B.Sc Data Science and Analytics

OVERVIEW OF THE PROGRAMME

The Programme in Data Science and Analytics is meticulously designed to empower participants with the expertise and skills required to analyze and interpret complex data. It combines rigorous academic theory with practical, real-world applications, covering a comprehensive curriculum that includes statistics, machine learning, data mining, and big data technologies. Through hands-on projects and case studies, students gain invaluable experience, preparing them for a successful career in the burgeoning field of data science. The programme aims to produce graduates who are not only proficient in the technical aspects of data analysis but also possess the ability to make data-driven decisions and communicate their findings effectively to stakeholders. This multidisciplinary approach ensures that graduates are well-equipped to meet the demands of an increasingly data-driven world.

ABOUT THE PROGRAMME:

Data Science and Analytics is delivered in both lecture-based and hands-on lab learning environments where students can develop and apply their skills to complex, real-world datasets and data science and analytics problems.Data Science with Analytics” is a program that typically combines the fields of data science and analytics to equip individuals with the skills and knowledge needed to extract insights from data. Here’s a breakdown of what this program might entail:Data science involves using various techniques, algorithms, and systems to extract insights and knowledge from structured and unstructured data. This often includes skills in programming languages like Python or R, statistical analysis, machine learning, data visualization, and data manipulation. Analytics involves the process of analyzing data to uncover patterns, trends, and insights that can be used to make data-driven decisions. This may involve techniques such as descriptive analytics (summarizing data), diagnostic analytics (identifying causes of events), predictive analytics (forecasting future trends), and prescriptive analytics (suggesting actions based on data analysis).Skills Development: program in data science with analytics would likely focus on developing practical skills in areas such as data collection, data cleaning, exploratory data analysis, statistical modeling, machine learning algorithms, data visualization, and interpretation of results.Tools and Technologies: in such a program would likely gain proficiency in tools and technologies commonly used in data science and analytics, such as Python libraries, R programming language, SQL databases, data visualization tools, , and machine learning frameworks.InReal-world Applications: The program may include case studies, projects, and real-world applications to provide students with hands-on experience in applying data science and analytics techniques to solve practical problems in various domains such as finance, healthcare, marketing, and others. Ethics and Privacy Given the sensitive nature of data and the potential impact of data-driven decisions, an emphasis on ethics, privacy, and responsible data handling practices may also be included in the curriculum.

PEO, PO and PSO

Program Educational Objectives (PEO):

The B.Sc. Computer Science with Data Analytics program describe accomplishments that graduates are expected to attain within five to seven years after graduation.

  • PEO1: Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics
  • PEO2: Apply principles of Data Science to the analysis of business problem
  • PEO3: Demonstrate knowledge of statistical data analysis techniques utilized in business decision making

Program Outcomes (PO):

On successful completion oft he B.Sc. Computer Science with Data Analytics

  • PO1: Exhibit good domain knowledge and completes the assigned responsibilities Effectively and efficiently in par with the expected quality standards.
  • PO2: Apply analytical and critical thinking to identify, formulate, analyze, and solve complex problems inorder to reach authenticated conclusions
  • PO3:  Design and develop research based solutions for complex problems with specified needs through a ppropriate consideration for the public health, safety, cultural, societal,
    And environmental concerns.
  • PO4:  Establish the ability to Listen, read, proficiently communicate and articulate Complexide as with respect to the needs and abilities of diverse audiences
  • PO5:  Deliver innovative ideas to instigate new business ventures and possess the qualities of a good entrepreneur
  • PO6: Acquire the qualities of a good leader and engage in efficient decision making.
  • PO7: Graduates will be able to undertake any responsibility as an individual / member of multidisciplinary teams and have an understanding of team leadership
  • PO8: Functionas socially responsible individual with ethical values and accountable to ethically validate any actions or decisions before proceeding and actively contribute to the societal concerns.
  • PO9:  Identify and address own educational need sinachanging world in ways sufficient to maintain the competence and to allow them to contribute to the advancement of knowledge
  • PO10:  Demonstrate knowledge and understanding of management principles and apply these to one own work tomanage projects and in multi disciplinary environment

Program Specific Outcomes (PSO):

After the successful completion of B.Sc. Computer Science with Data Analytics program the students are expected to

  • PSO1: Impart education with domain knowledge effectively and efficiently in par with the expected quality standards for Data analyst professional
  • PSO2: Ability to apply the mathematical, technical and critical thinking skills in the discipline of Data analytics to find solutions for complex problems.
  • PSO3: Ability to engage in life-long learning and adopt fast changing technology to prepare for professional development.
  • PSO4: Expose the students to key technologies in data science and business analytics:data mining, machine learning, visualization techniques, predictive modeling, and statistics.
  • PSO5:  Inculcate effective communication skills combined with professional & ethical attitude.

Career Opportunities

Data Scientist: Data scientists are at the heart of extracting actionable insights from complex datasets. They use a combination of programming, statistical skills, and machine learning to analyze data and predict trends.

Data Analyst: Data analysts focus on processing and performing statistical analysis on existing datasets. Their work often involves creating visualizations, dashboards, and reports to help businesses make informed decisions.

Machine Learning Engineer: These professionals specialize in creating algorithms and predictive models to make predictions or automate decision-making based on data. They work closely with data scientists to implement and optimize machine learning projects.

Data Engineer: Data engineers build and maintain the architecture (like databases and large-scale processing systems) that allows for the efficient analysis and processing of large data sets. They ensure that data flows smoothly from source to database to analytics.

Business Intelligence Analyst: BI Analysts use data analytics and visualization tools to develop insights into the business performance and market trends. They help in strategic planning by providing data-based recommendations to the management.

