Department of Data Science

Ambalika Institute of Management and Technology offers B.Tech in Data Sciences program affiliated with Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow. The course duration is 4 years (8 semesters) and it is designed to provide students with a solid foundation in the interdisciplinary field of data science, integrating computer science, statistics, mathematics, and domain-specific knowledge. This program prepares students for careers in data analytics, machine learning, artificial intelligence, and big data technologies.

Program Highlights:

  1. Industry-Relevant Skills: The program focuses on providing practical knowledge through real-world projects, ensuring that students are ready to tackle challenges in data science. Students are taught to apply techniques such as machine learning algorithms, statistical methods, and data visualization to solve actual business problems.
  2. Internships and Industry Exposure: AKTU emphasizes industry collaboration, and the program includes opportunities for internships with leading tech companies. These internships give students hands-on experience with large datasets, real-world tools, and cutting-edge technologies. They also allow students to network with industry professionals, which can be beneficial for future job placements.
  3. Capstone Projects: In the final year, students work on capstone projects, which involve solving industry-specific problems by applying the concepts and tools they have learned throughout the program. These projects help students build a strong portfolio to showcase their skills to potential employers.
  4. Research Opportunities: The program encourages students to engage in research, particularly in fields such as artificial intelligence, big data analytics, and machine learning. Students may also have opportunities to work alongside faculty on research projects and publish papers in international journals or present at conferences.
  5. Skill Development: Students will develop a mix of technical and soft skills, including problem-solving, analytical thinking, and communication, which are crucial for data science roles. The program fosters a collaborative learning environment where students work in teams to tackle complex problems.

To summarise, the B.Tech in Data Sciences at Ambalika Institute of Management and Technology is a comprehensive programme that prepares students to meet the growing demand for data professionals in a wide range of industries. By offering a blend of theoretical knowledge, practical skills, and industry exposure, the program ensures that graduates are well-equipped to succeed in the dynamic and competitive field of data science.

Program Educational Outcomes
PEO1: To prepare our students to find suitable employment commensurate with their qualification.
PEO2: To create good entrepreneurs who may contribute to the nation building and generate job opportunities for others.
PEO3: To develop proficiency in students for higher studies and R & D for the solution of complex problems for betterment of the society.
PEO4: To develop students as responsible citizens with high moral and ethical values who can become asset to a vibrant nation.

Program Outcomes
PO 1 Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
PO 2 Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
PO 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, societal, and environmental considerations.
PO 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
PO 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.
PO 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.
PO 7 Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
PO 8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice
PO 9 Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
PO 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 presentations, and give and receive clear instructions.
PO 11 Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
PO 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.

Program Specific Outcomes

PSO 1: Apply computational, statistical, and machine learning techniques to analyze and interpret complex data, developing innovative solutions for real-world challenges in diverse domains.

PSO 2: Design and implement data-driven systems with ethical considerations, ensuring transparency, fairness, and social responsibility in decision-making processes.

PSO 3: Engage in interdisciplinary research, lifelong learning, and innovative practices to contribute to advancements in Data Science and emerging technologies.

Department Vision

To become a leading center of excellence in Computer Science and Engineering (Data Science), empowering students with advanced knowledge, ethical values, and innovative skills to address real-world challenges and contribute meaningfully to the global data-driven society.

Department Mission

    • To nurture future-ready professionals in Data Science by integrating theoretical knowledge with industry practices through advanced infrastructure, cutting-edge technologies, and experiential learning.
    • To instill ethical values, integrity, and social responsibility in students, enabling them to make meaningful contributions to society through ethical decision-making.
    • To foster creativity, innovation, and interdisciplinary research, empowering students to transform data into actionable insights and address real-world challenges.

Department Achievements

The department regularly updates the curriculum to include the latest advancements in data science, such as AI/ML frameworks, data ethics, deep learning, and big data analytics, ensuring that the program remains cutting-edge. The department has collaborated with other departments such as computer science, business analytics, mathematics, and social sciences to create interdisciplinary programs and projects, thereby broadening the application of data science in diverse fields.

Faculty collaborate with industries, research institutes, and other universities for cutting-edge research, particularly in applications like predictive analytics, AI algorithms, data privacy, and natural language processing (NLP). The department regularly hosts guest lectures and expert talks by industry leaders and pioneers in AI, data engineering, and analytics, providing students with valuable exposure to real-world challenges and innovations.

These department achievements reflect the dynamic and forward-looking nature of a B.Tech Data Science program. Such accomplishments not only help enhance the program’s reputation but also provide students with rich opportunities for personal and professional growth. As the field of data science continues to evolve, these achievements contribute to the program’s ability to stay at the cutting edge of education and research in this exciting domain.

Career Opportunities

A B.Tech in Data Sciences opens up numerous career opportunities in India, a country rapidly embracing digital transformation and data-driven decision-making across industries. With the exponential growth of data, the demand for skilled data science professionals is at an all-time high.

