The course has been designed to help candidates jumpstart their careers in the field of Data Science and Machine Learning. Great Learning alumni have secured roles such as Data Scientists, Machine Learning Engineers, Data Analysts, Analytics Consultants, etc. in leading Analytics Companies.
150+ Participating Companies
15.6 L Highest CTC
Exclusive campus hiring drives
Attend dedicated placement drives with our over 150+ hiring partners.
Resume Building Sessions
Build your resume to highlight your data science skill-set along with your previous academic and professional experience.
Workshop to help you prepare for technical interviews conducted by industry experts.
Career Guidance and Mentorship
Get an expert career mentor personalised to your academic background and experience.
What is the program structure for M.Tech in Data Science
and Machine Learning?
The program is a full-time / weekend classroom program and spans 21-months
including a capstone project and an industry internship.
Explore - Program Structure
What is the Capstone Project all about and how exactly
would it help me?
The capstone project is an opportunity for candidates to work on industry like data science and
machine learning problems under the mentorship of faculty/industry mentors. This project would
bring in all the learnings that candidates would have gained in the course and would work on
problems relevant in the industry, using techniques and tools mastered in the course. The
capstone project also becomes a way for candidates to showcase their analytics credentials
and learning to their future employers. Explore - M.Tech in Data Science
and Machine Learning Program Curriculum
What certificate will I receive?
Successful participants will be awarded with an M.Tech Degree in Data Science and Machine
Learning from PES University. PES University has been ranked as the #1 University in
Karnataka (New Universities Under 5 Years). It has also been placed 7th on the list
of India’s Most Trusted Educational Institutes. (*KSURF).
Click here to view certificate.
The candidates need to bring their own Laptops, the technology requirement
shall be shared at the time of admission.
Will there be placements at the completion of the course?
Yes, candidates receive placement assistance upon successful completion of M.Tech in Data
Science and Machine Learning. We would also prepare candidates for the interviews by
providing extensive support in terms of mentoring, CV review, interview preparation,
etc. Explore Here - Great Learning Placement Assistance
Fee related queries
What is the course fee? Is there any financial aid provided?
Please refer to the fee details here. Admissions office will help
you in applying for loans once you receive an offer of admission. We ensure money is not
a constraint in the path of learning.
What is the refund policy for the course?
We advise all candidates to have complete information before enrolling
in the course. No refund requests will be accepted once the fee has been paid.
Download - Brochure
What is the eligibility criterion for M.Tech in Data
Science and Machine Learning?
Candidates should have a B.Tech, B.E or an M.C.A degree, and should have a
minimum of 60% in X, XII and Bachelor's degree.
What is the admission process?
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 an interview with the Program
Director/Faculty. Selected candidates will receive an admission offer letter.
The 21th century has witnessed how the internet could change their world and how artificial intelligence and machine learning are impacting each of our lives.
The driving force for all these technologies to create an impact is Data. The world is now revolving around the world Data. Every activity on the internet generates data, using which, technology has been evolving rapidly. The data that is already existing and the data that is being generated on a daily basis on the internet is being used to derive meaningful insights and solve many problems. This application is nothing but Data Science. Data science is widely used to process data to derive solutions and also to predict effective outcomes.
Data Science and Machine Learning are two different fields that have gained massive demand quickly. We get to hear these terms on a daily basis due to the astounding impact they have been creating. Data is the future. Hence the volume of professionals seeking a career in these domains is increasing in an expeditious way. Aspiring graduates and professionals across the world desire to pursue a Masters in Machine Learning and Data Science. To empower these professionals to upskill themselves in these technologies of the future, Great Learning has designed an industry-relevant program that offers a Masters in Data Science and Machine learning in collaboration with one of the country's best institutes - PES University.
Since Data Science and Machine Learning are interrelated, rather than taking up a masters in Machine Learning online or masters in Data Science, it would be preferable to opt for an M.Tech in Data Science and Machine Learning.
Let us look at the various aspects that make this program a great choice for those looking to upskill in Data Science and Machine Learning.
The Comprehensive Curriculum
The M Tech in Data Science and Machine Learning by Great Learning is a comprehensive program designed by accomplished data science academicians and professionals to give learners all the critical knowledge and skills that the industry demands.
The curriculum of this program consists of 4 modules spread over 21 months, and covers integral topics of Data Science and Machine learning.
The first module has 3 parts:
Statistical Foundations for Data Science
Since statistics is the most crucial aspect of Data Science and Machine Learning, the first module of this program concentrates on laying a strong foundation for the candidates that empowers them to clearly understand the various concepts of Data Science and Machine Learning.
The list of these concepts is as follows
Hypothesis Testing - ANOVA
Measures of Dispersion
Causality and ‘Fit’
Regression lines and error terms
Python for Data Science
Python is one of the most popular programming languages which is necessary to master Data Science. Amongst other programming languages such as R, C++, Java, etc, Python has immense demand due to the flexibility it offers to perform several mathematical, statistical and other operations that help data scientists perform their tasks efficiently.
