| data science course in iit: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science course in iit: Introduction to Machine Learning Ethem Alpaydin, 2014-08-22 Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. |
| data science course in iit: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science course in iit: Data Reconciliation and Gross Error Detection Shankar Narasimhan, Cornelius Jordache, 1999-11-29 This book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained.Data errors can cause big problems in any process plant or refinery. Process measurements can be correupted by power supply flucutations, network transmission and signla conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of sensors, among other factors. Here's a book that helps you detect, analyze, solve, and avoid the data acquisition problems that can rob plants of peak performance. This indispensable volume provides crucial insights into data reconciliation and gorss error detection techniques that are essential fro optimal process control and information systems. This book is an invaluable tool for engineers and managers faced with the selection and implementation of data reconciliation software, or for those developing such software. For industrial personnel and students, Data Reconciliation and Gross Error Detection is the ultimate reference. |
| data science course in iit: Data Science for Engineers Raghunathan Rengaswamy, Resmi Suresh, 2022-12-16 With tremendous improvement in computational power and availability of rich data, almost all engineering disciplines use data science at some level. This textbook presents material on data science comprehensively, and in a structured manner. It provides conceptual understanding of the fields of data science, machine learning, and artificial intelligence, with enough level of mathematical details necessary for the readers. This will help readers understand major thematic ideas in data science, machine learning and artificial intelligence, and implement first-level data science solutions to practical engineering problems. The book- Provides a systematic approach for understanding data science techniques Explain why machine learning techniques are able to cross-cut several disciplines. Covers topics including statistics, linear algebra and optimization from a data science perspective. Provides multiple examples to explain the underlying ideas in machine learning algorithms Describes several contemporary machine learning algorithms The textbook is primarily written for undergraduate and senior undergraduate students in different engineering disciplines including chemical engineering, mechanical engineering, electrical engineering, electronics and communications engineering for courses on data science, machine learning and artificial intelligence. |
| data science course in iit: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science course in iit: Innumeracy John Allen Paulos, 2011-04-01 Readers of Innumeracy will be rewarded with scores of astonishing facts, a fistful of powerful ideas, and, most important, a clearer, more quantitative way of looking at their world. Why do even well-educated people understand so little about mathematics? And what are the costs of our innumeracy? John Allen Paulos, in his celebrated bestseller first published in 1988, argues that our inability to deal rationally with very large numbers and the probabilities associated with them results in misinformed governmental policies, confused personal decisions, and an increased susceptibility to pseudoscience of all kinds. Innumeracy lets us know what we're missing, and how we can do something about it. Sprinkling his discussion of numbers and probabilities with quirky stories and anecdotes, Paulos ranges freely over many aspects of modern life, from contested elections to sports stats, from stock scams and newspaper psychics to diet and medical claims, sex discrimination, insurance, lotteries, and drug testing. |
| data science course in iit: Big Data Computing Rajendra Akerkar, 2013-12-05 Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries |
| data science course in iit: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science course in iit: Mastering Python for Data Science Samir Madhavan, 2015-08-31 Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios. |
| data science course in iit: Machine Learning with TensorFlow, Second Edition Mattmann A. Chris, 2021-02-02 Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape |
| data science course in iit: Information For Efficient Decision Making: Big Data, Blockchain And Relevance Kashi R Balachandran, 2020-11-19 Can there be reliable information that is also relevant to decision making? Information for Efficient Decision Making: Big Data, Blockchain and Relevance focuses on the consolidation of information to facilitate making decisions in firms, in order to make their operations efficient to reduce their costs and consequently, increase their profitability. The advent of blockchain has generated great interest as an alternative to centralized organizations, where the data is gathered through a centralized ledger keeping of activities of the firm. The decentralized ledger keeping is one of the main features of blockchain that has given rise to many issues of technology, development, implementation, privacy, acceptance, evaluation and so on. Blockchain concept is a follow-up to big data environment facilitated by enormous progress in computer hardware, storage capacities and technological prowess. This has resulted in the rapid acquiring of data not considered possible earlier. With shrewd modeling analytics and algorithms, the applications have grown to significant levels. This handbook discusses the progress in data collection, pros and cons of collecting information on decentralized publicly available ledgers and several applications. |
