Data Science is one of India's rising disciplines, and there are many opportunities to get work in various industries. The course lasts two years in total, broken down into four semesters. Students from any engineering branch are eligible to pursue the M.Tech level Data Science programme, which is one of its benefits. Students are given a curriculum of the specialized concept of data science through this course, along with in-depth theoretical and practical knowledge of data assessment and analytics. Analytics Tools, Data Assessment Techniques, Data Collection, Data Visualization, etc., are some subjects covered in this course.
One of the most innovative and exciting in-demand professional paths is data science. This multidisciplinary discipline combines the application of scientific approaches to turn the knowledge that can be extracted from data into practical commercial insights. To make understanding the technical outcomes of vast amounts of data more accessible, the discipline of data science encompasses an extensive range of technologies. To help students become skilled professionals capable of extracting business values from data by utilizing statistical and analytical approaches, the department offers a B.Tech CSE emphasizing Data Science.
With the help of this curriculum, graduates will be given superior conceptual knowledge, technical skills, and the capacity to do research. Through a comprehensive set of advanced courses and a dissertation to demonstrate their potential for independent study, students are exposed to mature fields of expertise such as data engineering, visualization, modelling, and big data analytics.
This program aims to provide graduates with advanced conceptual knowledge, technical skills and the ability to conduct research. Students mature in specific disciplines such as information technology, visualization, modelling and massive data analysis through rigorous graduate courses and a dissertation demonstrating their research potential.
50% of the authorised enrollment for each course will be admitted by the Admission Committee for Professional Courses (ACPC) on the basis of merit. Following the outcome, the student needs to register with ACPC.
50% of the authorised enrollment for each course are Management Quota seats, with the institute managing admission in accordance with ACPC criteria.
Discover the fascinating world of data with our M.Tech program in Data Science! Customized to equip you with the skills to excel in this domain, our course covers various topics, from advanced analytical methods to effective management of large datasets. With experienced faculties and hands-on projects, you'll gain practical knowledge and a deeper understanding of the dynamic field of data science.
The Master of Technology in Data Science Laboratories is a program designed to equip students with the latest tools, techniques and technologies used in the field of data science. The program is focused on hands-on learning, providing students with practical experience in data science through real-world projects and applications.
Students will be introduced to the basics of data science, including data collection, cleaning, and preparation. Topics covered include data types, data distributions, and descriptive statistics. Students will also learn about data visualization techniques and how to use them to better understand data.
Data Science Laboratories is a comprehensive program that provides students with hands-on experience in the field of data science. With a focus on practical learning and real-world projects, students will be well-equipped to succeed in data science careers and tackle real-world challenges.
The Workshops and Skill Development Programme in M.Tech Data Science are designed to enhance the students' technical and practical skills in the field of data science. These programmes are focused on providing hands-on experience and practical training to students, helping them to apply the concepts learned in the course to real-world problems.
Machine Learning: In this workshop, students will learn about machine learning, including supervised and unsupervised learning algorithms. Topics covered include decision trees, linear and logistic regression, k-nearest neighbors, and support vector machines. Students will also learn how to implement machine learning algorithms in R and Python.
Data Visualization and Storytelling: In this workshop, students will learn about data visualization and storytelling, including how to create compelling visualizations to communicate insights and data-driven stories. Topics covered include data visualization design principles, data storytelling techniques, and tools such as Tableau and PowerBI.
Big Data Analytics: In this workshop, students will learn about big data analytics, including how to process and analyze large amounts of data using Hadoop, Spark and other big data tools. Topics covered include distributed computing, data streaming, and real-time data processing.
Deep Learning: In this workshop, students will learn about deep learning, including artificial neural networks and convolutional neural networks. Topics covered include supervised and unsupervised learning, image recognition, and natural language processing. Students will also learn how to implement deep learning algorithms in TensorFlow and PyTorch.
Computer and information-systems manager: They are responsible for planning a company's overall information technology use, supervising technology use, and overseeing the creation of new systems and software to satisfy operational requirements. Responsible for network and data security.
Manager of Advertising and Promotions: The goal of advertising and promotions managers is to maximise return on investment. Keep an eye on important variables like e-commerce statistics and customer surveys.
General and Operations Manager: To decide how to use personnel and resources, establish policies, and maintain employee accountability for daily production.
Management Analyst: The role of a management analyst is to pinpoint and address issues that prevent an organisation from realising its full potential.
Big-Data Architect: Take care of merging data from several silos to quicken procedures, forecast issues, and enhance the business' comprehension of its clients. Ensuring the effective operation of data systems.
Indus University has an autonomous vertical - Training & Placement Department (T & P Dept.) - that connects two vital ends: education and the industry. It exemplifies a link between schools and university constituent associations (entry-level input) and the sector (output-end at the finishing level).
