NEP-GE-Electronic_Sci PDF

# Common Pool of Generic Electives (GE) Courses ## Offered by Department of Electronic Sciences ### Category-IV ## GENERIC ELECTIVES (GE-1): Fundamentals of Electronics ### Credit distribution, Eligibility and Pre-requisites of the Course | Course title & Code | Credits | Credit distribution of t...

# Common Pool of Generic Electives (GE) Courses ## Offered by Department of Electronic Sciences ### Category-IV ## GENERIC ELECTIVES (GE-1): Fundamentals of Electronics ### Credit distribution, Eligibility and Pre-requisites of the Course | Course title & Code | Credits | Credit distribution of the course | Eligibility criteria | Pre-requisite of the course | |---|---|---|---|---| | Fundamentals of Electronics | 4 | Lecture: 3 <br> Tutorial: 0 <br> Practical/Practice: 1 | None | None | | ELGE-1A | | | | | ### Learning Objectives The Learning Objectives of this course are as follows: - The paper equips the learners about basic circuit knowledge to analyze electric circuits using network theorems. - Understand diode and it's applications in clipping and clamping circuits, Rectifiers and design regulated power supply using Zener diodes. - To be able to plot the current voltage characteristics of Diode, Transistors and its different biasing conditions - Usage of semiconductor devices in designing the circuits. ### Learning outcomes The Learning Outcomes of this course are as follows: - **CO1** Study basic circuit concepts in a systematic manner suitable for analysis and design and further analyze the electric circuit using network theorems. - **CO2** To understand the different types of semiconductor devices and their characteristics - **CO3** Illustrate about working of transistors, transistor-based amplifiers and its biasing. - **CO4** Explain the concepts of feedback and oscillations and construct feedback amplifiers ## SYLLABUS OF GE-1 ### UNIT - I Basic Resistive Circuit (12 Hours) Ohm's Law, resistors in series and parallel combinations. DC voltage sources: ideal and non-ideal cases; DC current sources: ideal and non-ideal cases; Introduction to Kirchhoff's current law, Kirchhoff's voltage law, voltage divider circuit, current divider circuit; source transformations- voltage source to current source and current source to voltage source, basic problems. Resistive circuits: Thevenin's theorem, Norton theorem, Superposition theorem, Maximum power transfer theorem. ### UNIT - II PN-junction diode and its applications (12 Hours) PN junction, Unbiased PN junction, Forward and Reversed biased condition, IV-characteristics of PN junction diode, types of diodes – Zener diode, photo diode, LED. Diode circuits and power supplies. Half and full wave rectifiers, Bridge rectifier (qualitative comparison), Regulated power supply using Zener diode, Basic Clipper and Clamper circuits using diodes. ### UNIT – III Bipolar Junction Transistors (BJT) and Biasing (12 Hours) NPN Transistor and basic transistor action, Definition of a, ẞ and y and their interrelations, leakage currents, Modes of operation, Input and output characteristics of CB, CE and CC Configurations. Transistor biasing, thermal runaway, stability and stability factor, Fixed bias without and with RE, collector to base bias, voltage divider bias and emitter bias (+Vcc and -VEE bias), circuit diagrams and their working. ### UNIT – IV BJT Applications (12 Hours) BJT amplifier (CE), dc and ac load line analysis, Operating point, Concept of feedback, negative and positive feedback, advantages and disadvantages of negative feedback, voltage (series and shunt), current (series and shunt) feedback amplifiers, gain, input and output impedances. Positive feedback and Barkhausen criteria for oscillations. ### Practical component (if any) - Fundamentals of Electronic Lab (30 Hours) (Hardware and Circuit Simulation Software) ### Learning outcomes - **CO1** Verify the network theorems and operation of typical electrical circuits. - **CO2** Study various stages of a zener diode based regulated power supply. - **CO3** Understand various biasing concepts, BJT based amplifiers. 1. Study and operation of digital multi-meter, function generator, regulated power supply, CRO, etc. 2. Verification of KVL and KCL. 3. Verification of Superposition theorem. 4. Verification of Thevenin's, Norton's Theorem 5. Verification of Maximum power transfer theorem. 6. To plot the IV-characteristics of a ordinary and Zener diode and LED 7. Study of Half wave and Full Wave Rectifiers 8. Study of Fixed Bias, Voltage divider bias Feedback configuration for transistors. 