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Category: Analytics

How Companies Use Machine Learning

Posted on by Imarticus Learning

How Companies Use Machine Learning

Machine Learning and data processing has changed drastically the way things work inenterprises and even our daily lives. Digital technology has been able to enablemachines with ML software and algorithms to process intelligently and unsupervised the large volumes of data generated. The advent of the internet and such limitless uninterrupted data processing has generated many an error-free gainful insight.

Businesses can now transform to the high-efficiency mode where profits increase by creative use of employee time in using the insights and forecasts provided by machine learning, data analytics, big data processing, and accurate predictive analysis.

What are companies using ML in?

Learning and Scanning data images, text and voice: Repetitive tasks and tasks that are labour-intensive are now a one-step zero-error machine process. Digitizing data has scored in the following areas.

  • Data entry, documentation and report generation: The way data is processed, the volumes of data available, used and predictive analysis of data analytics have impacted lives and businesses to upgrade and upskill for better efficiency and profits.
  • Image Interpretations: Complex insights are possible with accurate predictive insights which have huge ramifications in the film, media, health, banking, insurance sectors and more.
  • Previewing videos: Data previewing in video form can help to process in speeds far higher than humans could ever think of. They can also match the videos to preferences of people, match advertisements to these, edit and curate video footage in fractions of a second! The advertising, marketing, media, film, and video industry has been transformed forever. The revenues generated with accompanied efficiency and speed has led to collaborations of machines and humans in a positive manner.

Uncovering and forecasting insights: ML has truly transformed the way we function with computers and ML replacing routine, repetitive tasks. Notably, the following sectors have improved tremendously.

Monitoring Markets: Mining of big data can result in time-saving and provides lead time in relevant and urgent monitoring of opportunities. News channels, competing in the business world, taking corrective actions and strategising have become a matter of nanoseconds with ML.

  • Root cause analysis: This technique used in production lines can predict and forecast failures of tasks, identify the root cause of the issues, suggest strategy changes required and generate alerts in these conditions.
  • Predictive maintenance: This tool is most effective in its forecasting abilities and ensures there will be no downtime in functioning.
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  • Predictive modelling: ML has enabled matching customer profiles and preferences to products available and browsing history-making auto-suggestions a routine affair. The huge potential of generating through advertisements matched to such preferences can generate more efficiency and high revenues.

With the advent and use of ML in everything you do, there is an urgent need for collaborators who can tweak software, create new applications, use the predictive and forecasting alerts and insights gainfully to improve profits, efficiency and save time, effort and costs. It is still early days and the right time to upgrade and re-skill with machine learning courses that will enable smart and creative use of machine learning benefits mentioned above.

Big data Hadoop training courses are also required to help ML understand and use the mind-boggling quantities of data that is now usable. Without the will to effectively use data and the training needed to adapt you will be left far behind. The situation today is adapt, or stay behind! 

How Will Imarticus’ Business Analyst Certification Course Help You Advance Your Career?

Posted on by Imarticus Learning

The market today has seen impressive use of technology in making use of artificial intelligence, machine learning, data analytics, and predictive analysis. All pervasive use of internet and smart systems like mobile phones, computers, use of virtual assistants, interactive apps, and so on, are everyday uses.
The Analytics and Financial Services sectors have ridden the wave of digitisation and are urgently in need of dedicated and experts in technological domains that did not exist a decade ago. Imarticus Learning caters to the aspiring needing tailor-made customized programs to further their career and prospects through skills up gradation with its innovative business analysis course in Bangalore.

Roles And Scope:

With thousands of jobs being advertised daily some with mind-boggling pay packets, a business analyst certification with Scrum and Agile BA can help in the following areas of professional empowerment.
Change management and its allied roles in Project Management, Business Analysis, Scrum and Agile suites, and SAP learning will equip and hone your skills to be able to successfully induce changes for enterprise success. Lucrative roles such as Scrum Master, Change Manager, Project Manager, or a Business Analyst could be yours with this skill upgrade.
In the role of a Business Analyst involvement in strategy and plans for change management and its implementation is crucial to the enterprise. Skills acquired here will help involvement with stakeholders and puts you in a pivotal role.
As a Scrum Master Agile helps you manage projects, overcome impediments, manage team health, eliminate wastage, manage self-organisation, apply process management tenets, and manage diverse cultures.