| data science course in iit: Machine Learning Refined Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, 2020-01-09 An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises. |
| data science course in iit: Artificial Intelligence and Data Science Based R&D Interventions Ratnajit Bhattacharjee, |
| data science course in iit: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data |
| data science course in iit: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016-11-10 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
| data science course in iit: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science course in iit: Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Wright, 2012 An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. |
| data science course in iit: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| data science course in iit: CUDA Programming Shane Cook, 2012-11-13 'CUDA Programming' offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. |
| data science course in iit: Let Us Python Kanetkar Yashavant, 2019-09-20 Learn Python Quickly, A Programmer-Friendly Guide Key features Strengthens the foundations, as detailed explanation of programming language concepts are given. Lists down all important points that you need to know related to various topics in an organized manner. Prepares you for coding related interview and theoretical questions. Provides In depth explanation of complex topics and Questions. Focuses on how to think logically to solve a problem. Follows systematic approach that will help you to prepare for an interview in short duration of time. Description Most Programmer's learning Python are usually comfortable with some or the other programming language and are not interested in going through the typical learning curve of learning the first programming language. Instead, they are looking for something that can get them off the ground quickly. They are looking for similarities and differences in a feature that they have used in other language(s). This book should help them immediately. It guides you from the fundamentals of using module through the use of advanced object orientation. What will you learn Data types, Control flow instructions, console & File Input/Output Strings, list & tuples, List comprehension Sets & Dictionaries, Functions & Lambdas Dictionary Comprehension Modules, classes and objects, Inheritance Operator overloading, Exception handling Iterators & Generators, Decorators, Command-line Parsing Who this book is forStudents, Programmers, researchers, and software developers who wish to learn the basics of Python programming language. Table of contents1. Introduction to Python2. Python Basics3. Strings4. Control Flow Instructions5. Console Input/Output6. Lists7. Tuples8. Sets9. Dictionaries10. Functions11. Modules12. Classes and Objects13. Intricacies of Classes and Objects14. Inheritance15. Exception Handling16. File Input/Output17. MiscellanyAbout the authorYashavant KanetkarThrough his books and Quest Video Courses on C, C++, Java, Python, Data Structures, .NET, IoT, etc. Yashavant Kanetkar has created, moulded and groomed lacs of IT careers in the last three decades. Yashavant's books and Quest videos have made a significant contribution in creating top-notch IT manpower in India and abroad. Yashavant's books are globally recognized and millions of students / professionals have benefitted from them. Yashavant's books have been translated into Hindi, Gujarati, Japanese, Korean and Chinese languages. Many of his books are published in India, USA, Japan, Singapore, Korea and China. Yashavant is a much sought after speaker in the IT field and has conducted seminars/workshops at TedEx, IITs, IIITs, NITs and global software companies. Yashavant has been honored with the prestigious e;Distinguished Alumnus Awarde; by IIT Kanpur for his entrepreneurial, professional and academic excellence. This award was given to top 50 alumni of IIT Kanpur who have made significant contribution towards their profession and betterment of society in the last 50 years. In recognition of his immense contribution to IT education in India, he has been awarded the e;Best .NET Technical Contributore; and e;Most Valuable Professionale; awards by Microsoft for 5 successive years. Yashavant holds a BE from VJTI Mumbai and M.Tech. from IIT Kanpur. Yadhavant's current affiliations include being a Director of KICIT Pvt Ltd. And KSET Pvt Ltd. His Linkedin profile: linkedin.com/in/yashavant-kanetkar-9775255 Aditya Kanetkar holds a Master's Degree in Computer Science from Georgia Tech, Atlanta. Prior to that, he completed his Bachelor's Degree in Computer Science and Engineering from IIT Guwahati. Aditya started his professional career as a Software Engineer at Oracle America Inc. at Redwood City, California. Currently he works with Microsoft Corp., USA. Aditya is a very keen programmer since his intern fays at Redfin, Amazon Inc. and Arista Networks. His current passion is anything remotely connected to Python, Machine Learning and C# related technologies. His Linkedin Profile: linkedin.com/in/aditya-kanetkar-a4292397 |
