M.Tech in Computer Science and Engineering

The program is delivered over 4 semesters, including an M.Tech thesis. The degree will be awarded at the end of 2 years.

Successful participants will be awarded with an M.Tech Degree in Computer Science and Engineering from SRM Institute of Science and Technology. Candidates will also receive an additional specialization certificate in Data Science and Machine Learning. SRMIST is Ranked in the Top 50 Universities in India by *MHRD - NIRF, and has been accredited with the Highest 'A++' Grade by *NAAC. SRMIST is globally rated as ‘Four Star’ University by renowned ranking agency QS and given ‘Diamond’ Rating by QS-IGAUGE in Indian context.

M.Tech in Computer Science and Engineering is a holistic and rigorous course and follows a continuous evaluation scheme. Candidates are evaluated in the courses they undergo through examinations, case studies, quizzes, assignments, and project reports.

The classes will be held at the SRM Institute of Science and Technology Main Campus in Kattankulathur, Chennai, and online learning platform. 

The candidates need to bring their own Laptops, the technology requirement shall be shared at the time of admission.

All program participants get access to Dedicated Career Services,  that helps you unlock your career potential by highlighting your skills and connecting you to the right opportunities for your next job through the following activities:
Access to curated jobs
Access a list of jobs relevant to your experience and domain. Leverage our dedicated career support team working with 3400+ organizations, who’ll recommend the right jobs for you.
Interview preparation workshops
Familiarise yourself with commonly asked questions that’ll help you crack any technical interview. Use your ePortfolio to showcase your skills and improve your chances of getting hired.
Personalised career mentorship
Get an expert career mentor personalised to your experience and industry, who will help you navigate your path to career success. Get guidance on choosing the right opportunities, building a great CV and much more.

Yes, this program is Accredited and Approved by the All India Council for Technical Education (AICTE)

A basic degree or equivalent in the following with a minimum aggregate of 50% - B.E / B.Tech. (or) M.Sc. (or) MCA.

A basic degree or equivalent in the following with a minimum aggregate of 50% - B.E/B.Tech. (or) M.Sc. (or) MCA.

All interested candidates are required to apply for the course through the online application form. The admissions committee will review and shortlist candidates. Shortlisted candidates are required to clear an admissions test followed by a program selection interview. Selected candidates will receive an admission offer letter.

If you are interested, you can apply through the Online Application Form. Please reach out to us at srm-mtech@greatlearning.in or 080 4718 9112 for any course or admission related queries.

Please refer to the fee details here. Admissions office will help you in applying for loans once you receive an offer of admission.

Refund policy governed by applicable AICTE refund guidelines

The 21st century is all about the internet. It is the digital age. The technology of Computer Science has created an astounding revolution and has created an irreversible impact on human lives. We are witnessing the technological revolution every single day of our lives. The Internet is ruling the world. From Agriculture to Aeronautics, every industry has been turned upside down by Computer Science and Engineering. There is a lot of demand for M.Tech in Computer Science and Engineering.

However, technology is ever updating and people who work in the industry of Computer Science and Engineering must also update themselves with the latest tools and technologies. Hence, pursuing an M.Tech in Computer Science and Technology in the most in-demand domains like Data Science and Engineering would be a great career choice to make.

 

 Data Science these days is recognized as one of the most heard terms.  Data Science indeed is a buzz word and it has gained huge demand in recent times. Many are aspiring to learn this exciting technology and the demand for the Masters in Data Science domain is rising high. Data Science and Machine Learning are the two domains that have got immense demand in recent times. Data Science is a broader umbrella that encompasses many technologies including Machine Learning. However, being efficient with several Machine Learning tools besides learning Data Science would stand as a great combination. Data Science and Machine Learning would go hand in hand. Hence, that helps you fetch with a wide range of enthusiastic opportunities.

 

Before we understand what Data Science is, let's understand it's origin.

In the past businesses and other institutions which dealt with data were able to store most of the data in excel sheets. Simple business intelligence tools were capable of analyzing and processing this data. The major reason behind this was the availability of a lesser amount of data. As time passed by, the amount of data available to be analysed kept increasing rapidly. We live in the era of the internet, everything we do on the internet is generating data. However, most of the data being available will be either semi structured or unstructured. In order to process data of this magnitude, we do need extremely sophisticated and advanced tools and technologies. This is why Data Science has come into the picture. The technology of Data Science deep dives at a granular level of data to mine and understand the complex behaviors and trends of Data.

