Introduction

An MSc in Data Science is a postgraduate program designed to equip students with the theoretical knowledge and practical skills needed to analyze, interpret, and extract insights from large and complex datasets. It's a highly sought-after degree in today's data-driven world, preparing graduates for a wide range of lucrative careers. The program is designed to blend theoretical knowledge with practical skills, ensuring students excel in academic, industrial, and research fields.

USPs

• Industry-Oriented Curriculum : Aligned with current trends in the Data science sector.
• Hands-On Training : Access to state-of-the-art labs for practical exposure.
• Practice School : A 150-hour program linking classroom learning with real-world experience.
• Research Opportunities : Mentorship and seed-funding support for innovative projects.
• Career Guidance :Workshops and seminars for skill enhancement and career preparation.

Eligibility and Selection Process

Eligibility	To be eligible for an M.Sc. in Data Science at Jagannath University, Jaipur, you generally need a 60% aggregate in your undergraduate degree (Mathematics/Computer Science/Physics/Statistics or Engineering in CSE/IT/ECE/EEE/E&I). Applicants from Jammu & Kashmir, Ladakh, and the North Eastern states may need a 50% aggregate. Selection is based on the marks you secured in your undergraduate degree.
Minimum Eligibility Criteria	To be eligible for an M.Sc. in Data Science at Jagannath University, Jaipur, you generally need a 60% aggregate in your undergraduate degree (Mathematics/Computer Science/Physics/Statistics or Engineering in CSE/IT/ECE/EEE/E&I). Applicants from Jammu & Kashmir, Ladakh, and the North Eastern states may need a 50% aggregate. Selection is based on the marks you secured in your undergraduate degree.
Selection Process	Merit-based or entrance test as per university admission guidelines.
Course Duration	2 years (4 semesters).

Specialization-Specific Information

Key Highlights: Features and Benefits Specific to the Specialization

1. Comprehensive Curriculum

   • Covers essential Data Science topics such as Applied Linear Algebra, Python Programming, Hive, and Pig language.
   • Focuses on both theoretical and practical aspects to create industry-ready professionals.

2. Research and Innovation

   • Opportunities to participate in research projects, seed-money initiatives, and publications.
   • Exposure to cutting-edge developments in Data Analytics and data visualization.

3. Practice School

   • A structured 150-hour program linking academic knowledge with practical industry experiences.

4. State-of-the-Art Laboratories

   • Equipped for advanced practical training in areas like Data Analyst, data quality control, and data mining.

5. Regulatory and Industrial Training

   • Insights into Data Engineering, Data Analyst, and company standards.

6. Career Versatility

   • Data science offers immense career versatility. Graduates can excel as Data Scientists, Engineers, or Analysts across diverse industries like tech, finance, and healthcare.

7. Global Relevance

   • Curriculum aligned with international standards, enhancing global career opportunities.

Infrastructure Details

• Modern Labs : Equipped for advanced research and practical learning in data science with Hadoop.
• Library Access :Rich collection of books, journals, and digital resources.
• Practice Areas:Dedicated spaces for compounding, dispensing, and formulation.

Internship Information

Structure

Internships are mandatory for MSc Data Science programs to provide students with practical exposure and hands-on experience. These internships are well-structured to cover different areas of the data science field.

Duration

• Data Science: 1-2 months (typically conducted during the 3nd semester).
• Data Science: 6 months (typically conducted during the 4th semester).

Types of Internships Provided

1. Data Scientist Intern

   • Focus: This is the most direct application of a data science master's degree. Interns work on end-to-end data science projects, including data cleaning, exploratory data analysis (EDA), statistical modeling, and machine learning model development and evaluation.
   • Responsibilities often include: Collecting, cleaning, and preprocessing data; building predictive models (regression, classification, clustering); applying machine learning algorithms; performing statistical analysis; creating data visualizations and dashboards; collaborating with cross-functional teams; and presenting insights.
   • Skills emphasized: Python (Pandas, NumPy, Scikit-learn), R, SQL, statistical concepts, machine learning fundamentals, data visualization tools (Matplotlib, Seaborn, Tableau, Power BI).