The following is a list of possible careers for Data Science graduates:

Graduates can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, Data Engineer, and AI Specialist. These roles are in high demand across diverse sectors, including IT, e-commerce, finance, healthcare, telecommunications, manufacturing, and government.

Data scientists are particularly sought after for their ability to analyze large datasets, develop predictive models, and provide actionable insights. Companies in India, like Tata Consultancy Services (TCS), Infosys, Wipro, Accenture, and Cognizant, are major recruiters, along with startups in sectors such as fintech, edtech, and healthcare.

The rise of artificial intelligence and machine learning also creates opportunities for roles such as AI/ML engineers, where professionals build algorithms that automate decision-making processes.

In addition to corporate careers, data science professionals can also explore roles in research, consulting, or start their own tech ventures. The potential for career growth is immense, with opportunities for specialization in areas like deep learning, big data, and data visualization, making it a lucrative and dynamic field in India’s evolving job market.

Industries such as IT, finance, healthcare, e-commerce, telecommunications, and government agencies are increasingly relying on data professionals to drive decision-making and innovation, making this a highly promising career path.

Curriculum & Syllabus

For detailed information on the syllabus, click here

(Semester-Wise Breakdown)

Year 1: Foundation of Programming and Mathematics

  • Semester 1:
    • Mathematics-I (Calculus and Linear Algebra): Fundamental mathematical principles such as calculus, matrices, and vector algebra essential for data analysis and machine learning.
    • Introduction to Programming: Basic programming skills using C and C++ to understand programming logic and algorithm design.
    • Engineering Physics: Basic concepts of physics, essential for understanding technologies like IoT (Internet of Things) and data collection.
    • Engineering Chemistry: Fundamental chemical concepts, particularly for data analysis in chemical and bioengineering applications.
    • Communicative English: Focus on developing effective communication and presentation skills.
  • Semester 2:
    • Mathematics-II (Probability & Statistics): Introduction to probability theory, statistical distributions, and basic inferential statistics, vital for data science.
    • Object-Oriented Programming (OOP): Programming in Java or Python focusing on object-oriented concepts such as classes, inheritance, and polymorphism.
    • Basic Electrical Engineering: Basic understanding of electrical systems, which may intersect with data science in industries like manufacturing and smart devices.
    • Engineering Drawing: Teaches the basics of technical drawing, useful for visualizing data flow and network designs.

Environmental Science: An introductory course on environmental science and its applications, relevant for sustainability-focused data science applications.

Year 3: Advanced Data Science, Machine Learning, and Big Data Technologies

  • Semester 5:
    • Machine Learning I (Supervised Learning): Introduction to machine learning algorithms, including regression, classification, and clustering techniques.
    • Big Data Analytics: Techniques and tools for processing and analyzing vast datasets, including Hadoop, Spark, and MapReduce.
    • Data Visualization Techniques: Training in visualization tools like Tableau, Matplotlib, and Power BI to represent data visually and make insights more accessible.
    • Artificial Intelligence Fundamentals: Introduction to AI concepts such as search algorithms, problem-solving techniques, and knowledge representation.
    • Software Engineering: Principles of software development, including version control, debugging, and working with development frameworks.
  • Semester 6:
    • Machine Learning II (Unsupervised Learning & Deep Learning): In-depth coverage of clustering algorithms, principal component analysis (PCA), and neural networks, including deep learning with frameworks like TensorFlow and Keras.
    • Cloud Computing for Data Science: Use of cloud platforms like AWS, Google Cloud, and Microsoft Azure for scalable data storage, processing, and machine learning model deployment.
    • Natural Language Processing (NLP): Introduction to NLP techniques for processing and analyzing textual data, including text mining, sentiment analysis, and speech recognition.
    • Data Mining: Techniques for discovering patterns, associations, and anomalies in large datasets.
    • Ethics and Legal Aspects of Data Science: Understanding the ethical and legal implications of data usage, privacy, and data security.

Year 2: Core Data Science and Computer Science Concepts

  • Semester 3:
    • Data Structures and Algorithms: Study of data structures (arrays, linked lists, trees) and algorithms for sorting, searching, and data manipulation, crucial for efficient data processing.
    • Discrete Mathematics: Mathematical structures and concepts that are critical for designing algorithms and solving data-related problems.
    • Database Management Systems (DBMS): Understanding relational and non-relational databases, SQL queries, and database design concepts.
    • Digital Logic Design: Basics of digital electronics, which are foundational for understanding computing hardware that supports data science applications.
    • Communication Skills and Technical Writing: Enhancing written and verbal communication skills for effective interaction in technical settings.
  • Semester 4:
    • Design and Analysis of Algorithms: Focus on advanced algorithms, complexity analysis, and optimization techniques for working with large datasets.
    • Data Science Fundamentals: Introduction to the key concepts of data science, including data collection, cleaning, transformation, and exploratory data analysis (EDA).
    • Operating Systems: A study of operating system fundamentals that are essential for managing resources in data processing tasks.
    • Computer Networks: Basics of networking concepts and protocols that are useful for cloud-based data storage and analysis.