Why choose Imarticus Learning?

This enterprise provides:

  • The basic prerequisite is the will to learn and other secondary supportive qualifications
  • Have centres at Mumbai, where it is headquartered, Chennai, Bangalore, Pune, Jaipur, Delhi, Coimbatore, and Hyderabad
  • Online and virtual classroom instructor-led sessions pan-India
  • Training faculty includes mentoring expertise by veterans from the industry who are domain experts and share practical hands-on training through real-time scenarios.
  • Further core competency skill upgrades offered in evolving fields of technology.
  • Project work and use of real-time scenarios enable you to imbibe best practices and being industry-ready.
  • Believe in practice and hands-on training rather than theoretical learning of concepts.

Accepted certification and opportunities to better your job prospects and earn lucrative pay packets.
Business analysis classes with Imarticus includes:

  • Global and standardised curriculum that is offered internationally.
  • Building resumes that make a lasting impression and translate into landing lucrative job offers.
  • Open and private networks access for placements on the portal which provide opportunities, references and leads for successful placements.
  • Conduct of mock scenarios and interviews with panels for one-on-one training for these rounds through well-experienced and certified trainer instructors.
  • Preparation for interviews through well-researched questions to help clear the technical and HR rounds of your interview.
    Why wait any longer? Now is the time to enrol and upgrade your skill sets.

What Are The Advantages Of Business Competition With Deep Learning?

Posted on by Imarticus Learning

We all produce data. Enterprises have their own data. But as the adage goes, the wise learn from the past. Today, machines, robots, and software are smart. Machine Learning has in the past decade transformed software to help machines learn unsupervised from data. Deep Learning is the subset which helps ML learn from data that is unstructured. Humans are limited by data.
ML process huge quantities of data and learns patterns and can thus give you recommendations on Facebook based on your browsing history and use or suggest interesting videos on YouTube on your smartphone. It is now time to use ML intelligently in your business enterprise or career and stay abreast with the latest upgrades or be left behind.

Advantages in Business:

Primarily three benefits accrue with Deep Learning.

  1. Time and Cost benefits: Most employees do the same repetitive job day in and day out. Neural networks have given artificial intelligence the brains to use data, learn both supervised and unsupervised from it and use it to perform such repetitive tasks. In terms of time saved the employees are now free to use their time on creative tasks. Money in hiring more employees to handle large data generated is saved. ML never sleeps or takes a holiday. With the potential to offer so much saving of time and money it is well worth the investment of ML.
  2. Quality scores with accurate results: Human emotions bias our results and output. ML, on the other hand, is error-free learning with no emotional bias. In processing data and for repeating tasks in a production line such errors can be costly. ML also needs no food, sleep or breaks. With highly accurate results that can be preset and traverses data with multi-variables and time constraints cutting across all departments and sources of data ML has the ability to improve quality and efficiency. The obvious outcomes are better organisations, speedy deliveries, accurate results, and high efficiency.
  3. Growth in jobs: ML needs humans to program them and to use the insights provided by them in newer applications. This definitely means more humans are required with an understanding of ML. But such human intervention and supervision need an in-depth knowledge of ML, data analytics, deep learning, and artificial intelligence.

    What Should You Do About It?

    If you are an employer, then it is time your employees were re-trained and learn how to use ML to the advantage of the enterprise creatively. Encourage employees to upgrade and up-skill with machine learning courses. This will offer them better prospects and pay packets because of increased efficiency.

    Why Do Machine Learning Courses In India?

    India today is an emerging hub of innovation with huge potential in terms of trained manpower and resources in training, expertise in software programming and high demand for good quality workers and software knowledge. Large enterprises need smaller businesses to get their tasks done and this, in turn, means job generation. If you have the right knowledge and the skill to use it your career will know no bounds. That’s why should know what is so trendy about Machine Learning courses is a step in the right direction for you!
    Growing your employees means business growth which becomes super-efficient and organised, offering better returns and results. Make your move today. It is a win-win situation for both the workers and the business.

