Learn from #1 University in Karnataka

M.Tech in Data Science and Machine Learning

  • Immersive Full-Time
  • Weekend Classroom
  • Bangalore
  • 21 months
Apply Now
Application Deadline - 2nd Apr 2020
A Program by:
In collaboration with:

Why learn from PES University?

Great Learning rank-1
#1 University in Karnataka

(New Universities Under 5 Years)

*KSURF
Great Learning certificate
Among India’s Most Trusted

PES University placed seventh on the list of India’s Most Trusted Educational Institutes

*KSURF
Great Learning rank-4
Top 25 M.Tech Colleges

PES University in the list of Top 25 M.Tech Colleges in India*

*Careerindia
Great Learning talent pool
#6 Ranked University

Outlook - ICARE India Private University Rankings 2019

*Outlook

Why Great Learning?

Great Learning Salary Hike

17

Top Ranked Programs

Great Learning rating icon

20000+

Learners

Great Learning job opportunity

1500+

Industry Experts

Great Learning course completion

250+

Industry Hiring Partners

Why Join our M.Tech in Data Science and Machine Learning?

This Program enables participants to gain an in-depth understanding of data science and analytics techniques and tools that are widely used by companies. It takes a very practical approach to teaching data science and analytics, and enables participants to apply their learning.

Great Learning rank-1
Ranked #1 in analytics education

Our program has been ranked #1 in India for 4 years in a row, and has an average rating of 4.8/5 across parameters like Course Content, Pedagogy, Faculty, and Brand Value.

Great Learning certificate
M.Tech Degree from PES University

Participants in this program (upon successful completion of all requirements) will earn a M.Tech Degree from PES University.

Great Learning rank-4
World class faculty

You gain from the decades of experience and expertise brought to the table by our faculty in data science domain. Several of our faculty have been ranked among India’s top Data Science academicians.

Great Learning talent pool
Intensive format

This Program enables participants to gain an in-depth understanding of data science and analytics techniques and tools that are widely used by companies.The classes will be held at PES University Electronics City Campus, and Great Learning online platform.

Great Learning rank-8
Hands-on learning

The course covers the tools and skills sought by leading companies in Data Science. Through the duration of the course, candidates are trained on Python, SQL, Tableau, Data Science and Machine Learning. Participants in the course build their knowledge through classroom lectures by expert faculty and doing multiple challenging projects across various topics and applications in Data Science.

Great Learning Placement Assistance
Placement assistance

Through our corporate network, several leading companies participate in the hiring drives organised for Great Learning students. Some of the companies that have recently participated in the hiring process include: Uber, Swiggy, Fractal Analytics, Oyo, KPMG, Mu Sigma, Mercedes Benz, Cognizant, Mahindra, Big Basket. more

Learn From #1 University in Karnataka

6.9L Average CTC | 15.6L Highest CTC | 150+ Hiring Companies

PES University Degree

Participants in this program (upon successful completion of all requirements) will earn a M.Tech Degree from PES University.

Great Learning - Data Science Engineering certificate

Curriculum

Data Science and Machine learning concepts will be covered in three modules in the first year, followed by an intensive capstone project and an industry internship in the second.

Module 1:
Statistical Foundations for Data Science
  • Statistics (Descriptive, Inferential)
  • Probability
  • Hypothesis Testing, ANOVA
  • Measures od Dispersion
  • Causality and ‘Fit’
  • Regression lines and error terms
Python for Data Science
  • Python basics, data structures & data handling
  • Functions
  • Numpy
  • Scipy
  • Scikit-learn
  • Pandas
Databases - SQL & NoSQL
  • Database concepts
  • Data Models
  • SQL
  • Comparison with NoSQL data stores
  • Common NoSQL tools like Cassandra & MongoDB
Module 2:
Data Visualization
  • Visualization principles
  • Exploratory Data Analysis (EDA)
  • Tableau for Visualization
  • Python packages for visualization
  • Presenting insights
Machine Learning – 1
  • Supervised Learning - Classification (Logistic Regression, kNN, Naïve Bayes, Support Vector Machines)
  • Feature Selection
Machine Learning – 2
  • Unsupervised Learning - Clustering (k-means, hierarchical, etc.), PCA
  • Ensemble Techniques in Machine Learning - Decision Trees, Random Forests, Bagging, Boosting
  • Features of a Cluster - Labels, Centroids, Inertia
  • Eigen vectors and Eigen values
  • Principal component analysis
Module 3:
Intro to Deep Learning and its applications
  • Neural networks
  • Deep Neural Networks
  • CNNs and their application to Computer Vision
  • RNNs/LSTMs and their application to Natural Language Processing
Intro to Big Data Analytics
  • Intro to Hadoop
  • Spark & ecosystem of tools
  • HDFS
  • MapReduce
  • Batch processing (Hive, HBase, ingestion)
  • Real-time processing (Kafka, Spark Streaming)
  • Deploying ML code using ML pipelines on the cloud
Year 2:
Industry Internship (Full-Time) / Capstone Project (Weekend-Classroom)
M.Tech Thesis

Learn from the Best

Learn from leading academicians and experienced industry practitioners in the field of data science.