The Training and Placement Department was established in 2006. It was previously affiliated with the Indus Institute of Technology & Engineering until becoming a part of the Indus University in 2012.
The Training and Placement Department is the hub for career assistance for students from all programmes and streams at the university. It provides students with overall career solutions by encouraging them to choose and pursue their ideal vocations.
50% of the authorised enrollment for each course will be admitted by the Admission Committee for Professional Courses (ACPC) on the basis of merit. Following the outcome, the student needs to register with ACPC.
50% of the authorised enrollment for each course are Management Quota seats, with the institute managing admission in accordance with ACPC criteria.
Discover the fascinating world of data with our M.Tech program in Data Science! Customized to equip you with the skills to excel in this domain, our course covers various topics, from advanced analytical methods to effective management of large datasets. With experienced faculties and hands-on projects, you'll gain practical knowledge and a deeper understanding of the dynamic field of data science.
The Master of Technology in Data Science Laboratories is a program designed to equip students with the latest tools, techniques and technologies used in the field of data science. The program is focused on hands-on learning, providing students with practical experience in data science through real-world projects and applications.
Students will be introduced to the basics of data science, including data collection, cleaning, and preparation. Topics covered include data types, data distributions, and descriptive statistics. Students will also learn about data visualization techniques and how to use them to better understand data.
Data Science Laboratories is a comprehensive program that provides students with hands-on experience in the field of data science. With a focus on practical learning and real-world projects, students will be well-equipped to succeed in data science careers and tackle real-world challenges.
The Workshops and Skill Development Programme in M.Tech Data Science are designed to enhance the students' technical and practical skills in the field of data science. These programmes are focused on providing hands-on experience and practical training to students, helping them to apply the concepts learned in the course to real-world problems.
Machine Learning: In this workshop, students will learn about machine learning, including supervised and unsupervised learning algorithms. Topics covered include decision trees, linear and logistic regression, k-nearest neighbors, and support vector machines. Students will also learn how to implement machine learning algorithms in R and Python.
Data Visualization and Storytelling: In this workshop, students will learn about data visualization and storytelling, including how to create compelling visualizations to communicate insights and data-driven stories. Topics covered include data visualization design principles, data storytelling techniques, and tools such as Tableau and PowerBI.
Big Data Analytics: In this workshop, students will learn about big data analytics, including how to process and analyze large amounts of data using Hadoop, Spark and other big data tools. Topics covered include distributed computing, data streaming, and real-time data processing.
Deep Learning: In this workshop, students will learn about deep learning, including artificial neural networks and convolutional neural networks. Topics covered include supervised and unsupervised learning, image recognition, and natural language processing. Students will also learn how to implement deep learning algorithms in TensorFlow and PyTorch.
Computer and information-systems manager: They are responsible for planning a company's overall information technology use, supervising technology use, and overseeing the creation of new systems and software to satisfy operational requirements. Responsible for network and data security.
Manager of Advertising and Promotions: The goal of advertising and promotions managers is to maximise return on investment. Keep an eye on important variables like e-commerce statistics and customer surveys.
General and Operations Manager: To decide how to use personnel and resources, establish policies, and maintain employee accountability for daily production.
Management Analyst: The role of a management analyst is to pinpoint and address issues that prevent an organisation from realising its full potential.
Big-Data Architect: Take care of merging data from several silos to quicken procedures, forecast issues, and enhance the business' comprehension of its clients. Ensuring the effective operation of data systems.
Indus University has an autonomous vertical - Training & Placement Department (T & P Dept.) - that connects two vital ends: education and the industry. It exemplifies a link between schools and university constituent associations (entry-level input) and the sector (output-end at the finishing level).
The Training and Placement Department was established in 2006. It was previously affiliated with the Indus Institute of Technology & Engineering until becoming a part of the Indus University in 2012.
The Training and Placement Department is the hub for career assistance for students from all programmes and streams at the university. It provides students with overall career solutions by encouraging them to choose and pursue their ideal vocations.
What qualifications are needed to study data science?
It's crucial to have a solid understanding of the prerequisites before you begin learning data science in order to do so successfully. Data science requires a number of preliminaries, such as programming, statistics, mathematics, a few databases, and others.
What factors affect a data scientist's salary?
The same elements that would affect another role's income also affect a data scientist's salary. These include the candidate's background, the position's location, and the business to which the applicant has applied.
Why is data science popular now?
Today's globe generates enormous amounts of data every second. Data science is a lucrative profession that aids in making sense of this data.
Can I still pursue an M.Tech in data science if my mathematical skills are poor?
Mathematical ideas are the cornerstone of data science. Even math professionals find this field to be rather difficult. Therefore, you might want to rethink your choice.
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