Month: September 2020

At present Technical education is at a turning point. The knowledge industry now expects that in addition to the core topics of computer science its graduates should also develop an inherent capacity for liberal arts, an innovative mindset, ethical issues, life values and sustainability. The knowledge is not just confined to one domain; it is stretching its boundaries and has become interdisciplinary. India’s leading IT Service and Consulting Company – TCS with a few top engineering institutes of the country, is offering a four year specialized B.Tech course titled “Computer Science and Business Systems” (CSBS) to cater to the rising need of engineering talent with skills in technology. This state-of-art course aims to impart knowledge of cutting-edge technologies and business skills with hands-on exposure to make students industry-ready.

Emerging technologies like cyber security, machine learning, cloud computing, IoT and others must be an additional part of the curriculum to facilitate students be future-ready. In coming years the digital devices and information services brought to homes, businesses, institutions and individuals will experience huge growth. Also, we will observe increasingly deep, intellectual partnerships between computer science & engineering and other disciplines – from astronomy to zoology, with sociology, psychology and business acumen in between. The result will be a transformation of most disciplines including computer science and the journey has already begun.

A 2017 study by AICTE found that 60% out of the 8 lac Indian engineers, who graduate annually, remain unemployed. The same report also stated that 39% of employers were faced with the challenge of finding quality talent; representing the enormous gap that exists between skills and employability.

Need of Business Engineers

The need of the age is ‘business engineers’, a breed of engineering talent tuned to the needs of business. Apart from appreciating the technologies of the future – analytics, cognitive and machine learning topics – these graduates now need to understand concepts of business management as well, and have the ‘innovation’ mindset, to be considered as quality talent.

It was in this context that TCS Academic Interface Program (AIP) took the lead to design a B. Tech program in ‘Computer Science and Business Systems’, resulting in a program that will cover in 4 years what earlier students took 6 to 7 years across two different programs to learn.

The four-year B.E. /B. Tech course has already been launched at three reputed institutes across India (one each from East, West and Southern part of India) in the academic year 2018-19. Students have already enrolled and the academia has been trained by TCS to start the program.

It’s heartening that B.Tech in computer science and Business System (CSBS)—has been approved by AICTE and is available on in few top institutes of the country.

Through this program, TCS in partnership with leading Engineering colleges in India endeavors to help students get a core understanding of Contemporary Technology and Technology Abstraction, along with knowledge of common Business Principles & discipline.  The elements of Design Thinking, Value Science and Innovation & Entrepreneurship, included in the curriculum will be of immense value in overall student development.

With a “Default is Digital” mindset and roadmap – the workforce revolution has begun! Good luck to the aspiring workforce of the future!

About the Programme

The Computer Science with Business Applications major is designed to provide students with a broad background in science and humanities and to provide an understanding of fundamental principles of computing and modern computing technology. It prepares the student for professional work with computer systems and business environments.

TCS (India’s largest software giant) supported B.Tech Computer Science and Business Systems (CSBS) program aims to enhance the relevance of the computer Science program in India to meet the future demands of the IT industry whose site is quickly changing in the era of Business 4.0.

The curriculum is designed to ensure that students should get better exposure to emerging subjects such as Analytics, Deep learning, Machine Learning, Cloud Computing, and Internet of Things and Design thinking and also to develop an equal interest and appreciation towards humanity, value added science and other interrelated topics. The students graduating from this program will be in high demand by the industries and will be productive.

To address the growing need of engineering talent with skills in digital technology, TCS, in partnership with leading academicians across India, has designed a 4 years undergraduate programme on Computer Science titled “Computer Science and Business Systems (CSBS). This course curriculum is designed based on the suggestions given by the TCS, which benefits the students in enhancing their employability skills by making them capable of using the latest computer technology in developing a business management system. The objectives of this course are to cultivate students in Understanding of Contemporary Technology, Understanding of Technology Abstraction, Knowledge of Common Business Principles and Innovation Ability, and Strong in Ethics and Life Value.