Linear Algebra for Data Science: Mathematical principles like vectors and matrices that are essential for machine learning and data modeling.

Year 4: Specialized Topics and Industry Application

  • Semester 7:
    • Big Data Technologies Advanced: Advanced tools and frameworks such as NoSQL databases, Cassandra, and Kafka for real-time data streaming and analysis.
    • Deep Learning & AI Applications: Specialized topics in deep learning for computer vision, speech recognition, and AI applications in industries such as healthcare and automotive.
    • Business Intelligence and Analytics: Use of data analysis tools for decision-making in business environments, focusing on predictive analytics and real-time business data.
    • Capstone Project I: Students start working on their final-year project, often in collaboration with industry partners or faculty, to apply all the concepts learned.
  • Semester 8:
    • Data Security and Privacy: Principles of data security, encryption, and techniques to protect sensitive data, especially in sectors like finance and healthcare.
    • Advanced Topics in Machine Learning and AI: Study of cutting-edge topics like reinforcement learning, generative adversarial networks (GANs), and autonomous systems.
    • Entrepreneurship and Innovation in Data Science: Exploring entrepreneurship opportunities and developing business models around data science and AI technologies.
    • Capstone Project II: Completion and presentation of the capstone project, where students work on a real-world problem using data science techniques.
    • Industry Internship: A mandatory industry internship that provides students with exposure to the professional world and hands-on experience with real data science challenges.

Industry Academia Interaction

Industry-academia interaction plays a crucial role in shaping a B.Tech Data Science program in India. This collaboration between educational institutions and the industry ensures that the program remains relevant to current trends, equips students with practical skills, and enhances their employability. Here’s why industry-academia interaction is particularly important for a B.Tech Data Science program:

  1. Alignment with Industry Needs

Industry partnerships ensure that the curriculum remains aligned with current data science industry needs, incorporating the latest skills in machine learning, big data, and analytics tools. They also provide insights into emerging trends like AI, deep learning, and automation, keeping the program dynamic and forward-thinking.

  1. Practical Exposure and Hands-On Learning

Industry collaboration provides students with real-world projects, allowing them to work on actual data and industry problems, bridging the gap between theory and practice. Internships and industry projects offer hands-on experience, helping students apply classroom knowledge to real-world data tasks, build problem-solving skills, and gain confidence.

  1. Skill Development and Employability

Industry collaboration helps academic institutions identify in-demand skills like Python, R, SQL, and machine learning frameworks, ensuring students develop these competencies. It also enhances job placements by facilitating direct placements, industry certifications, and recruitment drives, giving graduates practical experience and increasing their chances of being hired by top companies.

  1. Research and Development Opportunities

Industry partnerships enable collaborative research on cutting-edge data science topics, fostering innovation. They also provide access to high-quality data, software tools, and infrastructure, bridging the gap between theoretical learning and real-world application.

  1. Entrepreneurial Opportunities and Innovation

Industry partnerships, particularly with startups and tech companies, mentor students in entrepreneurial skills, helping them leverage data science for new products and services. This collaboration fosters innovation, inspiring students to think creatively and nurturing an entrepreneurial mindset, encouraging them to launch startups or join data-driven ventures.

  1. Faculty Development

Collaboration with industry keeps faculty updated on current practices, tools, and methodologies, allowing them to integrate industry insights into their teaching. Industry interactions also offer faculty professional development opportunities through workshops, seminars, and training, ensuring they remain equipped with the latest knowledge in data science.

  1. Feedback and Continuous Improvement

Industry partners provide valuable feedback on curriculum relevance, helping academic institutions update it to align with in-demand skills, ensuring students stay competitive and employable. Direct engagement through guest lectures, workshops, and webinars helps students gain practical skills and understand employer expectations, making them more industry-ready.

  1. Networking and Industry Connections

Industry-academia interactions help students build professional networks by connecting them with data scientists, professionals, and recruiters, enhancing career prospects through mentorship, internships, and job opportunities. These collaborations also provide students with opportunities to participate in conferences, events, webinars, and hackathons, allowing them to network and showcase their talent.

Faculty List

S. No. Session Faculty Name Department Designation
1 2024-25 MR AMIT ASTHANA DS ASSISTANT  PROFESSOR
2 2024-25 MR DEENA NATH DS ASSISTANT  PROFESSOR
3 2024-25 MRS. MEGHA PANDEYA DS ASSISTANT  PROFESSOR

We look forward to helping you acquire one of the top degrees in Data Sciences at Ambalika Institute of Management & Technology! Apply today or send us your queries!