A Beginner’s Guide- ‘Books for Learning Artificial Intelligence’

Posted on by Imarticus Learning

Data collections are readily available with most enterprises. However, one has to learn how to program with artificial intelligence systems like the computer to be able to understand the data and use the computer to get it to assimilate the data, learn from it and present the data after its due analysis.

How to do an AI course?

This process of AI, data analytics, machine learning and predictive forecasts based on the analysis is what most machine learning and artificial intelligence courses teach.
There are many books and free materials in the form of books that one can read and learn from to understand these concepts. One can do the course in virtual classrooms, one-to-one learning or even practice after reading online.
Some of the best books to learn AI are:
Thomas Laville’s Deep Learning for beginners and Artificial Intelligence by the same author, Malcolm Frank and others titled “When machines do everything”, James Barrat’s Our Final Invention, Michael Taylor’s Neural Networks, and many others like Grokking Algorithms, Introduction to Machine Learning with Python, and Python Machine Learning by example which are sold on Amazon.

How to do an ML course?

Machine learning courses incorporate the learning of neural systems, characterization trees, vector machines bolstering and boosting techniques. To understand how mining systems work, one must also learn how to actualize strategies in R labs, and themes related to automatic calculations, hypothesis etc.

Free Books on AI and ML

To learn machine learning or the use of AI which enables the system to learn from data assimilated without being modified to do so, use the top five free books to help you master ML.
Shai Ben-David and Shai Shalev-Schwartz presentation of Understanding Machine Learning will teach you the basics of ML, its principles, how it uses numerical data to make useful calculations and more. As in the title, it covers all theory regarding algorithms, their standards, neural systems, stochastic plunge slope, developing a hypothesis, ideas, and organised yield learning.
Andrew NG’s Machine Learning Yearning is about getting to be good at AI frameworks building.
Allen B. Downey’s Think Stats will help Python developers understand the subjects and help you make investigative inquiries from data collections.
Other excellent books for beginners to get fluent are Cam Davidson-Pilon’s Probabilistic Programming on Bayesian strategies, derivations and likelihood hypothesis, Trevor Hastie, Jerome Friedman and Robert Tibshirani writings of The Elements of Statistical Learning for learning how to get to unsupervised learning from administered data learning.
There are a vast variety of courses, free materials and visual aids to help with the learning process. The scope for enriching one’s knowledge, especially when required to learn new skills and upgrade one’s knowledge, can never end. Technology is in a state of flux and rapidly changing to embrace newer innovations across more sectors and uses designed to make AI, ML, visualization and deep learning of data and its analytics essential to understand and succeed in business, careers and all fields of applications. It is the will to get there that really matters.

7 Horrible Mistakes you’re making with Artificial Intelligence

Posted on by Imarticus Learning

We could notice that, numerous marketers commit mistakes with regards to AI. That is a common thing. We’ve done it, as well. It requires a lot of investment to get settled with AI.
In any case, a few mistakes are more inflated than others. Furthermore, these mistakes will bring your association down the wrong track with regards to AI.
No one wants to get in that bad situation. So to prevent this issue and to get benefit from AI later, marketers should think to avoid below 7 horrible mistakes that they are making while implementing a machine learning or an artificial intelligence course.
1. Thinking AI usage is simple.
Several marketers think if they have the accurate information, implementation is easy. a few of the AI tools are very simple to utilize and you can begin quickly. But transforming your association into an AI-driven organization is another responsibility completely. Receiving AI association wide requires some serious energy. It needs cash. What’s more, it takes experimentation.
You need to commit for the long period. In reality, the correct data and methodology are fundamental. Implementation is secondarily come!
2. Marking down artificial intelligence altogether.
The opposite side of the coin is advertisers who trust AI are all publicity. We get it. There is a huge amount of promotion out there and a ton of extremely strong claims. Normally, you may trust AI is simply one more popular buzzword.
Nothing could be further from reality. Over the most recent couple of years, critical advances in AI and machine learning have happened. This is an undeniable, exceptionally impact arrangement of technologies that will influence your profession.
3. Focusing on complete automation
Businesses aiming for entire automation process might merely save the salaries of the populace being supposedly substituted by AI. As per Jeremy, businesses that target to make a return on the employees by enhancing and rising workforce competence using AI would attain noteworthy ROI.
4. Fixating on where AI is going.
We get it. We cherish guessing about where AI is going. We even have a deadline for when our machine overlords will make their play for global control. But an excess of hypothesis on the most distant eventual fate of AI is diverting.
There are numerous miracles ahead as we enter the period of AI. Give yourself a little AI wandering off in fantasy land time, beyond any doubt. But, at that point discover a couple genuine implementation cases you can begin applying AI to begin at this point.
5. Thinking beginning with AI is too hard or excessively specialized.
It certainly requires some investment to get settled with ideas in Artificial Intelligence. What’s more, profoundly understanding the tech probably won’t be simple for the non-engineers among us.
This is not a regulation only for the technicians. As an advertiser, you have a gigantic chance to attach the specialized to the commonsense and discover genuine implementation cases for AI.
6. An inadequate foundation for machine learning
For most associations, dealing with the different parts of the foundation encompassing machine learning exercises can turn into a test all by itself. Trusted and dependable social database service frameworks can bomb totally under the load an assortment of data that organizations seek out to collect and investigate today.
7. Assuming AI can’t execute whatever marketers perform.
Indeed, even with a sound thankfulness for AI’s potential, it’s anything but difficult to laugh at it. How might it displace you or your partners? We can’t wait how ground-breaking AI will be, so we’re not saying it’ll replace anybody. Yet, it will change the idea of your work.
AI can do the plethora of things that marketers do today, quicker, less expensive and at scale. Inside this reality lies either guarantee or risk, reliant upon how you see it.
Marketers need to turn an attentive eye to how they create importance for associations and highlight the high-esteem innovative work

