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Category: Analytics

Analytics and Agriculture

Posted on by Imarticus Learning

Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.

Use of Big Data Analytics:

Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like

Cloud and Nanocomputing
Big data, digitalization and visualization use.
AI, IoT and ML use.
Saas Platforms, cloud services, and web-based apps.
Role of data and the data analyst:
Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.

Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.

Analytical technologies in the agriculture sector can be used effectively in

Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
Marketing of data and its visualization.
What is Smart Farming?
Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer

ML and data visualization techniques.
App-based integration for data extraction and education.
Monitoring through drones and satellites.
Cloud storage for securing large volumes of data.
Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.

What is Precision Agriculture?
This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.

Concluding with the challenges:
Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high. To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.

How Analytics is Helping Law Enforcement Prevent Crime?

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Spiralling rates of crimes is a deterrent to national growth, especially in the developing Indian economy. It impacts investments, tourism, and perceptions of growth both nationally and internationally thus impacting the stock markets too.

The need for constant analysis, regulation of and monitoring of crimes is the bounden duty of the government and the police. Analytics can prove to be the tool to effectively maintain order and law in India while also providing for safe tourism and image building as a strong and safe economy.

Data Analytics can help in crime detection, deriving suitable measures, actions, and conclusions and thus suggest and forecast necessary governmental measures for crime prevention. The field of analytics of crimes uses techniques and tools of statistical analysis which helps use the data available to detect and resolve crimes quickly. They can also be used in prosecution, evaluation of crime prevention measures, analysis and patterns of victims, criminals, modus operandi, traffic problems, the internal functioning of police departments, patrolling and strategizing.

Some of the ways in which the Indian police are making use of the advancements in technology and analytics capabilities are discussed briefly below.

The volumes of Big Data generated is stored for 28 years by the NCRB and runs into several petabytes of data in the form of audio clips, videos, text and chat files, footage from CCTV cameras, location files with GPS information, biometric information and so much more. The analytics, cleaning and storage issues are now being worked upon by the Indian police who use these records as a storehouse of both national and international criminals. This move helps in tactical and crime analysis of crimes thus curbing the crime rates.
Being able to forecast and map crime infested areas the police are now using ML for predictions, tracking persons and recommendations on curbing criminal activities.
The Delhi police in collaboration with ISRO have developed a satellite analytical method and system named CMAPS used for crime mapping, predictions, and analytics. This helps them maintain order, enforce laws and curb crimes.
In Jharkhand IIM Ranchi helps the police force implement systems, evaluate records, time and date of crimes, types of crimes with locations to be able to offer predictions, forecasts, and solutions to crimes in infested zones.
The police in Bangalore in association with IBM trains it’s officers on the state-of-art software for data analytics to make the best use of crime curbing techniques.
The Indian police systems are being updated with self-learning ML algorithms useful for crime analytics, analyzing large data volumes and providing accurate predictions and forecasts based on data accumulated and stored.
Predictions of crime-rates:

Such predictions fall into one of the below three groups.

Based on different crime types like dacoity, robbery, assault of women, trafficking of children, rape, molestations, etc.
Based on cluster parameters volume of lost property, lost lives numbers, the number of persons involved in the crime, modus operandi, method followed by the criminals, etc.
Based on area and its rate of crimes.
The Indian police follow the IACA or International-Association-of- Crime-Analysts techniques which use four distinct classes of functions to define its areas of analytical operations. They are

1) Analysis of crime-intelligence functions involving data on offenders, victims, networks, and organizations and is much lesser in India when compared to the USA.

2) Analysis of Tactical functions where the deployment of police human resources, patrol patterns and investigation are the major thrust areas.

3) Analysis of Strategic functions where policies, strategies, developmental issues, prevention, and evaluation methods and techniques are a priority.

4) Analysis of Administrative functions like administrative and interaction needs of the police, government interactions, and data regarding the police community is given precedence.

Concluding note:

Technological advancements, science, and analytics can help curb crime rates and make the common man’s life secure, happy and simple with efficient policing and measures to curb all kinds of crimes. The Indian police have embraced the changes wholeheartedly and look to succeed in their ventures detailed above with able help from institutions and the common people.

If you want to learn about how analytics can improve your life direct your queries regarding the Imarticus Learning courses to their 24×7 counselors. You too can make a difference in the field of analytics. Don’t wait.

How AI in The Energy Sector Can Help to Solve The Climate Crisis?

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How AI in the Energy Sector Can Help Solve the Climate Crisis

Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackle the climate crisis of all times.

The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.

Here is the list of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are

AI helps compile and process data:

We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.

AI can help reduce consumption of energy by ‘server farms’

The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.
The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.
The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.
The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.
Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.

Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.

If you care enough to be a part of this pressing solution to environmental change, learn at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.

Role of Peer to Peer Networks in Creating Transparency and Increased Usage of AI:

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With Amazon’s facial-recognition, face-IDs, use of facial-recognition at airports and on smart-phones, the police use of TASERs to immobilize suspects, and voice-cloning apps, the peer-to-peer networks aim of creating a transparent data system through increased usage of AI seems to have been accepted widely.

Artificial Intelligence applications have scored for their ease of operations; quick and unbelievable data-processing, identification capabilities, and flexible application amend-abilities.

The question of transparency has however been oft-discussed and flouted with impunity in instances of protecting privacy, ethical, legal and misuse issues. Selling of data to third parties, forced use of facial recognition, misuse of voice-cloning, and excessive use of TASERs did not result in data accountability. It appears to have become a nagging fear of constant governmental-surveillance and has come close to defeating the very purpose of its creation of transparency.

The following trends in 2018 may be important in the use of AI and transparent use of data which presently globally governments, countries and companies are vying to harness and control.

AI becomes the political focus

Some argue AI creates jobs while others claim to have lost work because of AI. A case in point is self-driven trucks and cars where more than 25 thousand workers become unemployed annually as per CNBC reports. The same is true in large depots working with very few employees. If the 2016 campaign of President Trump was about immigration and globalization, the midterms of 2018 would focus on rising unemployment due to the use of AI.

Peer-to-peer transparent networks will use blockchains

ML and AI used together are becoming useful in apps like Google, Facebook etc. where computing power and enormous data is processed in fractions of seconds to enable decision making. However, transparency in the decision making has been under a cloud and not in the control of users.

Peer-to-peer networks using block-chain technology transformed the financial sectors and are set to revitalize small industries and financial organisations functioning transparently. Ex: Presearch makes use of AI peer-to-peer networking to induce transparency in search-engines.

Other interesting trends using peer-to-peer networks and AI set to overhaul efficiency, transparency, productivity and profits are

Logistics and deliveries efficiency set to increase.
Self-driving cars rock.
Robo-cops will take-on action.
Content creation through AI.
Consumers and technology to become buddies.
Data scientists will rule in demand over engineers.
ML to aid and not replace workers.
AI will aid the health sector development.
The use of Siri, Alexa, and Google Assistant show that they use AI which currently understands advanced conversational nuances. The creation of robots, chatbots and such have raised questions of immortality, displacement of workers, and whether ML can be controlled at all to get machines to do what we humans tell them to do? It has become an issue of human wisdom vs AI- intelligence debate. Morality issues, misuse of intelligence, and subjective-experiences in humans allow us to feel, be ethical and transparent in use of AI intelligence data.

In conclusion, one must agree that the increased use of peer-to-peer networks, AI, ML, data analytics and predictive technologies are here to stay and can lead to increased transparency in data-transactions across sectors. Human wisdom and morality will be the traits that set us, humans, apart from our intelligent creations whose data-processing and learning capabilities and potential can fast spin out-of-control when these traits are not used to restrain AI.

Why Is Statistics Important For Data Science?

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Why is statistics important for Data Science?

Data Science is a scientific discipline, one that’s highly informed and dictated by computer science, mathematics, research, and applied sciences. Data is an integral part of today’s world– everyday individuals and corporations generate tonnes of data that can only be visualized and understood by experts.

Big Data Analytics Courses

Statistics provides the means and tools to find structure in big data as well as give individuals and organizations a deeper insight into what truths their data is showing. Statistics is one of the most fundamental steps of an insightful data science course– it’s also the linchpin that ties the whole process together from start to fruitful finish.

Finding structure in data, however large or small, and making predictions are crucial stages in data science that can make or break research. Statistical methods are the tool of choice here as using their methods, one can handle a plethora of analytical tasks to good results.

Enables classification and organization

This is a statistical method that’s used by the same name in the data science and mining fields. Classification is used to categorize available data into accurate, observable analyses. Such an organization is key for companies who plan to use these insights to make predictions and form business plans. It’s also the first step to making a massive dump of data usable.

Helps to calculate probability distribution and estimation 

These statistical methods are key to learning the basics of machine learning and algorithms like logistic regressions. Cross-validation and LOOCV techniques are also inherently statistical tools that have been brought into the Machine Learning and Data Analytics world for inference-based research, A/B, and hypothesis testing.

Finds structure in data

Companies often find themselves having to deal with massive dumps of data from a panoply of sources, each more complicated than the last. Statistics can help to spot anomalies and trends in this data, further allowing researchers to discard irrelevant data at a very early stage instead of sifting through data and wasting time, effort, and resources.