The students are exposed to learn the recent trends such as Computational Statistics, Software Development Models, Design Thinking, Cognitive Science & Analytics, Machine Learning, Financial Management, Services Science & Service Operational Management, Marketing Research, and Marketing Management.

Career Prospects

This course will provide industry-ready and employable engineers who can drive innovation and add value to the organization they work for as well as to the nation in the era of Business 4.0. Students can get placement in the product-based companies and service-based companies. The students have career opportunities in Government and Private sectors, Bank and Health Insurance sector, Manufacturing sector, financial sector, Business Analysis, and Marketing sector.

The course focuses on enhancing the following key attributes among graduating students:

• Understanding of Contemporary Technology

• Understanding of Technology Abstraction

• Knowledge of Common Business Principles

• Business Discipline and Service Orientation

• Innovation Ability

• Strong Human Ethics and Life Values

Key Objectives:

• Learning Common Business Principles

• Exploring Business Discipline and Service Orientation

• Innovative Ability

• Strong Human Ethics and Values

• Understanding Technology Abstraction

Highlights

• The curriculum is designed by faculty members closely working with eminent professionals from TCS and Academia and is approved by AICTE

• Industrial Training & live projects supported by TCS

• Dedicated faculty members trained by TCS to deliver the program

• High standard of teaching, learning and evaluation processes monitored by TCS

• Active mentoring by Experts from TCS

The main goal of this course is to make sure that the students not only know the core topics of Computer Science but also develop an equal understanding of humanities, human values and management sciences. The students also get good industrial exposure to emerging topics such a Analytics, Machine Learning, Cloud Computing, Internet of Things etc. The degree in Computer Science and business systems like any Computer Science degree prepares you to take the jobs in the IT sector.

Possible Careers

• Software Developer

• Database Administrator

• Computer Hardware Engineer

• Computer Systems Analyst

• Computer Network Architect

• Web Developer

• Information Security Analyst

By taking up this course one embarks on a journey to prepare for a life in the real world, to build a career. This course along with the technical knowledge offers you the skills and values that you need to adapt to the demands of the outside world and lead a successful life, whether you choose to be in the corporate sector or pursue a different path in life. There’s a mounting need to align technical education with the requirements of the existing corporate environment, and the B.Tech (CSBS) is the right step in that direction.

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In songs, poems and novels the term ‘cloud’ has been used since time immemorial. Who can forget the famous lines of William Wordsworth, “I wandered lonely as a cloud…” Despite this gap between imagery and actuality, “the cloud” has succeeded in becoming the agreed-upon shorthand for modern data storage. As far as it relates to computers, the term “cloud” dates back to early network design, when engineers would map out all the various components of their networks. The term ‘Cloud computing’ has been there since the early 2000s, but the concept of computing-as-a-service is much older than we might think. It goes back to 1960s when computer bureaus were built to rent time on a mainframe for companies instead of buying one.

By the emergence of the PC in 1980s, these ‘time-sharing’ services were completely replaced, as it had become cheaper to own one’s own computer to store and access data.

But in due course of time when a large data has been collected over internet the concept of renting access to computing power has come in to play repeatedly — like utility computing, and grid computing of the late 1990s and early 2000s.

This was followed by cloud computing, which really took hold with the emergence of software as a service and large scale cloud computing providers such as Amazon Web Services, Microsoft Azure, Oracle and many others.

Cloud computing is a method of storing and accessing data and programs from any device that can access the internet. It has reduced the requirement of a computer’s hard drive.

The data in the cloud is stored on several physical and/or virtual servers that are provided by a third-party service provider.

Types of cloud services

There are three basic types of cloud services:

Software as a Service (SaaS)

It is the most commonly acknowledged cloud service software. This includes a variety of services, such as file storage and backup, web-based email and project management tools.

Examples of SaaS cloud service providers include Dropbox, G Suite, Microsoft Office 365, Slack and Citrix Content Collaboration. In these applications, users can access, share, store and secure information in “the cloud.”

Infrastructure as a Service (IaaS)

Infrastructure as a service, or IaaS is a cloud-computing offering in which a vendor provides users access to computing resources such as servers, storage and networking. It serves as the complete data center framework, eliminating the need for resource-intensive and on-site installations.