9. Study of transistor amplifier circuit. **Note:** Students shall sincerely work towards completing all the above listed practicals for this course. In any circumstance, the completed number of practicals shall not be less than seven. ### Essential/recommended readings 1. R. L. Boylestad & Louis Nashlesky (2007), Electronic Devices &Circuit Theory, Pearson Education. 2. David A. Bell (2008), Electronic Devices and Circuits, Oxford University Press. 3. BL Theraja and AK Theraja, A Textbook Of Electrical Technology - Vol I. ### Suggestive readings 1. Donald A. Neamen, Electronic Circuit Analysis and Design, Tata McGraw Hill (2002) ## GENERIC ELECTIVES (GE-2): Data Engineering and Analytics ### Credit distribution, Eligibility and Pre-requisites of the Course | Course title & Code | Credits | Credit distribution of the course | Eligibility criteria | Pre-requisite of the course | |---|---|---|---|---| | Data Engineering and Analytics | 4 | Lecture: 3 <br> Tutorial: 0 <br> Practical/Practice: 1 | None | Basic Knowledge of Python Programming Language | | ELGE-1B | | | | | ### Learning Objectives The Learning Objectives of this course are as follows: The objective of this course is to introduce students to data analysis and impart them skills to solve data analytics problem. Data Engineering is basically designing and building pipelines that transform and transport data into a highly usable format before it reaches the Data Scientists or other end users. These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth. ### Learning outcomes The Learning Outcomes of this course are as follows: - **CO1** Use data analysis tools in the pandas library. - **CO2** Develop understanding of basic data analysis techniques. - **CO3** Collect, explore, clean, munge and manipulate data. - **CO4** Solve real world data analysis problems. - **CO5** Build data science applications using Python based toolkits. ## SYLLABUS OF GE-2 ### UNIT – I Mathematical Foundation for Data Engineering (12 Hours) Linear Algebra: Vectors, Matrices; Statistics: Describing a Single Set of Data, Correlation, Simpson's Paradox, Correlation and Causation; 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, P-hacking, Bayesian Inference ### UNIT – II Introduction to Data Engineering and Data Science (12 Hours) Relationship between Data Engineering and Data Science, Types of Data, Data file formats. Overview of Data Repositories; Data Warehouses, Data Marts, and Data Lakes. Introduction to ETL, ELT, and Data Pipelines. Data Integration Platforms, Traits of Big data, Analysis vs Reporting, Exploratory Data Analysis and Data Science Process. Motivation for using Python for Data Analysis. Introduction to Cloud Computing in Data Science Essential Python Libraries: NumPy, pandas, matplotlib, SciPy, scikit-learn, stats models ### UNIT – III Understanding Pandas and Data Wrangling (12 Hours) Getting Started with Pandas: Arrays and vectorized computation, Introduction to pandas Data Structures, Essential Functionality, Summarizing and Computing Descriptive Statistics. Data Loading, Cleaning, Preparation and Transformation. Data Wrangling: Hierarchical Indexing, Combining and Merging Data Sets Reshaping and Pivoting. ### UNIT – IV Data Aggregation and Analysis (9 Hours) Data Aggregation and Group operations: Group by Mechanics, Data aggregation, General split-apply-combine, Pivot tables and cross tabulation Time Series Data Analysis: Date and Time Data Types and Tools, Time series Basics, date Ranges, Frequencies and Shifting, Time Zone Handling, Periods and Periods Arithmetic, Resampling and Frequency conversion, Moving Window Functions. ### Practical component (if any) - Data Engineering and Analytics Lab (Python) (30 Hours) ### Learning outcomes - **CO1** Implement various data analysis tools in the pandas library. - **CO2** Implement various basic data analysis techniques, clean and filter and manipulate data. - **CO3** Solve real world data analysis problems. 1. Implement basic array statistical methods (sum, mean, std, var, min, max, argmin, argmax, cumsum and cumprod) and perform sorting operation with sort method. 2. Create a Data Frame and perform Matrix-like Operations on a Data Frame 3. Create a data frame with a following structure using pandas | EMP ID | EMP NAME | SALARY | START DATE | |---|---|---|---| | 1 | Satish | 50000 | 01-11-2017 | | 2 | Reeya | 75000 | 12-05-2016 |