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.

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.

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.


RATHINAM COLLEGE OF ARTS AND SCIENCE (AUTONOMOUS)

Scheme of curriculum for B.Sc. Artificial Intelligence and Machine Learning

for the students admitted in the Batch during 2024 – 2025

Board of Studies – Computer Science (UG)

 

 

 

Course Code

Titleof theCourse

Credits

Hours

Maximum marks

 

 

 

Theory

Practical

CIA

ESE

Total

FIRST SEMESTER

 

Language–I

4

6

 

25

75

100

 

English–I

4

6

 

25

75

100

 

Core1:ProgramminginC

4

4

 

25

75

100

 

CoreLab1:ProgrammingLab– C

4

 

3

40

60

100

 

Core2:Datastructures

4

4

 

25

75

100

 

Allied1:IntroductiontoLinear

algebra

4

5

 

25

75

100

 

EnvironmentalStudies#

2

2

 

 

50

50

 

Total

26

27

3

165

485

650

SECONDSEMESTER

 

Language–II

4

6

 

25

75

100

 

English– II

4

6

 

25

75

100

 

Core3:ProgramminginC++

4

5

 

25

75

100

 

CoreLab2:ProgrammingLab –

C++

4

 

4

40

60

100

 

CoreLab3:InternetBasics Lab

2

 

2

20

30

50

 

Allied2:Discrete Mathematics

4

5

 

25

75

100

 

ValueEducation–Human Rights

#

2

2

 

 

50

50

 

Total

24

24

6

160

440

600

THIRD SEMESTER

 

Core4: JAVA Programming

4

6

 

25

75

100

 

Core Lab4: JAVAProgramming

Lab

4

 

5

40

60

100

 

Core5:Database Management

Systems

4

6

 

25

75

100

 

Allied3:DataCommunication and Networks

4

6

 

25

75

100

 

SkillbasedSubject1:Data

Visualization

3

5

 

20

55

75

 

Tamil @/ Advanced Tamil (OR)Non-majorelective-1(Yoga

forHumanExcellence)#/ Women‟s Rights#

 

2

 

2

 

 

 

50

 

50

 

Total

21

25

5

135

390

525

FOURTHSEMESTER

 

Core6:PythonProgramming

4

6

 

25

75

100

 

Core7:DataWarehousingand

DataMining

4

6

 

25

75

100

 

CoreLab5:Python

ProgrammingLab

4

 

6

40

60

100

 

Allied4:DeepLearning

4

6

 

25

75

100

 

SkillBasedSubject 2:Capstone

ProjectWorkPhase I

3

 

4

30

45

75

 

Tamil @/ Advanced Tamil (OR)Non-majorelective–II

(GeneralAwareness)#

2

2

 

 

50

50

 

Total

21

20

10

145

380

525

FIFTHSEMESTER

 

Core8:RProgramming

4

6

 

25

75

100

 

CoreLab6:RProgrammingLab

4

 

6

40

60

100

 

Core9:BigDataAnalytics

4

6

 

25

75

100

 

Elective-I

BusinessDataAnalytics/Social Network Analysis/t/Artificial Neural Network and Fuzzy Systems

 

4

 

6

 

 

25

 

75

 

100

 

SkillBasedSubject3:Capstone

ProjectWorkPhase II

3

 

6

30

45

75

 

Total

19

18

12

145

330

475

SIXTH SEMESTER

 

Core10:Linuxand Shell

Programming

4

6

 

25

75

100

 

CoreLab7:LinuxandShell

ProgrammingLab

4

 

5

40

60

100

 

Core11:ProjectWorkLab

8

 

5

200

200

 

Elective-II

WebApplicationSecurity/

SoftwareAgents/Embedded systems

 

4

 

5

 

 

25

 

75

 

100

 

Elective-III

ClientServerComputing/Open source Software/Principles of Secure Coding

 

4

 

5

 

 

25

 

75

 

100

 

SkillbasedSubject4:Machine

Learning

3

4

 

30

45

75

 

ExtensionActivities

2

 

 

50

50

 

Total

29

20

10

195

530

725

 

GrandTotal

140

138

43

935

2565

3500

 

 

 

 

 

 

 

 

 

ONLINE COURSES

 

 

Ability Enhancement Course
S.No Course Code Course Pre-requesite Offering Department Mandatory
1 Environmental Studies General Yes
2 Women Studies Commerce II Yes
3 Constitution of India Commerce I Yes
4 Human Rights General Yes
5 Yoga Tamil Yes
6 NCC Viscom Yes
7 Communicative English English Yes
8 Quantitative Apptitude Mathematics Yes