Data Science Masters or Masters in data science 2 years | Course Details, Jobs Prospective & Syllabus 2023

A data science master’s degree is a type of degree that helps people learn how to work with and understand big sets of data. This can include learning statistics, computer science, and math. It can also involve learning about specific subjects like finance or healthcare. People who get this degree may also get hands-on projects and internships to practice working with data. To be accepted into this type of program, you usually need to be good at math and computer science and may need to pass some exams. After finishing this degree, you can work in different industries like finance or healthcare, and do jobs that involve working with data. Data Science Masters or Masters in data science (MDS) 2 years course offered by TU. Course Details, Jobs Prospective & Syllabus of Data Science Masters.

  • Tribhuvan University has established the School of Mathematical Sciences to offer Bachelor’s and Master’s degrees in Mathematical Sciences
  • These programs are designed to produce experts with a strong foundation in mathematics, statistical and analytical skills, and computational skills
  • The abundance of data and the use of computational simulations in the modern industry require a strong understanding of mathematical modeling, data analysis, and computational exploration
  • The Master’s Program in Data Science at the School of Mathematical Sciences is well-suited to meet these needs

 

Goals and objective to studying Data Science Masters |Duration and Nature of Course

This unique interdisciplinary program will prepare students to collect, clean, store, and query data from various sources, evaluate and respond to decision-making needs, apply appropriate analytic techniques to support decision-making, and communicate actionable information and findings effectively. Upon graduation, students will be equipped with the skills necessary to succeed in their careers.

There are several reasons why someone might choose to pursue a master’s degree in data science:

  1. Career advancement: A master’s degree in data science can open up new job opportunities or allow for advancement in a current career. Data science is a growing field with a high demand for skilled professionals, so earning a master’s degree can make you a more competitive candidate for data-related roles.
  2. Interdisciplinary skills: Data science is an interdisciplinary field that combines elements of computer science, statistics, and domain-specific knowledge. A master’s degree in data science can provide a broad range of skills that can be applied in a variety of industries.
  3. Real-world experience: Many master’s programs in data science offer hands-on projects and internships, which can provide valuable real-world experience working with data. This experience can be useful in building a portfolio and gaining practical skills.
  4. Improved salary potential: Data science professionals with advanced degrees tend to have higher salaries than those without. A master’s degree in data science can increase your earning potential and make you a more valuable employee.
  5. Personal growth: Pursuing a master’s degree in data science can be a personally rewarding experience. It can challenge you to learn new things and expand your knowledge and skills in a rapidly evolving field.

Duration and Nature of Course

Here are some key points about the Masters in Data Science program at Tribhuvan University:

  • It is a full-time program that lasts for 4 semesters over a period of 2 years.
  • The program includes compulsory foundational courses in mathematics, statistics, computer science, and information technology, as well as a selection of elective courses that may vary from year to year.
  • The total number of credits required to complete the program is 60.
  • The course includes a combination of theoretical and practical components, as well as projects, seminars, internships, and a thesis.
  • The program is designed to provide students with a strong foundation in the fundamentals of mathematics, statistics, and computer science, as well as the skills and knowledge needed to analyze and interpret large and complex data sets.

 

TU Masters in data science Eligibility

Here are some key points about the eligibility requirements for the Masters in Data Science program at Tribhuvan University:

  • Students must have a Bachelor’s degree with a strong quantitative and computational background, including coursework in calculus, linear algebra, and introductory statistics.
  • Eligible majors include B Sc CSIT, B Math Sc, B Sc. (Math), B Sc (Stat), and B Sc/BA with Math/Stat in the first 2 years, BE, BIT, and BCA (with two Math and one Stat).

 

Job Opportunities for Masters in Data Science

A master’s degree in data science can lead to a variety of job opportunities in a range of industries. Some potential job titles for graduates with a master’s in data science include:

  • Data Scientist: Data scientists analyze large and complex data sets to extract insights and inform decision-making.
  • Data Analyst: Data analysts collect, process, and analyze data to generate reports and make recommendations based on their findings.
  • Data Engineer: Data engineers design, build, maintain, and troubleshoot data pipelines and systems to support data-driven applications and processes.
  • Machine Learning Engineer: Machine learning engineers build and deploy machine learning models and systems to solve real-world problems.
  • Business Intelligence Analyst: Business intelligence analysts use data to inform strategic business decisions and optimize business performance.

Graduates with a master’s in data science may also pursue roles in industries such as finance, healthcare, technology, retail, and government. The specific job prospects and opportunities available to an individual will depend on their skills, experience, and education, as well as the job market in their location.

  • Data scientists are skilled at analyzing large and complex data sets to extract insights and inform decision-making.
  • They use their technical skills and creativity to translate data into innovative ideas that can have significant implications for commercial and social change.
  • Data scientists are responsible for collecting, organizing, and manipulating data, as well as communicating their findings to strategists and decision-makers.
  • They have a deep understanding of organizations and industries and know how to ask the right questions to uncover hidden relationships between disparate data sets.