Examples of IaaS are Amazon Web Services (AWS), Microsoft Azure and Google Compute Engine. In these providers all storage servers and networking hardwares are maintained. Providers manage security, operating systems, server software and backups.

Platform as a Service (PaaS)

Platform as a service, or PaaS, serves is a web-based service where developers can create cloud apps. PaaS enables its users to access a database, operating system and programming language that can be used to develop cloud-based software.Many IaaS vendor also offer PaaS capabilities.

Cloud computing for Engineers

Cloud computing is one of the hottest technologies with a high demand for qualified professionals. Since it is a specialty area it is not an easy thing for an individual to acquire a job in this field.

Some skills required for obtaining a secured position in cloud computing are:

Programming Languages and Coding Skills

Java and C languages are two of the most common languages, but these are rarely used in creating cloud computing services The data-oriented programming languages like SQL, XML data language, and R Math Language, in addition to Python and Ruby are needed in addition to exceptional coding skills.

Linux

This is another skill required for cloud computing job. At present bout a fourth of the servers is powered by Linux based Microsoft Azure and it is definitely going to increase with the coming days.

Cloud Security

Security is an in important factor when cloud storage is concerned. It is one of the primary aspect developers are looking into.To secure the data of their organizations, professionals can use various security tools in the market.CCSP (Certified Cloud Security Professional) training provides an adequate and professional understanding of Cloud Security.

Artificial Intelligence (AI)

The technologies that can perform exemplary actions at data integration, aggregation, and analysis without human assistance like: Artificial Intelligence and Machine learning are gaining more attention in the field of Cloud-based technology

Database Management Skills

Collecting, storing, maintaining, and accessing data stored in the cloud is pivotal to cloud systems. Hence, data management becomes a most significant skill. Along with this Data-oriented languages like SQL and Hadoop are a must.

Cloud Platforms

Amazon Web Services (AWS) and Microsoft Azure are two of the leading cloud platforms including some others. So doing a certification course on these platforms can provide better opportunities.

Business and Analytical Skills

In addition to the technical skills, business and analytical skills are also important for cloud computing job which helps in managing, communicating, and negotiating business within an organization and also with cloud providers.

DevOps

It is the most commonly used framework in cloud computing.

DevOps is a process of software development. DevOps means combination of Dev (software development) and Ops (information technology operations) to lessen the application development life cycle. There are various tools such as Monit, Jenkins, or ELK that can be useful.

 “I don’t need a hard disk in my computer if I can get to the server faster… carrying around these non-connected computers is byzantine by comparison.”  Steve Jobs, Co-founder, CEO and Chairman of Apple Inc.

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“[M]achine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on earth.”― Pedro Domingos

Communication is the basic phenomena in the nature. All species communicate to each other. The languages are nothing but a medium to represent the thoughts but as technology evolves people can talk to machine also. For example Siri or Alexa, Facebook friend suggestions, Gmail spam filters, traffic congestion predictions are some common examples where one talks to a machine and all this is machine learning.

Machine Learning (ML) is emerging as one of the hottest fields today.“Machine learning is the study of computer algorithms that allows computer programs to automatically improve through experience” as defined by Computer Scientist and machine learning expert Tom M. Mitchell.

The scientific field of machine learning (ML) is a branch of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed by relying on patterns and inference instead.

How does machine learning work?

Machine learning utilizes a variety of techniques to intelligently handle large and complex amounts of information to make decisions and/or predictions. It describes computer algorithms trained with real-world data to build predictive models.  An algorithm can be thought of as a set of rules/instructions that a computer programmer specifies which a computer is able to process. Machine learning algorithms are learnt by experience, similar to as humans do. For example, after having seen multiple examples of an object, a compute-employing machine learning algorithm can become able to recognize that object in new, previously unseen scenarios.

In practice, the patterns that a computer (machine learning system) learns can be very complicated and difficult to explain. Once the machine learning model has been trained, we can give different images as input to see if it can correctly differentiate between them.