* Indicative list of faculty

Placement Assistance

150+

Participating Companies

6.9 L

Average CTC

15.6 L

Highest CTC

Participants from the course can secure roles such as Data Scientists, Machine Learning Engineers, Data Analysts, Analytics Consultants, etc. Here are some of the companies that have recently participated in Great Learning’s hiring drives:

Great Learning M.Tech in Data Science and Machine Learning placements in leading companies

Admission Details

Step 1

Fill application form

Step 2

Application review

Admissions committee will review and shortlist.

Step 3

Admission Test

Write and clear the admission test.

Step 4

Interview

Interview with Program Director/Faculty.

Learn as per your convenience

Immersive Full-Time Mode

  • 21-Month Course
  • Classroom sessions from Mon-Thu
  • 6 Month Internship
  • M.Tech Thesis
  • Dedicated Placement Assistance

Weekend Classroom Mode

  • 21-Month Course
  • Classroom sessions on Sat-Sun
  • Capstone Project
  • M.Tech Thesis
  • Career Support - GL Excelerate

Financial Aid

Our tie-ups with several lending partners like HDFC Credila, Aditya Birla and Eduvanz ensure that money is not a constraint in the path of learning.

Great Learning education loan financial partners by HDFC

Scholarship options available for candidates based on merit. Financial assistance is also provided.

Upcoming Application Deadline

Only 60 seats available per batch

2nd Apr 2020

We follow a rolling admission process and admissions are closed once the requisite number of participants enroll for the upcoming batch. So, we encourage you to apply early and secure your seat.

Apply Now

Frequently Asked Questions

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 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. Apply Now
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.
How will I be evaluated during the course?
M.Tech in Data Science and Machine Learning 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. Explore Now - M.Tech in Data Science and Machine Learning Program
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.
Where will the classes be held?
The classes will be held at PES University Electronics City Campus, and Great Learning online platform. Click Here To Know More Details
Do I need to bring my own Laptop?
The candidates need to bring their own Laptops, the technology requirement shall be shared at the time of admission. Apply Now
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.
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
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
How can I apply for the course?
If you are interested, you can apply through the Online Application Form. Please reach out to us at mtech_datascience@pes.edu or +91 74285 94608 for any course or admission related queries.

M.Tech in Data Science and Machine Learning

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.

Module 1

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

  1. Descriptive Statistics
  2. Inferential Statistics
  3. Probability
  4. Hypothesis Testing - ANOVA
  5. Measures of Dispersion
  6. Causality and ‘Fit’
  7. 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

  1. The basics of Python, data structures & data handling
  2. Functions
  3. Numpy
  4. Scipy
  5. Scikit-learn
  6. Pandas

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

  1. Database concepts
  2. Data Models
  3. SQL
  4. Comparison with NoSQL data stores
  5. Common NoSQL tools like Cassandra & MongoDB

Module 2

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

Data visualization is a process of accessing raw data and transforming into graphs, charts, videos, pictures, etc that make it easy to understand.

  1. Visualization principles
  2. Exploratory Data Analysis (EDA)
  3. Tableau for Visualization
  4. Python packages for visualization
  5. Presenting insights

Machine Learning – 1

The first module of machine learning teaches about supervised learning.

  1. Classification
  2. Logistic Regression
  3. kNN, Naive Bayes
  4. Support Vector Machines

Machine Learning – 2

The second module of machine learning teaches unsupervised learning.

  1. Unsupervised Learning -Clustering (k-means, hierarchical, etc.), PCA
  2. Ensemble Techniques in Machine Learning - Decision Trees, Random Forests, Bagging, Boosting
  3. Features of a Cluster - Labels, Centroids, Inertia
  4. Eigenvectors and Eigenvalues
  5. Principal component analysis

Module 3

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.

Deep learning

  1. Intro to Deep Learning and its applications
  2. Neural networks
  3. Deep Neural Networks
  4. CNNs and their application to Computer Vision
  5. RNNs/LSTMs and their application to Natural Language Processing

Big Data

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.

  1. Intro to Big Data Analytics
  2. Intro to Hadoop
  3. Spark & ecosystem of tools
  4. HDFS
  5. MapReduce
  6. Batch processing (Hive, HBase, ingestion)
  7. Real-time processing (Kafka, Spark Streaming)
  8. 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.

  1. Data Analyst
  2. Operations Analyst
  3. Data Engineer
  4. Database administrator
  5. Quantitative Analyst
  6. Data Scientists
  7. Data Architect
  8. Data analytics consultant
  9. Machine Learning Engineer
  10. Statistician
  11. Business Analyst

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.

  1. Data Science plays a major role in understanding business requirements and solving business problems by accessing the given set of data.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.