M.Tech in Data Science and Machine Learning

Master Data Science from Karnataka’s #1 University

  • Bangalore
  • 21 Months
  • Full-Time / Weekend Classroom

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PES University

A Program by:

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Why Choose this Program?

Comprehensive Curriculum
  • Learn Data Science, Data Visualisation, Machine Learning, Deep Learning, Big Data, and more
  • Hands-on learning through lab sessions on Python, SQL, Tableau, and other Data Science tools & techniques
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Immersive Classroom Learning Experience
  • Offered in Full-time / Weekend learning formats
  • Classroom sessions at PES University Campus, Bangalore
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Dedicated Placement Assistance
  • Exclusive campus hiring drives with leading analytics companies
  • 150+ Participating Companies, 6.9 L Average CTC, 15.6 L Highest CTC
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Working Professional
Comprehensive curriculum designed by industry experts
Career Support
Full-time / Weekend classroom learning formats
Salary Hike
M.Tech Degree from PES University
Industry Project
Dedicated Placement Assistance

Learn from #1 University in Karnataka

Bangalore | 21 Months | Immersive Full-Time / Weekend Classroom

M.Tech Degree from PES University

#1 University in Karnataka


#7 Among India’s Most Trusted


Top 25 M.Tech College in India


#6 Ranked University in India


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.