7 Awesome Lessons You Can Learn From Studying Data Science

Posted on by Imarticus Learning

Data archives have increased exponentially, and it’s posing great challenges to various industries. Fortunately, they have gone beyond ‘What is Data Science?’ to finally adopt Data Science analysis, to derive something meaningful and productive from the existing pile of information.
Today there are many takers for data science training, and people have learnt some important lessons. Let’s discuss some of them:   
It’s important to understand business as a whole
At times people give too much emphasis on technical knowledge and out the domain knowledge on the backburner. This way they end up creating a sophisticated model without really understanding the business needs. Such models don’t add much value to the business, regardless of their accuracy.
As a data scientist, one needs to understand a business through the eyes of data. Only having the technical knowledge won’t help you articulate your ideas to colleagues in the context of business. So, besides Jargons, it’s important to learn the commonly used terms pertaining to a business.
It’s important to have a penchant for details
Data scientists can’t carry out data cleansing and transformation without having an eye for details. Data in real-world scenarios is never arranged perfectly, and one needs to isolate a lot of noise from it, to arrive at something meaningful. So, a detail-oriented mindset is a must to succeed in Data Science. Without that, you may not derive insightful results from your Exploratory Data Analysis. You may put your heart and soul into the data cleaning process, but still the data might not be reliable enough to be used by your Model.
Framing logic and designing an experiment
Machine learning problems are not that complicated, as you just need some data for training purpose in order to build your model. In case of Data science, there is a well-structured workflow that provides a larger picture of the undergoing processes (Data cleaning to Model interpretations). There is a component called Experiment which is a part of the workflow. It includes the logic for hypothesis testing and Model building.
Therefore, data science helps in framing a logic and designing an experiment for real-world scenarios, to test certain assumptions and evaluate the Model. You can understand more about this aspect by opting for a Data Science course.
Communication skills
If you are a Data Scientist, you better enhance your communication skills, as it will help you sail through. As mentioned earlier in the write-up, there is no point in acquiring the technical knowledge and crunching the data all day long, if you can’t communicate your ideas to stakeholders in a business-friendly language. This affects your credibility as well as your professional relationships. In short, it’s a lose-lose situation.
As a data scientist, your biggest challenge is to put forward your most complex ideas and insights in a layman’s language, so that even a 15 years old can understand them. Your language should make your colleagues feel empowered, so that they can invest emotionally and intellectually.
Art of Storytelling
If you think that Data Science is all about crunching data and building models, then you are mistaken. It’s also about weaving a compelling story based on data analysis, to indulge the stakeholders. Depending on the project goals, the story should cover the following questions:
> What’s the reason to analyze the data?
> What insights can be obtained from the results?
> Can any action plans be derived out of the analysis?
Often the art of storytelling is ignored over data-driven analysis. Lousy storytelling or boring presentations, can greatly undermine the valuable results from even some of the best models.
It’s important to set a benchmark for comparison
It’s naïve to assess the efficiency of a Model without comparing it with other models. Without a benchmark, it always difficult to define ‘What is good’, and the results can’t be fully trusted.
Art of Risk management
Every Model is built, keeping in mind the best and the worst-case scenarios. You are required to explain your model’s limitations to stakeholders, and how much risk can the company potentially bear if the model goes to production. This is where Risk Management comes into picture. If you understand what’s at stake and have a plan to minimize the risk involved, then only you can take stakeholders into confidence. To understand more about the art of risk management, you may opt for a Data science course.
Intensive Data Science training can help you realize the above-mentioned lessons. If you are an aspiring Data Scientist, we hope this write-up served the purpose.

