Undergraduate

CSE Data Science Department

Welcome to our Data Science specialization within the Department of Computer Science and Engineering. As data becomes the cornerstone of modern decision-making, our program is dedicated to equipping students with the skills and knowledge required to harness the power of data for impactful insights and innovations.

About Us

Established in the academic year 2024, our Department of CSE Data Science offers a 4-year undergraduate program with an annual intake of 60 students. The faculty members of the department have global experience and training. The departmental research is focused in the areas of Artificial Intelligence, Parallel Processing, Software Engineering, Image Processing, and Computer Vision, Medical Image Processing, Pattern Recognition, Data mining and Web mining, Biometrics, Natural Language Processing (NLP), Data Science, and Information Extraction. The department has all the facilities to carry out the related teaching and research work. The Department enthusiastically supports and initiatives students to create intellectual communities that extend beyond the classroom, and we encourage students at all levels to take advantage of the programs offered at the global level.

Scopes of Data Science

Data science programs cover a wide range of topics and skills to prepare students for the various challenges and roles they may encounter in the field. Here are some of the key scopes of a typical data science program:

1. Core Mathematical and Statistical Foundations

1. Probability and Statistics: Understanding the fundamentals of probability theory, statistical inference, hypothesis testing, and descriptive statistics.
2. Linear Algebra: Essential for understanding data transformations, dimensionality reduction, and machine learning algorithms.
3. Calculus: Important for optimization problems and understanding the mechanics of many machine learning models.

2. Programming Skills

1. Languages: Proficiency in programming languages commonly used in data science such as Python, R, SQL, and sometimes Julia or Java.
2. Software Development: Principles of software engineering, including version control (e.g., Git), testing, and debugging.

3. Data Management and Manipulation

1. Databases: Knowledge of relational databases (SQL) and NoSQL databases, data warehousing, and data lakes.
2. Data Wrangling: Techniques for cleaning, transforming, and organizing raw data for analysis.
3. Sentiment Analysis: Analyzing customer reviews and social media to gauge public opinion about products or brands.

4. Exploratory Data Analysis (EDA)

1. Data Visualization: Tools and techniques for visualizing data (e.g., Matplotlib, Seaborn, Tableau).
2. EDA Techniques: Identifying patterns, outliers, and insights from data through summary statistics and visual exploration.

5. Machine Learning and Predictive Analytics

1. Supervised Learning: Algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
2. Unsupervised Learning: Clustering techniques (e.g., k-means, hierarchical clustering) and dimensionality reduction methods (e.g., PCA).
3. Model Evaluation: Metrics for evaluating model performance (e.g., accuracy, precision, recall, F1 score, ROC-AUC).

6. Deep Learning

1. Neural Networks: Understanding the basics of neural networks, backpropagation, and training techniques.
2. Advanced Architectures: Knowledge of convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequential data, and other deep learning frameworks.

7. Big Data Technologies

1. Frameworks: Proficiency in big data frameworks and tools like Hadoop, Spark, and Kafka.
2. Distributed Computing: Concepts of parallel processing, distributed storage, and computation.

8. Domain-Specific Applications

1. Business Intelligence: Using data science for business analytics, decision-making, and strategy.
2. Natural Language Processing (NLP): Techniques for processing and analyzing textual data, including sentiment analysis and topic modeling.
3. Computer Vision: Applications in image and video analysis.

Career Opportunities in Data Science:

1. 1. Data Scientist
2. 2. Data Analyst
3. 3. Machine Learning Engineer
4. 4. Data Engineer
5. 5. Business Intelligence (BI) Analyst
6. 6. Data Architect
7. 7. AI Specialist
8. 8. Data Consultant
9. 9. Research Scientist
10. 10. Marketing Analyst
11. 11. Operations Analyst
12. 12. Product Manager (Data Products)
13. 13. Healthcare Data Scientist
14. 14. Cybersecurity Data Scientist

VISION

To be among the best programs in Data Science, generating Software & IT Professionals, along with various skills like proper attitude, Techincal skills, Knowledge, ethics and to become a renowned center for the genesis of creative ideas and solutions.

MISSION

1. To provide distinctive and relevant education within scientific and professional environment.
2. To develop curricula that are holistic, flexible and dynamic in design so as to nurture heightened cognitive abilities leading to creativity and innovation.
3. To dispense quality knowledge and practical skills, to achieve excellence in education and create technologically competent manpower for the global institutions.
4. To give impetus to creative minds to transform society through innovation, design and to build entrepreneurship, leadership and to facilitate concerted action.

PROGRAM OUTCOMES (POs)

Engineering Graduates will be able to:

1. 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals and an engineering specialization to the solution engineering problems.
2. 2. Problem analysis: Identify , formulate,review research literature and analyze complex engineering problem reaching substantiated conclusion using first principles of mathematics , natural science and engineering sciences.
3. 3. Design/Development of solutions:Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety and the cultural,society and environmental considerations.
4. 4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data and synthesis of the information to provide valid conclusions.
5. 5. Modern tool usage: Create, select and apply appropriate techniques, resources and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
6. 6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
7. 7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal environmental contexts and demonstrate the knowledge of and need for sustainable development.
8. 8. Ethics: Apply ethical principles and commit to professional ethics and responsibility and norms of the engineering practice.
9. 9. Individual and team work: Function effectively as an individual and as a member or leaded in diverse teams and in multidisciplinary setting.
10. 10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large such as being able to comprehend and write effective reports and design documentation make effective presentation and give and receive clear instructions.
11. 11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one's own work as amember and leader in a team to manage projects and multidisciplinary environments.
12. 12. Life- long learning: Recognize the need for and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

PROGRAMME SPECIFIC OUTCOMES (PSOs)

After completion of this programme students will be able to attain.

1. PSO 1: Demonstrate basic knowledge of computer applications and apply standardized practices to software project development.
2. PSO 2: Apply the fundamental knowledge of computer science, analyze the real time problems and provide the computer based solutions.
3. PSO 3: Design effective systems and models for real time applications using the data structure, programming languages, Data Science, artificial intelligence models and networks.

PROGRAMME EDUCATIONAL OBJECTIVES (PEOs)

1. PEO 1: To be able to understand and analyze computer science and engineering problems and relate them to real life.
2. PEO 2: To impart in-depth knowledge of computer science and engineering to meet industrial needs, undertake and excel in graduate studies and innovation in related areas of engineering.
3. PEO 3: Promote collaborative learning and teamwork through multidisciplinary projects and a diverse professional ethics with an inclination for higher studies and research.
4. PEO 4: To inculcate a conviction to believe in self, impart professional and ethical attitude and nurture to be an effective team member, infuse leadership qualities, and build proficiency in soft skills and the abilities to relate engineering with the social, political and technical issues.

Engineering at RITEE USP's

1. First & Only Engineering College in Chhattisgarh accredited with Grade A+ by NAAC
2. Experienced faculties
3. Exceptional Laboratory Setup
4. Huge collection of books in Library
5. 7 Specialization in M.Tech
6. 5 Research Centres for Ph.D
7. Only Engineering college in India to have a commercial Bio Diesel plant within the campus
8. Only Institute in Central India to have a BARC Centre
9. First Engineering college of state to adopt Solar Power
10. Many research work in progress
11. Entrepreneur Development Cell – The story of Suraj Kumar
12. Training Program
13. Industrial Relation
14. Amazing Placement record with highest package of 12 L per annum
15. Campus placement of companies like Samsung, LG Soft, Shapoorji Pallonji, Byju’s
16. About 45 companies visit every year