M.Tech in Data Science and Machine Learning

Master Data Science from Karnataka’s #1 University

  • Bangalore
  • 2 Years
  • Weekend Classroom

Enquire: 080 4718 9254

Eligibility: 2+ years of work experience

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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 on Python, SQL, Tableau, and other Data Science tools & techniques
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Immersive Classroom Learning Experience
  • Offered in Weekend learning format
  • Classroom sessions at PES University Campus, Bangalore
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Dedicated Career Assistance
  • Exclusive career fairs for our learners
  • 400+ hiring partners, 8000+ career transitions, Avg. hike of 48%
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Comprehensive curriculum designed by industry experts
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M.Tech Degree from PES University
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Bangalore | 2 Years | Weekend Classroom

M.Tech Degree from PES University

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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 various M tech courses in Data Science and Machine Learning.

To empower these professionals to upskill themselves in these technologies of the future, Great Learning has designed an industry-relevant program that offers M tech in Data Science and Machine learning in collaboration with one of the country's best institutes - PES University for fresh graduates as well as existing working professionals. PES is recognised as one of the highly reputed universities of the country. PES University is ranked sixth in the Outlook-ICARE India University Rankings 2019 and is the only University in Karnataka to rank in the top 20. PES is one among the top universities that offer M tech in Data Science in india. If you are seeking to pursue an Mtech in Data Science in Bangalore, this is the best choice you could make.

 M tech in Data Science for working professionals in India

Great Learning has designed this program considering the existing working professionals. This is the best M tech program in data science for working professionals in India. The classes will be organised during the weekends and the part time  program of M.tech in data science syllabus covers all the concepts of Data Science and equips the candidates with industry desired skills. If you are desiring to pursue a part time Mtech program in data science, this program is for you.

Now let us learn a bit about these two technologies

What is Data Science?

Data science is a multi -disciplinary field that employs various scientific techniques, methods, algorithms and systems to derive knowledge and insights from structural and unstructured data.

Payscale of Data Scientists

The domain of Data Science is perceived as one of the leading career professions in India for the enormous demand it has attained in recent times. The job roles in the domain of Data science are observed to offer one of the highest salaries. Therefore many engineering graduates are desiring to pursue their M. tech in data science. 

The salary scale in this domain ranges from 4 lakh per annum to 25 lakh per annum considering various factors. In India, the average pay scale of a data scientist is observed to be 7 lakh per annum. Therefore, M tech in Data Science in India has got unbelievable demand.

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence, the most exciting technology.  Machine Learning generates the ability to automatically learn and act without being explicitly instructed in computer systems. In simple words, Machine Learning can be explained as the tools and techniques employed for teaching the systems to learn from data and act as humans do.

Payscale of Machine Learning Engineers

The various job roles in the domain of Machine Learning have now evolved as the most in-demand career professions of the 21st century. Many engineering graduates are aspiring to pursue a career in this domain and are striving to pursue M.Tech in machine learning.

The job roles in the domain of Machine Learning are observed to be one of the highest paid jobs in the country. The pay scale of the Machine Learning professionals in the industry varies from 5 lakh to 30 lakh in India, considering various factors. Nevertheless, the average pay scale of a Machine Learning professional in India is estimated at 7.5 lakh per year.

What are the prerequisites needed to pursue an M tech in Data Science and Machine Learning?

Below are the various prerequisites to gain a strong understanding of the various concepts of Data Science and Machine Learning

1. Mathematics

Mathematics and Statistics are considered as one of the major prerequisites to master these two technologies.Being good at mathematical and statistical concepts such as probability distributions, hypothesis testing, statistical significance, and regression would be a great aid to understand the various concepts of Data Science and Machine Learning. Concepts of probability such as conditional probability, priors, and posteriors, and maximum likelihood makes it easy to understand the concepts of Machine Learning.

2. Programming Languages

Knowledge of different programming languages helps you master Data Science and Machine Learning. Java, C++, R, Python, etc are a few languages that are widely applied in Data Science and Machine Learning. Python is recognized as the highly preferred programming language by many Data Science and Machine Learning professionals for the flexibility it offers.

