CSE AI DS

About B. Tech in Computer Science & Engineering (Artificial Intelligence and Data Science)

B. Tech in Computer Science & Engineering (Artificial Intelligence and Data Science) is an undergraduate program that focuses on the study and application of artificial intelligence (AI) and Data Science (DS) technologies. It combines computer science, mathematics, and statistics to develop intelligent systems and algorithms.

While data science utilizes statistical and computational methods to derive valuable insights from data, artificial intelligence (AI) seeks to build computers that are capable of performing tasks that need human-like intellectual ability.

Vision

“To educate globally capable and ethically minded specialists in Computer Science & Engineering (Artificial Intelligence and Data Science), primed to contribute to both industry and broader societal needs.”

Mission

M1: To provide students with a top-tier technical education, empowering them to engage in lifelong learning and cultivate expertise in cutting-edge technologies within AI and DS.
M2: To promote research and development through opportunities collaboration with industry and governmental organizations, aim tackling intricate engineering challenges.
M3: To inspire students towards professional advancement by imbuing them with ethical principles and leadership skills while engaging with the community to address societal issues collaboratively.

Here are some key aspects of the B. Tech program in Computer Science & Engineering (Artificial Intelligence and Data Science):

Curriculum: The curriculum typically includes courses in computer programming, data structures, algorithms, statistics, mathematics, machine learning, Data Science, deep learning, natural language processing, computer vision, robotics, data mining, and AI ethics.

Core Concepts: The technique by which computers, in particular, simulate human intelligence. Applications of artificial intelligence (AI) include machine vision, speech recognition, processing of natural languages (NLP), and expert systems.

Data science is based on probability and statistics. They support our data analysis and pattern recognition. Important subjects include probability, hypothesis testing, probability distributions, and basic statistics. Use: These ideas are crucial for interpreting huge datasets and reaching reliable results.

Programming Languages and Tools: Students learn popular programming languages like Python, Java, or C++, which are widely used in AI and DS development. As it focuses on advanced analytical methods and insights, data science is typically seen of as being easier to enter. Tools like Num Py, Pandas and libraries like scikit-learn, Tensor Flow, and Py Torch are used for data manipulation, analysis, and machine learning, and Keras for building AI and DS models.

Projects and Practical Experience: B. Tech programs often include hands-on projects and practical experience to apply AI and DS concepts. Students work on real-world problems, develop algorithms, analyze data, and build AI-powered solutions. These projects help develop problem-solving skills and provide valuable practical knowledge.

Electives and Specializations: Some programs offer electives and specializations to allow students to focus on specific areas within AI and DS, such as computer vision, natural language processing, robotics, or data mining. These options help students develop expertise in their chosen fields.

Database Management and Architecture

Database design and management.
Data warehousing.
Business intelligence.
Big data systems engineering.
Advanced architecture design.
Business analysis.

There are four primary categories of data analysis in data science and analytics: descriptive, diagnostic, predictive, and prescriptive.
Consider the IBM Data Science Professional Certificate on Coursera, which covers fundamental ideas and abilities, or the Johns Hopkins University Data Science Specialization on Coursera, which provides a thorough and respected introduction to the area.
Consider taking courses in data science, artificial intelligence, cyber security, and software development for a profession that will be future-proof in the present labor market. These fields are in great demand due to technological breakthroughs.

Internships and Industry Exposure: Many B.Tech programs encourage internships in AI and ML-related industries or research organizations. These internships provide students with real-world exposure, professional networking opportunities, and a chance to apply their knowledge in practical settings.

Career Opportunities

Graduates with a B.Tech in Computer Science & Engineering (Artificial Intelligence and Data Science) have a wide range of career opportunities. They can work as AI engineers, machine learning engineers, data scientists, AI researchers, robotics engineers, software developers, or consultants in industries such as healthcare, finance, e-commerce, autonomous vehicles, and more.

It’s important to note that the specific curriculum and structure of B.Tech programs may vary among universities. It’s recommended to research individual universities and their program details to get a better understanding of the specific courses and opportunities available.

Roles such as Machine Learning Engineer, Data Scientist, AI Engineer, Robotics Engineer, Natural Language Processing Engineer, and AI Research Scientist are among the many new employment prospects brought about by the development of AI.

AI and data science together have a huge and quickly growing impact on many different businesses and offer a wide range of job prospects. These industries, which range from social media and entertainment to healthcare and banking, are changing the way people solve problems and make decision Career Paths in Artificial Intelligence and Data Science
Data science and artificial intelligence have a bright future ahead of them. Let’s examine a few of the most common job profiles in these domains:

Data Scientist Data Scientist
Principal/Chief Data Scientist, Data Scientist, Data Scientist Manager, Data Architect,
Data Analyst and Data Warehouse Engineer
Developer of Business Intelligence and Computer Vision Engineer
ML Expert
Python for Machine Learning Programming
R/Statistical Modeling Data Scientist
Artificial Intelligence
Engineer in Machine Learning
AI/Machine Learning Data Scientist
Senior Machine Learning Business Analyst
Leader in Artificial Intelligence Solutions:
Advanced Analytics & AI Leadership
NLP Developer: Artificial Intelligence/Machine Learning Data Scientist
Engineer in Artificial Intelligence and Machine Learning
AI/Machine Learning Communication Scientist
Lead AI Scientist for Deep Learning in Artificial Intelligence and Machine Learning
Analyst for Artificial Intelligence in Business

Some potential career paths and employment opportunities:

AI Engineer/Developer: AI engineers or developers work on designing, developing, and implementing AI systems and solutions. They build algorithms, develop DATA SCIENCE models, and integrate AI technologies into various applications.

Machine Learning Engineer: Machine learning engineers specialize in developing and deploying machine learning models and algorithms. They work on data preprocessing, feature engineering, model training, and optimization to create effective ML solutions.

Data Scientist: Data scientists analyze large and complex datasets to extract valuable insights and develop predictive models. They apply statistical techniques, machine learning algorithms, and data visualization to solve business problems and drive decision-making processes.

AI Researcher: AI researchers focus on advancing the field of artificial intelligence through research and innovation. They explore new algorithms, develop cutting-edge AI technologies, and contribute to scientific publications and conferences.

Robotics Engineer: Robotics engineers combine AI and ML techniques with robotics to design and develop intelligent robots and autonomous systems. They work on perception, motion planning, control systems, and human-robot interaction.

Natural Language Processing (NLP) Specialist: NLP specialists work on understanding and processing human language by computers. They develop algorithms for tasks such as sentiment analysis, language translation, speech recognition, and chatbot development.

AI Consultant: AI consultants provide expertise and guidance to businesses looking to leverage AI and DS technologies. They assess business needs, design AI strategies, and help implement AI solutions that align with the organization’s goals.

Entrepreneurship: With a strong background in AI and DS, graduates can explore entrepreneurial opportunities by starting their own AI-based ventures or joining startups in the AI space.

The demand for AI and DS professionals spans across various industries, including healthcare, finance, e-commerce, manufacturing, telecommunications, and transportation. Companies are increasingly adopting AI technologies to improve efficiency, make data-driven decisions, enhance customer experiences, and drive innovation.

It’s worth mentioning that the field of AI and DS is evolving rapidly, and continuous learning and up-skilling are crucial for professionals to stay updated with the latest advancements. Pursuing higher education, attending workshops and conferences, and engaging in self-study is valuable for career growth in this field.