7 Answers to the Most Frequently Asked Questions About Artificial Intelligence

Posted on by Imarticus Learning

One of the most trending topics in the today’s world is Artificial Intelligence. From self-driving cars to Siri, the world is now overwhelmed with automation. Technically, Artificial Intelligence is seen as a robot possessing characteristics similar to humans. However, AI has a lot more to offer. IBM Watson or automated weapons, facial recognition or search algorithms, AI explores every aspect of human life.
Hearing so much of this much-hyped technology, it is obvious that a pool of doubts strikes your mind. From what to who and how you might be too curious to know what Al does and has to offer. Here, we outline 7 most of the frequently asked questions about Artificial Intelligence.
Questions:

  1. Why is the need to employ artificial intelligence?

 Ans: Artificial Intelligence is an abode of automation. Till date, programmers required to hardcode the instructions in files to have the robots working. But, with the advent of machine learning & Artificial Intelligence courses, IT Industry has taken a steep turn. Now, we expect the programs to learn rather than simply follow what it has been coded with. Myriads of tasks that seek human intelligence are effectively automated with Artificial Intelligence. It is simply a pool a data. The more, the merrier. Health care, education, agriculture, and business are few industries that have readily adopted this new technology and driving benefits too. Cutting unnecessary human labor by replicating them with AI made machines. Also, jobs that pose threat to human life can be efficiently done with the help of AI.

  1. Why do you need to study Artificial Intelligence?

 Ans: Before we head towards the prime need to study AI, let’s see it’s application. Siri or Google assistant are not just fun but cuts downtime of typing while surfing something. Playing chess with a virtual opponent seems amazing. Agree? Do you have an idea that Google translate employs AI or the spam blockers incorporating AI?
We all use it but are not aware of it. Studying AI would not just open up a pool of job opportunities for you, but on s lung run turn you more subjective towards things you notice or witness. You would be able to link technologies better. AI is such a subject that is ever evolving and hitting a job in such a domain would change your life.

  1. How can we apply AI?

 Ans: In order to use AI, you don’t need to be a programmer or work in any IT industry. We are presently using products of AI and they have genuinely made our life simpler and easier.

  • Siri: The smart assistants are used almost every day. Though they are a little slow, yet it is expected by 2025, they will gradually occupy the business world.
  • Chatbots: The most effective application of Artificial Intelligence is learning ways to build chatbots. Currently, chatbots are based on rules and do not employ AI. So, the near future would definitely look forward to the implementation of AI in the creation of Chatbots.
  1. What kinds of jobs are related to AI?

 Ans: There induction of AI has opened doors for Myriads of job opportunities. What we lack is the resources to fulfill the need. Few jobs directly related to the field of AI include:

  • Data Scientist
  • Research Scientist
  • Machine Learning Experts.
  • Deep Learning Experts
  • Software Engineers.

In the future, there would be a need for chat bots designer, business strategy consultant and many more opportunities for a job.

  1. Name some powerful agencies inducing Artificial Intelligence?

 Ans: Seeing such a massive growth in the field of AI, almost all companies are now thriving to rank one in the underlying field. However, the major ones include:

  • Google
  • Amazon
  • Microsoft
  • Facebook
  • IBM
  • Apple

Google leads all of the above mentioned and apple being the weakest. Amazon Alexa software makes it rank in after Google.