Below are the various core modules of Python which will be taught to the Data Science master degree program by Great Learning
The basics of Python, data structures & data handling
Databases - SQL & NoSQL
Since Data Science and Machine Learning are all about data, you will need to learn to work with huge data sets. So, the M.Tech in Data Science and Machine Learning teaches each candidate to gain a complete understanding of the different types and applications of databases.
The list of these modules includes
Comparison with NoSQL data stores
Common NoSQL tools like Cassandra & MongoDB
Once you get familiar with the core concepts to master Data Science, you will be learning several Data Science and data analytics techniques and skills. The second module of the Data Science curriculum of Great Learning teaches various data visualization and machine learning tools and techniques.
Data visualization is a process of accessing raw data and transforming into graphs, charts, videos, pictures, etc that make it easy to understand.
Exploratory Data Analysis (EDA)
Tableau for Visualization
Python packages for visualization
Machine Learning – 1
The first module of machine learning teaches about supervised learning.
kNN, Naive Bayes
Support Vector Machines
Machine Learning – 2
The second module of machine learning teaches unsupervised learning.
Ensemble Techniques in Machine Learning - Decision Trees, Random Forests, Bagging, Boosting
Features of a Cluster - Labels, Centroids, Inertia
Eigenvectors and Eigenvalues
Principal component analysis
After understanding the concepts of machine learning which is an important subset of artificial intelligence, you will enter the third module that teaches you about deep learning which is a subset of machine learning.
Intro to Deep Learning and its applications
Deep Neural Networks
CNNs and their application to Computer Vision
RNNs/LSTMs and their application to Natural Language Processing
After studying deep learning, you will get to master the techniques of Big data which play an essential role in helping data scientists manage and analyse large volumes of data.
Intro to Big Data Analytics
Intro to Hadoop
Spark & ecosystem of tools
Batch processing (Hive, HBase, ingestion)
Real-time processing (Kafka, Spark Streaming)
Deploying ML code using ML pipelines on the cloud
After gaining complete knowledge of all the above described topics, the candidates will be assigned capstone projects that help them to apply all the skills and knowledge mastered in the classroom. This enables them to gain hands-on experience and build the confidence to pursue their dreams of taking up a career in Data Science.
Great learning offers this program in weekend classroom as well as full-time formats. The online Data Science masters program is designed for working professionals with classes being conducted during the weekends.
If you are ambitious about pursuing a career in the field of data science and Machine learning, you will need to possess practical knowledge of various aspects of Data Science and Machine Learning.
The fields of Data Science and Machine Learning offers a broad range of career opportunities with a wide range of job roles. Let us know a few of the job roles offered to a candidate with a Masters in Data Science.
Data analytics consultant
Machine Learning Engineer
The Roles and responsibilities of a Data Scientist
What are the major roles and responsibilities involved in the job role of data scientists? What do the data scientists do on a daily basis?
Many don't have a brief idea of what data scientists do. Many also believe that data scientists perform jobs like Data visualization, data processing, data munging, data mining, etc. But let us get into reality and understand what a data scientist does on a daily basis and how his work impacts the organisation.
Data scientists are the backbone of any organisation today as they perform many key roles that impacts the growth and development of the organisation.
Data Science plays a major role in understanding business requirements and solving business problems by accessing the given set of data.
Data collection is the major responsibility of a data scientist. This is the process of retrieving historical data that is needed to perform necessary operations.
The second stage involves the cleaning of data. This is another important responsibility carried by data scientists which involve the assessment of the collected data and removing unwanted data. This task reduces complexity and makes it easy to derive the right solution.
After cleaning the data, data scientists need to perform data exploration and data analysis. This is an integral action performed by a data scientist. Data exploration is more like a brainstorming session to data analysis as this involves the application of several techniques to the given set of data in order to derive meaningful insights. Understanding the patterns of data helps you derive the most accurate results to solve specific business problems.
Data modeling is the next phase where a data scientist performs the application of several machine learning algorithms to the given data after deriving the necessary insights and detecting the patterns of the data. The data modeling phase gives the most accurate predictions and the best solutions to define any given problem.
The next phase is Data validation. In this phase, the selected model is tested to discover if there exists any peculiarities or inconsistencies. This phase is crucial as this helps to identify errors, false predictions and undesirable insights retrieved in the above stages.
After performing all the above mentioned operations, the data scientist is now aware of the efficiency of the selected model and he gets ready to deploy the results acquired.
After the deployment, the data scientists receive feedback and make necessary corrections considering the comment received.
By establishing a career in this domain, you will pursue one of the most valued careers of this era. Build the right skills and gain practical expertise with this program and take your career forward.