JNTUH CSE-AIML INTRODUCTION TO DATA SCIENCE SYLLABUS

JNTUH CSE-AIML INTRODUCTION TO DATA SCIENCE SYLLABUS

Course Objectives:
1. Learn concepts, techniques and tools they need to deal with various facets of data science
practice, including data collection and integration
2. Understand the basic types of data and basic statistics
3. Identify the importance of data reduction and data visualization techniques
Course Outcomes: After completion of the course, the student should be able to
1. Understand basic terms what Statistical Inference means.
2. Identify probability distributions commonly used as foundations for statistical modelling. Fit a
model to data
3. describe the data using various statistical measures
4. utilize R elements for data handling
5. perform data reduction and apply visualization techniques.
UNIT – I
Introduction: Definition of Data Science- Big Data and Data Science hype – and getting past the hype – Datafication – Current landscape of perspectives – Statistical Inference – Populations and samples -Statistical modeling, probability distributions, fitting a model – Over fitting. Basics of R: Introduction, REnvironment Setup, Programming with R, Basic Data Types.
UNIT – II
Data Types & Statistical Description Types of Data: Attributes and Measurement, What is an Attribute? The Type of an Attribute, The Different Types of Attributes, Describing Attributes by the Number of Values, Asymmetric Attributes, Binary Attribute, Nominal Attributes, Ordinal Attributes, Numeric Attributes, Discrete versus Continuous
Attributes. Basic Statistical Descriptions of Data: Measuring the Central Tendency: Mean, Median, and Mode, Measuring the Dispersion of Data: Range, Quartiles, Variance, Standard Deviation, and Interquartile Range, Graphic Displays of Basic Statistical Descriptions of Data.
UNIT – III
Vectors: Creating and Naming Vectors, Vector Arithmetic, Vector sub setting, Matrices: Creating and Naming Matrices, Matrix Sub setting, Arrays, Class. Factors and Data Frames: Introduction to Factors: Factor Levels, Summarizing a Factor, Ordered Factors, Comparing Ordered Factors, Introduction to Data Frame, subsetting of Data Frames, Extending Data Frames, Sorting Data Frames. Lists: Introduction, creating a List: Creating a Named List, Accessing List Elements, Manipulating List Elements, Merging Lists, Converting Lists to Vectors
UNIT – IV
Conditionals and Control Flow: Relational Operators, Relational Operators and Vectors, Logical  Operators, Logical Operators and Vectors, Conditional Statements. Iterative Programming in R: Introduction, While Loop, For Loop, Looping Over List. Functions in R: Introduction, writing a Function in R, Nested Functions, Function Scoping, Recursion, Loading an R Package, Mathematical Functions in R.
UNIT – V
Data Reduction: Overview of Data Reduction Strategies, Wavelet Transforms, Principal Components Analysis, Attribute Subset Selection, Regression and Log-Linear Models: Parametric Data Reduction,Histograms, Clustering, Sampling, Data Cube Aggregation. Data Visualization: Pixel-OrientedVisualization Techniques, Geometric Projection Visualization Techniques, Icon-Based Visualization Techniques, Hierarchical Visualization Techniques, Visualizing Complex Data and Relations.
TEXT BOOKS:
1. Doing Data Science, Straight Talk from The Frontline. Cathy O’Neil and Rachel Schutt, O’Reilly, 2014
2. Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems.
3. K G Srinivas, G M Siddesh, “Statistical programming in R”, Oxford Publications.
REFERENCE BOOKS:
1. Introduction to Data Mining, Pang-Ning Tan, Vipin Kumar, Michael Steinbanch, Pearson Education.
2. Brain S. Everitt, “A Handbook of Statistical Analysis Using R”, Second Edition, 4 LLC, 2014.
3. Dalgaard, Peter, “Introductory statistics with R”, Springer Science & Business Media, 2008.
4. Paul Teetor, “R Cookbook”, O’Reilly, 2011