The technology of Data Science can bring out hidden insights that can help entitles to make smarter business decisions. Netflix employs data science to mine data related to viewing patterns of its consumers and understand what drives user interest, based on the findings, it produces original series. P&G utilizes time series models to more clearly understand the future demand which helps to plan for production levels more optimally.

 

Machine Learning is an important subset of the broader term Artificial Intelligence which is an in demand technology.

 

The idea behind Machine Learning is that you teach machines by feeding them data and letting them learn on their own without any human interventions. The process of learning begins with observations or data such as examples, direct experience, or instruction in order to look for patterns in data and make better decisions in the future considering the examples that we provide. The primary goal here is to allow computers to learn automatically from the data without human intervention or assistance and adjust actions accordingly.

 

Data science conveys a wide spectrum of domains and machine learning is one among them. Apart from Machine Learning, technologies of Artificial Intelligence and Deep Learning also fall under the domain of Data Science. Deep learning is a subset of Machine Learning. Hence, Artificial Intelligence, Machine Learning and Deep Learning are all used in Data Science for the analysis of data and extraction of meaningful insights out of it.

 

Before we understand how Machine Learning is used in Data Science, it is essential to understand the Data Lifecycle and understand the stage at which Machine Learning is employed. Are Data Science and Machine Learning the same? What is the difference between Data Science and Machine Learning?

Let's say you are aspiring to design a recommendation system for your e commerce website, this system recommends products to the customers on the basis of their shopping patterns. In order to build such a recommendation system, you may use the data related to the customer's browsing history, previous purchases, reviews, ratings, profile details, card details, and more. During the development process, you will have to go through the different stages of Data Science.

 

1. Business Requirements

The first phase of Data Science is understanding the problem you are trying to solve. Hence, you will have to list the business requirements accordingly. Gathering the required data from different resources is a crucial part of the whole Data Science process. 

 

2. Data Acquisition

In the Data Acquisition stage, you will identify the various resources from which the data will be acquired to solve the given business problem. If you are trying to build a recommendation system, gathering user ratings, comments, chat history, and more would be a few resources of data gathering.

 

3. Data Processing

Once you gather all the data that is required for solving the given problem, you enter the Data processing and cleaning phase of the data science process. In this phase, the raw data that is collected will be transformed into the desired format so that it facilitates you to perform the desired operations on it. 

 

4. Data Exploration

Once you are one with the Data Processing phase, you will enter the Data Exploration stage where a data analyst employs visual exploration to understand what is in a data set and the characteristics of the data. These characteristics can include size or amount of data, completeness, the correctness of the data, possible relationships amongst data elements, and more.

 

5. Data Modelling

The fifth stage of Data Science is Data Modelling where you incorporate Machine Learning in Data Science. This is where you need Machine Learning. Let us understand how Machine Learning is implemented in the Data Modelling stage. The data gathered in the earlier stages is imported in the process. This data should be in a proper structure. A table or CSV formats are a few of the preferred formats. After this, the data is further cleaned in order to get rid of any inconsistencies. Then the data model is built where the data is split into two sets, one for training and the other for testing. This model is built by the training dataset. This is where the data analyst employs several Machine Learning algorithms. A training dataset is used to train the model. Once the model is trained it is then evaluated by using the data set. In this phase, the model is fed with new data points and it must predict the outcome by running on the new data points on the Machine Learning model that was built earlier. After the model is evaluated using the resting data, its accuracy is calculated. The accuracy is then improved by various different methods. Hence, this is how Machine learning is employed in the lifecycle of Data Science.

 

6. Data Optimization

After the completion of the Machine Learning stage, the final model is deployed onto a production environment for final user acceptance.

 

This is how Data Science and Machine Learning go hand in hand. 