 

Sectors using Data Science

Data science is used in a wide range of sectors and industries to analyze and interpret large and complex data sets. Some examples of sectors that use data science include:

  1. Finance: Data science is used in finance to analyze financial data, identify trends and patterns, and make predictions about market movements and investment opportunities.
  2. Healthcare: Data science is used in healthcare to analyze patient data, identify trends in health outcomes, and improve patient care.
  3. Technology: Data science is used in the technology industry to analyze user data, identify trends and patterns, and inform product development and marketing efforts.
  4. Retail: Data science is used in the retail industry to analyze customer data, identify trends and patterns in consumer behavior, and optimize sales and marketing efforts.
  5. Government: Data science is used in government to analyze data related to issues such as public health, crime, and economic policy.
  6. Manufacturing: Data science is used in manufacturing to analyze production data, identify trends and patterns, and optimize operations and supply chain management.
  7. Education: Data science is used in education to analyze student data, identify trends and patterns in student performance, and inform instructional and curricular decisions.

These are just a few examples of the many sectors and industries that use data science. Data science is a growing field with a wide range of applications across many different sectors. It is used in a wide range of sectors and industries, including government agencies, clinical research centers, the banking sector, the manufacturing industry, the travel and hospitality sector, the healthcare industry, and business houses.

Masters in Data Science Syllabus and Curricular Structure of TU

The curriculum for the Master’s in Data Science program is structured as follows:

  • During the first and second semesters, students must take four compulsory courses and one elective course (relevant to their interests) per semester.
  • In the third semester, students must take three compulsory courses and two elective courses.
  • In the fourth semester, students must take two compulsory courses and two elective courses.

FIRST SEMESTER

 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 501 Fundamentals of  Data Science 3 Th.
MDS 502 Data Structure and  Algorithms 3 Th.+ Pr.
MDS 503 Statistical Computing with R 3 Th.+ Pr.
MDS 504 Mathematics for Data Science 3 Th.

 Elective Courses (Any One)  

Course Code Course Titles Credits Nature
MDS 505 Data Base Management Systems 3 Th.+ Pr.
MDS 506 Programming skills with C 3 Th.+ Pr.
MDS 507 Linear and Integer Programming 3 Th.+ Pr.

 

SECOND  SEMESTER

Compulsory Courses

Course Code Course Titles Credits Nature
MDS 551 Programming with Python 3 Th.+ Pr.
MDS 552 Applied Machine Learning 3 Th.+ Pr.
MDS 553 Statistical Methods for Data Science 3 Th.+ Pr.
MDS 554 Multivariable Calculus for Data Science 3 Th.

                Elective Courses (Any One)

Course Code Course Titles Credits Th.+ Pr.
MDS 555 Natural Language Processing 3 Th.+ Pr.
MDS 556 Artificial Intelligence 3 Th.+ Pr.
MDS 557 Learning Structure and Time Series 3 Th.+ Pr.

 

THIRD  SEMESTER

 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 601 Research Methodology 3 Th.
MDS 602 Advanced-Data Mining 3 Th.+ Pr.
MDS 603 Techniques for Big Data 3 Th.+ Pr.

 Elective Courses (Any Two)

Course Code Course Titles Credits Nature
MDS 604 Cloud Computing 3 Th.+ Pr.
MDS 605 Regression Analysis 3 Th.+ Pr.
MDS 606 Decision Analysis
Monte Carlo Methods
3 Th.+ Pr.
MDS 607 Cloud Computing 3 Th.

 

FOURTH  SEMESTER

 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 651 Data Visualization 3 Th.
MDS 652 Capstone Project/ Thesis 3 Project+ Report

 Elective Courses (Any Two)

Course Code Course Titles Credits Nature
MDS 653 Social Network Analysis 3 Th.+ Pr.
MDS 654 Actuarial Data Analysis 3 Th.+ Pr.
MDS 655 Deep Learning 3 Th.+ Pr.
MDS 656 Business Analytics 3 Th.+ Pr.
MDS 657 Bioinformatics 3 Th.+ Pr.
MDS 658 Economic Analysis 3 Th.+ Pr.

 

Final Thoughts

A master’s degree in data science is a postgraduate academic degree that is designed to prepare students for careers in fields that involve the analysis and interpretation of large and complex data sets. These fields include data mining, machine learning, and statistical analysis, and may also include areas such as data visualization, predictive modeling, and data engineering. Studying data science is highly demanding these days. Tech Startups and AI is also demanding data scientist in high volume.

University Tribhuvan University
Course Name Master’s in data science (MDS)
Duration 2-Years (4 Semesters)
Eligibility B Sc CSIT, B Math Sc, B Sc. (Math), B Sc (Stat), and B Sc/BA with Math/Stat in the first 2 years, BE, BIT, and BCA (with two Math and one Stat).
Jobs finance, healthcare, technology, retail, and government
Expected Salary Rs. 50k to 500k Per Month
Website http://tribhuvan-university.edu.np/

 

Also, Check

Application for Masters in KU