Facilitates statistical modeling

Data is made up of series upon series of complex interactions between factors and variables. To model these or display them in a coherent manner, statistical modeling using graphs and networks is key. This also helps to identify and account for the influence of hierarchies in global structures and escalate local models to a global scene.

Aids data visualization

Visualization in data is the representation and interpretation of found structures, models, and insights in interactive, understandable, and effective formats. It’s also crucial that these formats be easy to update– this way, nothing needs to undergo a huge overhaul each time there’s a fluctuation in data.

Beyond this, data analytics representations also use the same display formats as statistics– graphs, pie charts, histograms, and the like. Not only does this make data more readable and interesting, but it also makes it much easier to spot trends or flaws and offset or enhance them as required.

Facilitates understanding of distributions in model-based data analytics

Statistics can help to identify clusters in data or even additional structures that are dependent on space, time, and other variable factors. Reporting on values and networks without statistical distribution methods can lead to estimates that don’t account for variability, which can make or break your results. Small wonder, then, that the method of distribution is a key contributor to statistics and to data analytics and visualization as a whole.

Aids in mathematical analysis and reduces assumptions

The basics of mathematical analysis– differentiability and continuity– also form the base of many major ML/ AI/ data analytics algorithms. Neural networks in deep learning are effectively guided by the shift in perspective that is differential programming.

Predictive power is key in how effective a data analytics algorithm or model is. The rule of thumb is that the lesser the assumptions made, the higher the model’s predictive power. Statistics help to bring down the rate of assumptions, thereby making models a lot more accurate and usable.

In just 2018, 16,000 freshers got enviable jobs in the analytics workforce, so the demand is high and unceasing. However, a mistake quite a few undergraduates make is majoring in Computer Science if there isn’t a course fully dedicated to data analytics, machine learning, or AI.

The fact of the matter is that ‘deep learning is applied statistics in disguise’! For more details, you can also visit – Imarticus Learning and can drop your query by filling up a simple form through the site or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.

How Is Data Science Changing The Process Of Web Design?

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A data science course will cover a lot of ground in learning about data sciences and its interlinkages with the creation and management of data and involves research and studies into how data is used to accomplish tasks that can change processes in web management and design.
Let us now explore how AI and data sciences have actually impacted web design.
AI and data analysis:
Today’s AI devices no longer depend on humans for the input of data or limit their insights to only the data inputs provided. Rather, self-learning AI devices are scoring and ML algorithms can not only clean and format large volumes of data they have turned self-learning and making predictions from data across the board in different formats and from different sources a breeze. This impacts web design too, as like AI it depends on the human interpretation of data and making gainful insights.
The web design process:
The normal process of web design will start with seeking information from various surveys and focus groups and this data is banded together, organized, cleaned, formatted, assimilated and organized by a team of human beings. Next, the coding process begins. A model or prototype for coding is drawn up and this is tested through beta-testing.
Once it emerges successfully from testing the software is declared ready for use. However, many inadvertent flaws end up in the code and land in the final design for want of effective prevention methods to vulnerabilities in coding. These are set right while in use through newer versions and updates. The modern Data Science Course uses AI and data science to create and reuse code to make it secure against such vulnerabilities.
Focus on AI and Data Analysis:
A change from the current methods of web design allows AI and data analysis to function differently from the current focus groups. Data that is collected is taken up by AI for in-depth data analysis using the vast swathes of Internet resources. This provides for the ability to overcome human errors, improve coding, streamline the creation process and create more security. This, in turn, increases the traffic of users, figures in the search engines with SEO optimization and enhances the web designs.
Use of Code:
The use of coding is an integral part of web design. What will, however, change with AI handling the coding from scratch is that one would no longer look for solutions to fix vulnerabilities. It would just mean perfect coding without vulnerabilities and most users, governments, businesses, and enterprises using such data science course solutions will no longer have to worry about Internet threats and hackers or data breaches and unauthorized tampering of data. Web designing stands to gain in many ways like keeping the app developers and owners of the websites free from the problems of the past, ensuring the protection of proprietary data, and encouraging the online conduct of business.
Apps and new versions:
In the near future, it will be AI that decides in conjunction with ML, DL and more how new software gets designed, why a website menu needs to be a certain way, which apps are best suited and whether updates or re-versioning is needed for any particular app. With the increased sophistication brought in more repetitive tasks will be handled by bots and apps based on AI and thus leave more quality time for business decisions and doing business online. At the end of the day, manual human intervention will also be more sophisticated and need more in-depth skills to handle such changes. Web designing is surely set to become possible on much smaller budgets.
Opportunities for web designers:
A very potent question doing the rounds today is that AI will eventually cost the jobs of current web designers and coders. Just remember that technological advances do mean some jobs will be displaced but at the same time newer requirements and jobs are being created for those willing to tweak and make good use of their skills. AI is never about replacing human intervention. Rather it is about aiding human intervention in data sciences, ML, DL, and other emerging technologies. Experienced web designers should do a re-skilling course to stay abreast of the changes and be exposed to newer emerging jobs where the payouts and demand are bound to be much higher.
The Bottom Line
On the never static Internet subjects like AI, DL, ML and such emerging technologies hold great potential and should be embraced by doing a data science course. The reputed Imarticus Learning Institute is where you should head to for comprehensive learning and skill assimilation in emerging technologies.
For more details, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