 

A career in data science has become one of the most desirable jobs of the 21st century. In Post Millennium no corner of our lives left untouched by the application of technology, data science has been creating an insane impact. The demand for data science and data analytics is on the rise and it is expected to grow exponentially in the future. Data science has demonstrated its potential by invading many organizations regardless of the dynamics surrounding an organization. It has already become an integral part of many organizations. But, there is also a lack of data science employees in and around the country. Many organisations are adopting data science, the requirement is growing rapidly which resulted in a lack of data science professionals. Data Science has become one of the most desirable technologies for many organisations across India. Many fresh graduates and existing working professionals that have perceived the importance of this technology and the revolution it has been creating in every domain it has been embraced by are now seeking a career in this field. As many job roles are being replaced by robots, it is important to look for a career that is promising.

Many top-notch companies in India are in need of data science professionals. Statistics state that there are around 1 lakh + existing vacancies for several job roles in Data Science in India. This demonstrates the demand and need of Data Science in every industry. Hence choosing a career in the domain of Data Science would be the best career choice you could make.

If you are someone who is desiring to take up a data science course, you must need to know about the various job roles that it offers. Once you are done with the data science training, you will be offered these job roles.

1. Data Scientist.

2. Data Administrator.

3. Data Analyst.

4. Business Analyst.

5. Analytics Manager.

6. Data Architect.

7. Business Intelligence Manager.

8. Statistician

9. Data Manager

 

The average pay scale of a data science professional in India is estimated as 6.5 lakh rupees for annum, which is a decent pay. A fresher who finished the data scientist course could expect a minimum of 3 to 5 lakh packages in the country.

An experienced data science professional makes around 10lakhs a year. Many multinational companies offer way beyond the estimated packages for data science professionals working in different job roles.

 

Concerning the increasing demand and the exciting impact of this amazing field, it is the right time to the best data science course.

If you are keen on learning data science, let us know what are the prerequisites to take up the Data Science and Machine Learning certification program?

 

Mathematical skills: Being good at mathematical concepts such as linear algebra, matrices, calculus, gradients, etc is one of the prerequisites for taking up a data science course online.  As many concepts of data science revolve around the concepts of mathematics and statistics, Mathematics is one of the major foundations to learn Data Science.

 

Programming Skills: Knowledge of different programming languages helps you master Data Science and Machine Learning. Possessing a minimum knowledge of programming would aid you to gain complete knowledge and understanding throughout the data science training. The list of these programming languages includes Python, C, C++, SQL, Java, and many more. Knowing these programming languages would serve you in modeling the unstructured datasets which are the primary role of any data science professional. The more understanding you have of these languages, the more it makes the data science course simple.

 

Data Visualization Skills: Data Visualization skills are considered one major prerequisite of learning Data Science. Being a data scientist, it is important to converse with people in the organization to implement the strategies he/she makes by studying the data. Possessing knowledge of the data visualization tools such as Matplottlib, tableau and many more would help you comprehend the complex outcomes and let the people understand the metrics. These tools let people easily grasp the insights you are trying to deliver.

 

If you are keen on taking up an M.Tech (Computer Science and Engineering) with additional specialization in Data Science and Machine Learning, understand the various applications of Data Science in different domains

 

Image and speech recognition

Image recognition is one of the major applications of data science which is applied in social media sites, security purposes, etc. When you upload a picture on social media sites such as Facebook, you will get an automated request for a tag as the social media site recognizes the image of the person. Speech recognition is also an application of data science that has been applied by products like Google Voice, Alexa, Siri, etc.  This accelerates extreme flexibility and ease in accessing your phone, smart TV, smart window treatments, etc making our lives easy. 

 

Fraud Detection

One of the major applications of Data Science is fraudulent detection. Data Science employs data to determine the probability of fraudulent transactions. Data Science gathers data about each transaction such as the location, amount, purchase type, cardholder details, and more. Data Science is being applied in the domain of banking and other financial organisations to eradicate fraudulent activities.

 

Recommendation Systems

Even as you shop on e-commerce websites, data science helps these sites to recommend the utmost related products by studying and analyzing the data of the specific customer.  This application also results in enhancing the user experience and promoting customer satisfaction. Many E commerce giants such as Amazon, Flipkart, and more employ Data Science to enhance the user experience and gain astounding benefits. 

 

Gaming

Data science is widely applied in the field of gaming to enhance the gaming experience. The application of the tools and techniques of data science has taken the gaming experience to another level by training the systems to act based on the user's past moves. These actions of the systems make the game even more enthusiastic.