B.Tech Syllabus (7th Sem Computer Science Engineering)
Seventh semester syllabus for Computer Science Engineering at Bihar Engineering University
7th Semester Courses
| Paper Code | Paper Title | L | T | P | Credits |
|---|---|---|---|---|---|
| 105702 | Biology for Engineers | 2 | 1 | 0 | 3 |
| 1057xx | Theory of Computation | 3 | 0 | 0 | 3 |
| 1057xx | Data Science | 3 | 0 | 0 | 3 |
| 1057xx | Cloud Computing | 3 | 0 | 0 | 3 |
| 100728 | Soft Skills and Interpersonal Communication | 3 | 0 | 0 | 3 |
Subject Details
Module 1: Introduction
Bring out the fundamental differences between science and engineering by drawing a comparison between eye and camera, Bird flying and aircraft. Mention the most exciting aspect of biology as an independent scientific discipline.
Module 2: Classification & Genetics
Hierarchy of life forms at phenomenological level. Genetics: Mendel’s laws, Concept of segregation and independent assortment. Concept of allele. Gene mapping, Gene interaction, Epistasis.
Module 3: Biomolecules
Molecules of life. In this context relevant forms of sugar, fatty acids, proteins and nucleotides. Structure-function relationships of proteins and nucleic acids.
Module 4: Enzymes & Information Transfer
Enzymology: How to monitor enzyme catalyzed reactions. How does an enzyme catalyze reactions. DNA as a genetic material. Hierarchy of DNA structure from single stranded to double helix to nucleosomes.
Module 5: Metabolism
Thermodynamics as applied to biological systems. Exothermic and endothermic versus endergonic and exergonic reactions. Concept of Keq and its relation to standard free energy. ATP as an energy currency.
Module 6: Microbiology
Concept of single celled organisms. Concept of species and strains. Identification of microorganisms. Growth kinetics. Microscopy.
Logic
First-order predicate calculus - syntax, semantics, validity and satisfiability, decision problems in logic, quantified Boolean formulas and their relation with the polynomial hierarchy.
Computability theory
Review of Turing machines, some other computing models and formalisms, their equivalence with Turing machines, undecidability, Post correspondence problem, Turing computability, primitive recursive functions, Cantor and Goedel numbering, Ackermann function, mu-recursive functions, recursiveness of Ackermann and Turing computable functions, lambda calculus, term rewriting, oracle machines and the arithmetic hierarchy.
Complexity theory
Time- and space-bounded Turing machines, reduction and complete problems, oracle machines and the polynomial hierarchy, randomized computation, parallel computation.
Introduction to Data Science
Concept of Data Science, Traits of Big data, Web Scraping, Analysis vs Reporting
Programming Tools for Data Science
Toolkits using Python: Matplotlib, NumPy, Scikit-learn, NLTK, Visualizing Data: Bar Charts, Line Charts, Scatterplots, Working with data: Reading Files, Scraping the Web, Using APIs, Cleaning and Munging, Manipulating Data, Rescaling, Dimensionality Reduction
Mathematical Foundations
Linear Algebra: Vectors, Matrices, Statistics: Describing a Single Set of Data, Correlation, Probability: Dependence and Independence, Conditional Probability, Bayes's Theorem, Random Variables, Continuous Distributions, The Normal Distribution, The Central Limit Theorem, Hypothesis and Inference: Statistical Hypothesis Testing, Confidence Intervals.
Module 1: Introduction to Cloud Computing
Cloud Computing Overview, Characteristics, Deployment Models (Public, Private, Hybrid), Service Models (IaaS, PaaS, SaaS), Benefits and Challenges.
Module 2: Virtualization
Introduction to Virtualization, Hypervisors, Types of Virtualization, Virtualization in Cloud Computing.
Module 3: Cloud Infrastructure
Cloud Architecture, Cloud Storage, Compute Services, Networking in Cloud.
Module 4: Cloud Security
Security Challenges in Cloud, Data Security, Identity and Access Management (IAM), Compliance and Privacy.
Module 5: Cloud Platforms
Overview of AWS, Azure, Google Cloud Platform (GCP). Case studies and comparisons.
Module 6: Future Trends
Serverless Computing, Edge Computing, Fog Computing, Multi-cloud and Inter-cloud strategies.
Module 1: Introduction to Soft Skills
Meaning and Importance of Soft Skills, Identifying and Assessing Soft Skills, Developing a Roadmap for Soft Skills Development.
Module 2: Communication Skills
Effective Listening, Verbal and Non-verbal Communication, Presentation Skills, Public Speaking, Body Language.
Module 3: Interpersonal Skills
Building Relationships, Empathy, Conflict Resolution, Teamwork, Collaboration, Networking.
Module 4: Leadership and Emotional Intelligence
Self-awareness, Self-regulation, Motivation, Social Skills, Leadership Styles, Time Management, Stress Management.
Module 5: Professional Etiquette
Email Etiquette, Telephone Etiquette, Meeting Etiquette, Workplace Ethics, Professional Grooming and Attire.
Module 6: Career Development
Resume Writing, Cover Letter Writing, Interview Skills, Group Discussion, Goal Setting, Career Planning.