2. Machine Learning Engineer Intern / AI Engineer Intern

   • Focus: More focused on the engineering and deployment aspects of machine learning models. Interns might work on building, optimizing, and deploying ML pipelines, integrating models into production systems, or developing AI-driven features.
   • Responsibilities often include: Developing and optimizing machine learning algorithms; working with deep learning frameworks (TensorFlow, PyTorch); model deployment and monitoring; experimenting with different model architectures; contributing to MLOps (Machine Learning Operations); researching and implementing advanced AI techniques.
   • Skills emphasized: Strong programming (Python, Java, Scala), deep learning frameworks, cloud platforms (AWS, Azure, GCP), MLOps tools, software engineering principles.

3. Data Engineer Intern

   • Focus: Concentrates on building and maintaining the infrastructure for data. This includes designing data pipelines, managing databases, and ensuring data quality and accessibility for data scientists and analysts.
   • Responsibilities often include: Building ETL/ELT pipelines; working with big data technologies (Hadoop, Spark, Kafka); designing and optimizing data warehouses and databases (SQL, NoSQL); data governance; ensuring data quality and integrity; automating data processes.
   • Skills emphasized: SQL, Python/Java/Scala, big data frameworks, cloud computing, database management systems, data warehousing concepts.

4. Business Intelligence (BI) Analyst Intern / Data Analyst Intern

   • Focus: Interpreting data to provide actionable insights for business decision-making. This role often involves extensive data visualization and reporting to stakeholders.
   • Responsibilities often include: Performing ad-hoc data analysis; creating interactive dashboards and reports (e.g., using Tableau, Power BI, Looker); extracting data using SQL; identifying trends and patterns in business data; supporting business units with data-driven recommendations.
   • Skills emphasized: SQL, Excel, data visualization tools, strong analytical skills, business acumen, communication and presentation skills.

5. Research Intern (in Data Science/AI/ML)

   • Focus: These internships are often found in academic institutions, corporate R&D labs, or specialized research organizations. They involve exploring novel algorithms, developing new methodologies, or conducting in-depth studies on specific data science challenges.
   • Responsibilities often include: Literature review; experimental design; algorithm development; statistical analysis of complex datasets; writing research papers or technical reports; contributing to open-source projects.
   • Skills emphasized: Strong theoretical understanding of statistics, machine learning, or AI; proficiency in programming for research; ability to work independently and contribute to academic publications.

6. Quantitative Analyst Intern (Quant Intern)

   • Focus: Primarily in the finance sector, these internships involve applying advanced mathematical, statistical, and computational methods to financial data for risk management, trading strategies, and financial modeling.
   • Responsibilities often include: Building quantitative models for pricing, risk assessment, or trading; analyzing financial market data; backtesting strategies; developing tools for financial analysis.
   • Skills emphasized: Strong mathematics and statistics, programming (Python, R, C++), financial market knowledge, econometrics, time series analysis.

Placement Details

Average CTC Offered to Students : MSc data science: ₹ 6- 9 LPA (varies based on skill set, specialization, and industry trends).

Top Recruiters/Companies Hiring from this Specialization

• Data Science Companies
  • Google (Alphabet)
  • Amazon
  • Microsoft
  • Meta
• Consulting & Analytics Firms:
  • Accenture
  • Deloitte
  • PwC (Price water house Coopers)
  • Ernst & Young (EY)
  • Fractal Analytics
  • Mu Sigma
  • Tiger Analytics
• Natural Language Processing (NLP)
  • Amazon (Alexa)
  • Microsoft (Cortana)
  • Apple (Siri), IBM (Watson)
  • OpenAI
  • Anthropic
  • Grammarly
  • DeepMind

Additional Information: Industry Exposure and Practical Experience Opportunities for Hands-On Learning

1. Industry Training

   • Students undergo internships and training programs in leading data science companies and research organizations to gain real-world experience.
   • Practical exposure to data analysis, data visualization, and regulatory documentation.

2. Projects and Research Opportunities

   • Opportunities to work on funded research projects such as seed-money initiatives supported by the university.
   • Collaboration with industry experts to solve real-world challenges in data analysis and data visualization.
   • Encouragement to publish research papers and present findings at national and international conferences.

3. Practice School

   • A structured 150-hour program for MSc data science students, integrating academic learning with practical experience in industry.

4. Simulation-Based Learning

   • Utilization of advanced simulation labs to practice data analysis, data cleaning, and Extraction of Data in a controlled environment.