Why Do Data Scientists Need To Learn Java?

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Java has today regained its prominence as the most popular language suite for developers and has outrun both R and Python. This is not surprising since Java boasts of the largest community of developers and also has applicability, compatibility, and ease of learning to aid it. AI, ML, and data sciences are all relying on the JavaScript suite and its applications and these are the areas seeing rapid evolution and need for personnel.

Further, when demand rises the payouts get better. Career aspirants and career-changers both are ready to learn data science and are flocking to these fields and this only adds to the popularity of Java as the ultimate weapon in the developer’s kit.

Here are the top reasons to learn data science and Java.

  1. The old-gold class: Being the oldest language in enterprise development it is frequently found that legacy systems have their infrastructure already running on Java. This means you have probably used R or Python for modeling and have to rewrite the models to suit the system running in Java.
  2. Wide frameworks: The Big-Data tools and frameworks like Flink, Spark, Hive, Hadoop and Spark are Java-based. Familiarity in the Java-stack is thus easier for analysts working with large data volumes and big data with Hive and Hadoop.
  3. Libraries aplenty: Java has toolsets and a great variety of libraries for ML applications and data science applications. Take a look at Deeplearning4j, Java-ML, Weka, or MLlib to quickly resolve and issues in data science.
  4. REPL and Lambdas: While Lambdas that came with Java 8 altered the verbosity in Java, the recent REPL of Java 9 adds iterative development to the developer kit. It is now easy to learn and work in Java than it initially was.
  5. Virtual Machine in Java:  JVM helps write multi-platform identical codes facilitating rapid customization of the tools required. With the IDEs variety on offer, developers can be more productive.
  6. Strongly Typed: This does not refer to classic static typing. Rather it deals with Java being able to specify the types of variables and data the developer needs to work with. The strong typing feature is especially useful in large data applications and is a feature that is well-worth the developer’s time in avoiding trivial unit test writing and in maintaining the code base of applications.
  7. Scala in JVM: Heavy data applications make learning Scala easier when you code in Java. The Scala framework is awesome since it provides data science support and other frameworks of the likes of Spark can be built atop it.
  8. Provides jobs: Other than the SQL requirements, it is Java that is most popular in the job-space as per the chart indexed below. All the more reason to learn data science and Java for developers!
  9. Scalability: Application scaling in Java is rapid and excellent making it the developer’s choice for writing complex and larger AI ML applications. Especially so if you are writing the program ground-up since then you only need the one language of Java coding.
  10. Speed: Java is fast and provides for fast integration in heavy large-scale applications. The likes of LinkedIn, Facebook and Twitter rely on Java for heavy data engineering.

A data scientist/ developer is the one who is the single point of contact for the data itself. They take the data both structured or unstructured and use a wide variety of engineering, statistical, mathematical, and programming skills to spot trends and arrange the data organizing and managing the data to resolve the targeted outcomes. In essence, they are the people the analysts look up to for the data they need to analyze.

Practical skills required:

Let the truth be told, even if you do your master’s or a Ph.D., to be a good and effective data scientist you will need to also garner training for technical skills in:

  • Proficiency in social sciences
  • Programming in R and Python
  • Coding and writing with the Java suite
  • BigData querying  on Hadoop framework
  • Coding and SQL-Databases
  • Apache Spark
  • AI, ML, and Neural networks
  • Visualization of data
  • Working with unstructured data

You could also bolster your knowledge in managing data through online MOOCs, tutorials, and courses. Ensure your training partner for paid courses is a reputed institute like Imarticus Learning as they offer to train you for professional certifications and also award certifications that are valued in the industry.

Conclusion:

If you’re an analyst, Data Scientist, Deep Learning or ML Engineer the Java skill quotient is worth improving when you are eyeing lucrative and in-demand development jobs. You should learn data science and Java at Imarticus Learning if you want to stay ahead of the job-curve.

For more detailed information regarding this and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

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.