3. Database Concepts

In order to work with huge chunks of data, it is essential to know the data management skills. Being familiar with diverse database concepts and tools such as big data, SQL, SAS, etc would offer to gain a comprehensive understanding of the theories of Data Science and Machine Learning.

4. Data Visualization Tools

Having a brief knowledge of data visualization tools such as Tableau, Datawrapper, FusionCharts, etc would help effective presentation.

Since Data Science and Machine Learning are interrelated, rather than taking up a M tech in Machine Learning online or M tech 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 in the M tech in Data Science 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 M tech in Data Science 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 M tech 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.

What are the various roles and responsibilities of a Machine Learning professional?

1. Developing several Machine Learning production ready algorithms.

2. Takepart in developing Machine Learning solutions by working with data scientists.

3. Implementation of the developed machine learning algorithms.

4. Coordinating with the various technical teams to successfully implement the machine learning project.

5.  Machine Learning engineers study and transform various data science prototypes.

6. Machine Learning professionals are also responsible for designing machine learning systems, machine learning algorithms and machine learning applications.

7. They are also responsible for selecting the appropriate datasets by analysing them.

8. The Machine learning engineers are liable for performing various statistical analysis and training the systems accordingly.

Applications of Machine Learning and Data Science

Machine Learning and Data Science are the two game changers of the modern day technological industries. These two domains have become necessities to almost every industry for the astounding benefits they have been offering. Most of the top most companies have embraced these technologies and they have become an integral part of their organisations.

1. Social Media

Social Media websites like Facebook, Instagram, Twitter use Data Science and Machine Learning.

Example: The tools of Data Science and Machine Learning are employed in the automatic friend tagging suggestion on Facebook. When you post a picture on Facebook, it suggests you tag your friends in it. The face detection and recognition algorithms help Facebook to perform this activity.

2. Google Maps

Google map uses Data Science and Machine Learning in Google maps. Google has got vast traffic data acquired over the time from the users using the application.The machine learning algorithms help the google maps to provide the user with the fastest route to reach his destination.

The people using the app generate the data to help the app predict the most accurate results considering the traffic flow and  the estimated time to reach the destination.

3. E commerce

Data Science and Machine Learning aids e commerce websites to suggest the relevant ads to the customers based on their previous purchases and search history.  This makes the customers encounter a personalised shopping experience. This way Machine learning and Data Science benefits the e-commerce industry by targeting the customer with suitable recommendations and providing an optimised customer experience.

4. Search Engine Optimization

The search engine even applies the techniques of data science to give the most optimised results to the search query given by the user. Both Machine Learning and Data science are playing a vital role in enhancing the user experience while accessing the Search Engine.

5. Financial Sector

Most of the leading financial companies and banks employ the machine learning and data science practices to offer more secured processes. Machine Learning techniques are serving the financial sector in detecting the fraudulent activities and minimising the risks.

Choose Great Learning

Even as there are a wide range of institutes that claim to offer various courses in Data Science and Machine Learning, it is necessary to choose the best institute to pursue the course.

Great Learning is known for its exceptional quality of education and has established credibility over the years. Therefore, choose Great Learning if you are seeking to pursue from the best institutes in the country.

Let us look into a few attributes that make Great Learning stand as the best choice to pursue an M.Tech in Data Science and Machine Learning.

Placement Assistance

Great Learning has been successful in serving it's students with the best career support. Great Learning has built an exceptional corporate network with many top notch companies across the country. These companies take part in the regularly conducted recruitment drives organised by Great Learning. The list of these companies include The MathCompany, Uber, KPMG, Bigbasket, Cognizant, Swiggy, Tata Capital, United Health Group, American Express, Mercedes Benz  and many more.

Faculty

The faculty of Great Learning are highly accomplished and are also recognised as one of the best in the industry. Students will also gain valuable practical insights from various industry experts to share their knowledge and enhance their understanding.

Hands on Learning

This program is followed by several lab sessions that employ rigorous hands on learning. All the students will gain enhanced practical knowledge as they will be thoroughly trained on Python, SQL, Tableau, Data Science, and Machine Learning throughout the course. 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.

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