  1. What are the benefits of AI technology?

 Ans: AI is expected to change the life of all across the globe dramatically. It could be direct or indirect but the benefits are huge. Some of the key benefits are listed below.

  • AI is expected to facilitate better life reducing poverty.
  • Dangerous tasks can be efficiently performed by AI.
  • AI would automate vehicles, thereby easing travel.
  • AI would create a pool of opportunities for business professional.
  1. Would AI be a threat?

 Ans: This has a twofold answer. Where few consider it as a threat to eliminating the need for human sole believe it would create job opportunities. Some consider to would ease life, few think it would create endless issues. Now, what you believe depends on you. However, everything is a threat is over-utilized. Agree?

Will Data Analytics Ever Rule the World?

Posted on by Imarticus Learning

Of late, there has been a sudden surge of data analytics in the world. This will undoubtedly change the way people live and trade in the market. The use of data analysis tools is increasingly used in different technology devices for carrying out several day-to-day decisions in professional lives. It helps people to drive the business smoothly by identifying waste and blank spots seeking the help of different data analytic tools.

Although the companies are finding crunch in leveraging the ideas of this field, yet several global surveys reveal that it has the capacity of make the impossible possible, and it is still in the early stage of the data age. Today, most of the companies are investing in data analytics capacities by creating data analyst jobs are merely to remain in the competition. Data analytics have a great future, and it has the potential to rule the world.

Data Analytics- The Present and the Future
The data analytics development cycle can be defined in different stages. It starts from Descriptive to Diagnostic stage – The former deals with what happened, while the latter explains why did it happen? Then comes the stage of discovery followed by predictive. The former deals with everything that helps us to learn from and the latter talks about the things those are likely to happen.

Lastly, the prescriptive analytics that deals with what kind of action is to be taken. Generally speaking, the organizations today are in the first stage (diagnostic and discovery stages).

In order words, the data analyst jobs are simply helping companies to make informed and better decisions than before. With proper use of data analysis tools, it has become simple to blend a number of multiple data sources giving away the insights.     Thus experts feel that it would be the backbone of a decision-making process, which will end up in producing a better outcome. The Google Car is the classic example of it.

The impact on Business
There will be a radical change in business with the use of data analytics. More and more new data analyst jobs will be created and the job profiles would change with the growth of the market by unleashing the power of this field. With the passage of time, the number of data analysis tools will keep on adding new capabilities, which will help in managing and storing the data effectively.

Also, there will be newer methods of analyzing the data will emerge seeking the help of cognitive analytics and machine learning ideas. This will further help in giving few professions. Currently, IBM Watson and MS Cortana are among the forerunners in this domain. So, the days of asking what is data analytics, are now gone as the world are in the transition phase and soon would have data analytics dominating everywhere.

The Opportunities
The modern day smart devices are easily able to share data with the Internet of Things and are able to deliver massive amounts of data. These include the sensor data including location, health weather, machine data, and error messages to name a few. This will help in honing diagnostic and predictive analytics capabilities. Things would turn inexpensive, as people will be able to exchange the supplies even when it is not required, however, with this you can boost up the uptime.

Also, the coming time will make things simple and user-friendly to connect all types of data from numerous sources to each other. This will end up giving the insights in real time. You will be able to solve all your issues in minimal time duration, which will further settle down the challenges of business and IT alignment. These challenges will not be seen in the coming years with the advancements in data analytics courses and technologies.

Wrapping up
Needless to say that data analytics will rule the world. Currently, the world is passing through the transition as data analytics remain in the nascent stage. However, with ongoing research and development in this field, the data analyst jobs with better insight and capacities will increase and change the phase of the world. So, if you are planning to join any data analytics course, it’s the right time to invest.

How Startups E-Commerce Firms Will Be Campus Hiring In 2019?

Posted on by Imarticus Learning

Those who graduate in 2019 may receive lucrative offers from startups and e-commerce companies. Well-known companies like Amazon, OYO, Droom, ShopClues, and Shadowfax will be partly staffed through campus hiring. Noteworthy is the fact that they are aggressively growing, full of funds and are adding to the institutes in their talent search.