CSE-AIML

SEMESTER SUBJECT CODE SUBJECT Lession Plan Lecturer Notes & Question Bank SYLLABUS ABOUT TUTOR
I MA101BS Mathematics – I Click Here
I AP102BS Applied Physics Click Here
I CS103ES Programming for Problem Solving Click Here
I ME104ES Engineering Graphics Click Here
I AP105BS Applied Physics Lab Click Here
I CS106ES Programming for Problem Solving Lab Click Here
I MC109ES Environmental Science Click Here
II MA201BS Mathematics – II Click Here
II CH202BS Chemistry Click Here
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II ME205ES Engineering Workshop Click Here
II EN205HS English Click Here
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II EN207HS English Language and Communication Skills Lab Click Here
II EE208ES Basic Electrical Engineering Lab Click Here
II-I CS310PC Discrete Mathematics Click Here
II-I CS302PC Data Structures Click Here
II-I MA313BS Mathematical and Statistical Foundations Click Here
II-I CS304PC Computer Organization and Architecture Click Here
II-I CS311PC Python Programming Click Here
II-I SM306MS Business Economics & Financial Analysis Click Here
II-I CS307PC Data Structures Lab Click Here
II-I CS312PC Python Programming Lab Click Here
II-I MC309 Gender Sensitization Lab Click Here
II-II CS416PC Formal Language and Automata Theory Click Here
II-II CS417PC Software Engineering Click Here
II-II CS403PC Operating Systems Click Here
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II-II CS412PC Object Oriented Programming using Java Click Here
II-II CS406PC Operating Systems Lab Click Here
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II-II CS408PC Java Programming Lab Click Here
II-II MC409 Constitution of India Click Here
III-I Design and Analysis of Algorithms Click Here
III-I Machine Learning Click Here
III-I Computer Networks Click Here
III-I Compiler Design Click Here
III-I Graph Theory (PE1) Click Here
III-I Introduction to Data Science(PE1) Click Here
III-I Web Programming(PE1) Click Here
III-I Image Processing(PE1) Click Here
III-I Computer Graphics(PE1) Click Here
III-I Software Testing Methodologies(PE2) Click Here
III-I Information Retrieval Systems(PE2) VIJAYANAND S
III-I Pattern Recognition(PE2) Click Here
III-I Computer Vision and Robotics(PE2) Click Here Click Here
III-I Data Warehousing and Business Intelligence(PE2) Click Here
III-I Machine Learning Lab Click Here
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III-I Advanced Communication Skills Lab Click Here
III-I Intellectual Property Rights Click Here
III-II Artificial Intelligence Click Here Click Here
III-II DevOps Click Here Click Here
III-II Natural Language Processing Click Here Click Here
III-II Internet of Things(PE3) Click Here Click Here
III-II Data Mining(PE3) Click Here Click Here
III-II Scripting Languages(PE3) Click Here Click Here
III-II Mobile Application Development(PE3) Click Here Click Here
III-II Cryptography and Network Security(PE3) Click Here Click Here
III-II Artificial Intelligence and Natural Language Processing Lab Click Here Click Here
III-II DevOps Lab Click Here Click Here
IV-I Neural Networks & Deep Learning Click Here Click Here
IV-I Reinforcement Learning Click Here Click Here
IV-I Quantum Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Social Network Analysis(PE5) Click Here Click Here
IV-I Federated Machine Learning(PE5) Click Here Click Here
IV-I Augmented Reality & Virtual Reality(PE5) Click Here Click Here
IV-I Web Security(PE5) Click Here Click Here
IV-I Ad-hoc & Sensor Networks Click Here Click Here
IV-I Deep Learning Lab Click Here Click Here
IV-II Organizational Behaviour Click Here Click Here
IV-II Speech and Video Processing(PE6) Click Here Click Here
IV-II Robotics Process Automation(PE6) Click Here Click Here
IV-II Randomized Algorithms(PE6) Click Here Click Here
IV-II Cognitive Computing(PE6) Click Here Click Here
IV-II Semantic Web(PE6) Click Here Click Here

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