Types of Machine Learning

There are different types of machine learning for different kinds of problems. There are generally two categories: supervised and unsupervised – but sometimes combination of both is also used.

Supervised Machine Learning

In Supervised Machine Learning our training data contains known, correct answers for the thing we’re trying to predict. It’s called supervised because we can easily evaluate how good our model is while it is being trained by comparing it to known correct answers. Most machine learning algorithms fall into the supervised learning category including regression, decision trees, XGBoost, and many more.

In the field of machine learning, the thing we try to predict is the label. So, supervised machine learning deals with labeled training data.

Unsupervised Machine Learning

Sometimes, we try to find hidden patterns in the data we have. The goal in unsupervised learning is generally to cluster the data into characteristically different groups. Unsupervised machine learning is more challenging than supervised learning due to the absence of labels. The unknown attributes are called latent features. Techniques such as K-means clustering, principal component analysis, latent Dirichlet allocation, and K-nearest-neighbors can be used to uncover these latent features.

Here as we don’t know the correct answers, unsupervised algorithms use unlabeled training data.

Semi-Supervised Learning

Real-world projects aren’t always so definite.

In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. Typically, this combination will contain a very small amount of labeled data and a very large amount of unlabeled data. The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabeled data.

 Supervised learning is used to train a model that assigns labels to unlabeled data, based on the human-generated labels it receives. With some practice we can compare the labels produced by the supervised algorithm to the labels produced by humans. As they start to agree, we can use the supervised model to label our training data instead of humans in cases where the model has high confidence. Those machine-generated labels are called pseudo-labels. Since our training data now contains a mixture of known labels assigned by humans and data that was inferred by a model, these models are called semi-supervised.

Areas where ML is employed

Machine learning technology has immense advantages in the industries which are working with large amounts of data. It has been observed that the organizations working with ML are able to work more efficient and be ahead their competitors.

Financial services

The two key purposes to use machine learning technology in banks and other businesses in the financial industry are to identify important insights in data, and prevent fraud. It can also identify investment opportunities, or help investors know when to trade. Data mining can look for clients with high-risk profiles, or use cyber surveillance to prevent from any fraud.

Government

Government agencies such as public safety and utilities utilize machine learning by collecting data through sensors from various inputs to fetch desired results. Machine learning can also help detect fraud and minimize identity theft.

Health care

Machine learning is growing at a fast pace in the health care industry, with the addition of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can help the medical field to analyze data to identify trends that may lead to better diagnoses and treatment. 

Retail

When we online shopping on any website it provide us recommendations for the items we might like based on our previous purchases using machine learning. Retailers use machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, and merchandise supply planning. 

Oil and gas

Machine learning can be used to finding new energy sources, analyzing minerals in the ground, predicting refinery sensor failure and streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.

Transportation

The data analysis and modeling aspects of machine learning are of great importance to delivery companies, public transportation and other transportation organizations. Machine learning is utilized to analyze data to identify patterns and trends in the transportation industry, which helps in making routes more efficient.

Career Opportunities in ML

There are some of the best engineering colleges in Delhi NCR which offer 100% Placement .

Job opportunities in Machine Learning

1. Machine Learning Engineer – They are sophisticated programmers who develop the systems and machines that learn and apply knowledge without having any specific lead or direction.

2. Deep Learning Engineer – They are specialized in using deep learning platforms to develop tasks related to artificial intelligence.

3. Data Scientist – They extract meaning from data and analyze and interpret it. It requires methods, statistics and tools.

4. Computer Vision Engineer – They are software developers who create vision algorithms for recognizing patterns in images.

Machine learning has already and will change the course of the world in the coming decade.

Therefore, there is a huge scope of Machine Learning in India, as well as in other parts of the world, in comparison to other career fields when it comes to job opportunities. According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2022. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles.According to Forbes, the average salary of a Machine Learning Engineer in the United States is US$99,007. In India, it is ₹865,257.Thus, the future belongs to the Machine Learning, and one has a bright future if he/she becomes an ML professional.

“Machine learning will automate jobs that most people thought could only be done by people.” ~Dave Waters

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