Pay Packets and Areas of Hiring

Departments and sectors like product management, business development, marketing, finance, HR, operations, software development, digital transformation, and data analytics are expected to see payouts to the tune of Rs 30 lakhs per annum according to business schools and the companies.

The List of Who, Where and How Much?

The September unicorn OYO will be looking to recruit around 400 personnel from reputed institutions like the ISB, IITs, IIFT, IIMs, TISS, XLRI, NITs, and ICAI. They will be offering sign-up bonuses for select candidates. They were recently recipients of 800 million dollars in funds and recruited 200 persons in 2018.
Amazon has been aggressively offering placements to the summer 2020 batch. Artificial Intelligence, visualisation and cloud technology, machine learning, and data analytics have emerged as priority sectors for Business Analyst Courses Online hoping to receive their offers.

Agile Startups like Amazon with a compound growth calculated annually of 40% in hiring and institutes like the IIM Bangalore, see high interest in the Agile environment. This is an obvious ramp-up of hiring and talent acquisition through summer placements by startups and companies in e-commerce which far exceeds hiring expectations. Amazon’s success-run in India has prompted it to hire superstars for their fast-evolving Agile environment through campus recruitments.
Agile business scrum prodegree with SAP would make perfect sense for being hired by Drrom the marketplace for automobiles. They are scouting for fresh talent in marketing, technology and product management from reputed institutes. They will be visiting the IIT s at Roorkee and Delhi, ISB, FMS, IIM Lucknow, Kozhikode, Ahmadabad, Kashipur and Indore, and IIFT.
Imarticus Learning may hire 30 candidates from Mithibai, Don Bosco Institute of Technology, HR College, Welingkar, and KJ Somaiya, in 2019.
Shadowfax from logistics may hire 20 students in 2019 from the visiting NITs, IITs, BITS through AccioJobs also a startup handling campus recruitments. Salaries could range for starting salaries and up to 20 lakhs and are role based.
ShopClues the e-commerce market space has doubled its offers and is scouring the IITs in Delhi, Bombay, FMS, IIFT, ISB and reputed 2 or 3 tier town reputed institutes. The roles on offer are software engineer, designers in user-experience, analytics specialist, data scientist, product manager, digital marketer, and category manager.
Expansion does depend on quality manpower after all!

Reference:

https://tech.economictimes.indiatimes.com/news/corporate/startups-e-commerce-firms-plan-to-step-up-campus-hiring-in-2019/66551215

How AT and Analytics Work at OpenText Enterprise World

Posted on by Imarticus Learning

OpenText customers must surely understand the value that Artificial Intelligence Systems and analytics offers to them, by now. AI is quickly trending around the world as the go-to system for many tech enterprises since it offers the capability of processing a large amount of data within a short time to get some amazing insights and suggestions from that.
OpenText is one of the leaders in Enterprise Information Management, and works to create an intelligent, connected enterprise for the benefits of its customers. Such a structure can be created only after in-depth analyses of large amounts of data – however, it leads to some amazing insights for the customers to benefits from.
OpenText employs the best minds in the industry, with most of the employees learning to use business analytics and undergoing data science courses in some capacity or the other. This is done so as to get a competitive advantage over the others, and thus provide better services to the customers. Some of the questions dealt with in the OpenText conference, regarding their functioning includes the ones discussed below.

AI Or BI?

Business Intelligence refers to the long tradition of using analytics to get a competitive edge in business processes, by qualitatively analysing the data available. Artificial Intelligence or AI is a more recent development – using computers and machine learning algorithms, large troves of data are analysed so as to get an idea about the best way to move forward. AI deals more with predictive analysis so that the customer receives a clear idea about the benefits and negatives associated with a particular action.
So which way would be suitable for the current business scenario? The answer is that both, in a balance – AI and BI are now converging, and organisations are increasingly becoming insight-driven from data-driven.

How Data Can Help Build a Better World

The constant between both BI and AI is that both require a large amount of data to work well. Companies, therefore, have the opportunity to make a huge difference in the world with their philanthropic ambitions, if the available data is used well. Today, large amounts of innovations are coming out every day which deal with the methods using which AI can be used to make the world a better place with every passing day.
If you find yourself interested in AI and wanting to change the world, check out the business analytics training courses available on Imarticus Learning!