make effective use of large volumes of big data readily available and growing fast today as the capstone of their key projects. The financial industry too has kept pace with such developments and offers many career aspirants a winning ticket to a career in the stock market.

The stock market rates, numbers of investors, key indices and prices are constantly changing. Each change generates data and considering such changes the total volumes of data is huger than huge volumes of petabytes of data. The ecosystem, landscape and trading process has gone completely online and real-time thanks to technology. Where once had to compute and take calculated risks based on very small windows into the data, today’s stock market has evolved over the last decade into the best example of the use of data analytics.

Let us explore the influence of big data analytics over the three major impacted areas.

Stabilizes and offers a level playing field for online trading:

Big data analytics depends on machine learning and algorithmic trading. The computers are trained to ingest, clean and use these large volumes of data much like the human brain processes information to do any task.

The ML enables the computers to use the real-time data which it rapidly processes to detect trends on the stock markets. Such representations and candlestick bar graphs are the basis of investor-decisions as they provide real-time information and can provide instant comparisons, present prices, other markets information and more to help compare and choose investment opportunities. This also provides a level uniform platform to all players, large or small.

Returns and outcomes estimation:

Big data analytics makes it possible to use powerful algorithms and AI to reduce possible risks in trading of stocks that takes place online and in real-time. The traders and financial analysts use the ability of data analytics to make forecasts and predictions regarding the prices and its behavior, trends and market behavior with accuracy and nearly instant speeds.

Improves ML to deliver forecasts and predictions.

ML in combination with big data makes a huge difference when taking strategic decisions based on a large data set that is far more logical than making inaccurate guesses and estimates. The data can then be reviewed and used in other applications if required to forecast market conditions, price trends, favorable conditions, and such factors on a real-time basis.

Conclusion:

Data analytics has immense potential for all from the professional to small-time hobby investors. You can learn from the Big data analytics courses and acquire a good grasp of trading practices, financial practices and knowledge of data analytics which are attributes that can be used even in making careers in a variety of fields where stocks are traded in. The payouts in any job will depend on the knowledge and skill proficiency in the trade and your ability to handle clients. Jobs in banks, as consultants and even as traders are available and obviously come with jaw-dropping commissions, salaries, and payouts.

Do your Big data analytics courses at Imarticus Learning and use the opportunity to make headway in your career.