Quantitative Analyst (Quant): In the finance sector, quants use data analytics to model and predict financial markets, helping companies in risk management, investment management, and trading strategies.

Data Analytics Consultant: These consultants work across industries, advising businesses on how to use data analytics to improve processes, increase efficiency, and boost profits. They often work on a project basis and may serve multiple clients.

Big Data Engineer/Architect: Big Data Engineers or Architects handle the management and organization of big data environments. Their work involves designing, building, and maintaining scalable and secure big data ecosystems.

AI Specialist: Specialists in artificial intelligence develop AI models and applications, often working closely with machine learning engineers and data scientists to integrate AI capabilities into various products and services.

S.No. Sem Part Sub Type Sub Code Subject Credit Hours INT EXT Total
1 1 1 L1   Language – I 3 5 50 50 100
2 1 2 L2   English – I 3 5 50 50 100
3 1 3 Core    Core  Course – I Theory Programming in C 4 5 50 50 100
4 1 3 Core     Core  Course – II Practical Programming in C 4 4 50 50 100
5 1 3 Allied   Allied-I 
Mathematics for Computer Science 		4 5 50 50 100
6 1 4 SEC   Skill Enhancement Courses – I Database Management System / Practical – Database Management system Lab 4 4 50 50 100
7 1 4 AEC   Ability Enhancement Course I Environmental Studies or Universal Human Values & Professional Ethics 2 2 50 0 50
            24 30 350 300 650
                     
1 2 1 L1   Language – II 3 5 50 50 100
2 2 2 L2   English – II 3 5 50 50 100
3 2 3 Core    Core  Course – III Theory Python Programming 4 5 50 50 100
4 2 3 Core     Core  Course – IV  Practical  Python Programming Lab 4 4 50 50 100
5 2 3 Elective   Elective  – I Entreprenuership Development 4 4 50 50 100
6 2 3 Allied   Allied-II Discreate Mathematics 4 5 50 50 100
7 2 4 AEC   Ability Enhancement Course II Design Thinking 2 2 50 0 50
8 2 5 Ext   Extension Activity – I (NASA) 1 0 25 0 25
            25 30 375 300 675
                     
1 3 1 L1   Language – III 3 4 50 50 100
2 3 2 L2   English – III 3 4 50 50 100
3 3 3 Core    Core  Course – V Theory  Programming Concept Using Java  4 6 50 50 100
4 3 3 Core     Core  Course – VI  Practical Programming Concept Using Java  Lab 4 4 50 50 100
5 3 3 Allied   Allied-III Quantitative Aptitude 4 5 50 50 100
6 3 4 SEC   Skill Enhancement Courses – II Practical / Training Fundamentals of Data Science 4 5 50 50 100
7 3 4 AEC   Ability Enhancement Course III Soft Skill-1 2 2 50 0 50
8 3 3 ITR   Internship / Industrial Training (Summer vacation at the end of II semester activity) 2 0 50 0 50
9 3 5 Ext   Extension Activity – II (NASA) 1 0 25 0 25
            27 30 425 300 725
                     
1 4 1 L1   Language – IV 3 4 50 50 100
2 4 2 L2   English – IV 3 4 50 50 100
3 4 3 Core    Core  Course – VII Theory  Programming in R Language 4 6 50 50 100
4 4 3 Core     Core  Course – VIII Practical   Programming in R Lab 4 4 50 50 100
5 4 3 Allied   Allied-IV  Maths for data Science 4 5 50 50 100
8 4 3 Elective    Elective  – II  – Data Mining 4 5 50 50 100
7 4 4 AEC   Ability Enhancement Course IV Soft Skill-2 2 2 50 0 50
8 4 5 Ext   Extension Activity – III (NASA) 1 0 25 0 25
            25 30 375 300 675
                     
1 5 3 Core    Core  Course – IX Theory Big Data Technology 4 6 50 50 100
2 5 3 Core     Core  Course – X Practical  Big Data Technology Lab 4 6 50 50 100
3 5 3 Elective    Elective  – III-Natural Language Processing 4 6 50 50 100
  5 3 PRJ   Project 0 6 0 0 0
4 5 4 SEC   Skill Enhancement Courses – III Practical / Training   Machine Learning Foundations 4 6 50 50 100
5 5 3 ITR   Internship / Industrial Training (Summer vacation at the end of IV semester activity) 2 0 50 0 50
6 5 5 Ext   Extension Activity – IV (NASA) 1 0 25 0 25
            19 30 275 200 475
                     
1 6 3 Core    Core  Course – XI Theory  Cloud Computing 4 6 50 50 100
2 6 3 Core     Core  Course – XII Practical   Cloud Computing Lab 4 4 50 50 100
3 6 3 Elective    Elective – IV   Image Analytics 4 6 50 50 100
4 6 3 PRJ   Core Project 8 8 100 100 200
5 6 4 SEC   Skill Enhancement Courses – IV Practical / Training Algorithms in Data Science 4 6 50 50 100
            24 30 300 300 600
          Total credit 144 180 2100 1700 3800
Additional Credits
S.No. Sem Part Sub Type Course Code Course Name Credit Hours INT EXT Total
1 2 6 VAC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
3 4 6 IDC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
4 5 6 VAC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
                     
Certificate on Minor Discipline
S.No. Sem Part Sub Type Course Code Course Name Credit Hours INT EXT Total
1 2 6 MD   Course – I 5 2 0 100 100
2 3 6 MD   Course – II 5 2 0 100 100
3 4 6 MD   Course – III 5 2 0 100 100
4 5 6 MD   Course – IV 5 2 0 100 100