| data science course in iit: OPTIMIZATION FOR ENGINEERING DESIGN KALYANMOY DEB, 2012-11-18 This well-received book, now in its second edition, continues to provide a number of optimization algorithms which are commonly used in computer-aided engineering design. The book begins with simple single-variable optimization techniques, and then goes on to give unconstrained and constrained optimization techniques in a step-by-step format so that they can be coded in any user-specific computer language. In addition to classical optimization methods, the book also discusses Genetic Algorithms and Simulated Annealing, which are widely used in engineering design problems because of their ability to find global optimum solutions. The second edition adds several new topics of optimization such as design and manufacturing, data fitting and regression, inverse problems, scheduling and routing, data mining, intelligent system design, Lagrangian duality theory, and quadratic programming and its extension to sequential quadratic programming. It also extensively revises the linear programming algorithms section in the Appendix. This edition also includes more number of exercise problems. The book is suitable for senior undergraduate/postgraduate students of mechanical, production and chemical engineering. Students in other branches of engineering offering optimization courses as well as designers and decision-makers will also find the book useful. Key Features Algorithms are presented in a step-by-step format to facilitate coding in a computer language. Sample computer programs in FORTRAN are appended for better comprehension. Worked-out examples are illustrated for easy understanding. The same example problems are solved with most algorithms for a comparative evaluation of the algorithms. |
| data science course in iit: Interdisciplinary Research in Technology and Management Satyajit Chakrabarti, Ashiq A. Sakib, Souti Chattopadhyay, Sanghamitra Poddar, Anupam Bhattacharya, Malay Gangopadhyaya, 2024-05-30 IRTM 2023 We live in an inter-connected world. In the era of Industry 5.0, technology is getting embedded more and more in the way ‘we learn, live, work and play’. This progression is accelerating at a pace never seen before. Inter disciplinary and collaborative research across disciplines within the Technology domain and Management domain, and across the Technology — Management interface is opening up exciting new possibilities for solving problems whose solutions are beyond the scope of a single discipline, domain or practice, and helping to create a brave, new world. We are living in an incredible time of change. Our effort to hold such an interdisciplinary conference, in the virtual mode, apparently resonated across the academic community, as was evident from the huge response that the first ever conference on “Interdisciplinary Research in Technology and Management”, (IRTM) held in February 2021 had received from participants across many countries. This has encouraged the organizers to hold the next edition of the conference physically in Kolkata on a larger scale in the online mode. The pandemic unleashed by Covid 19 in the last two years has shaken the socio-economic foundations of countries and societies to a point where the world cannot be the same as before the pandemic. It has re-focused the world’s attention on the priority of healthcare, and healthcare infrastructure and its innovative management. Inevitably, questions have again been raised more vehemently on what kind of a world we want to live in. Environmental concerns are being pursued with renewed vigour, The urgency of developing new, robust infrastructure relevant for the new world is gaining wider consensus. By 2030, as reports suggest, cyber – physical systems, internet of things and wearable technology will be everywhere and in everything, renewable energy will power the world, and digital entertainment will take centre stage among other developments. The third edition of the conference on “Interdisciplinary Research in Technology and Management” attempts to spotlight the above concerns. The number of tracks on which papers are invited from scholars, researchers, consultants and practitioners to share their interdisciplinary research and consultative work has been enlarged. As before, the papers will be peer reviewed and authors of the selected papers will be invited to present their papers in the IRTM conference. The presentation of papers will be interspersed with Keynote Talks by eminent experts on the theme of the conference or individual domains. |
| data science course in iit: Data Science Uncovering the Reality Pulkit Bansal, Kunal Kishore, Pankaj Gupta, Srijan Saket, Neeraj Kumar, 2020-04-15 Data Science has become a popular field of work today. However a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravel how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here. This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies. How Data Science leaders actually work and the challenges they face. |
| data science course in iit: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science course in iit: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| data science course in iit: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| data science course in iit: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| data science course in iit: Neural Networks and Deep Learning Charu C. Aggarwal, 2018-08-25 This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. |
| data science course in iit: Introduction to IoT Sudip Misra, Anandarup Mukherjee, Arijit Roy, 2021-06-10 A valuable guide for new and experienced readers, featuring the complex and massive world of IoT and IoT-based solutions. |
| data science course in iit: Kurukshetra August 2024 (English) Publications Division, A monthly published in Hindi and English. The journal is devoted to all aspects of rural reconstruction and village democracy. The journal carries educative and informative articles on rural development and is useful for scholars, academicians and students preparing for civil services and other competitive examinations. |
| data science course in iit: Advances in Data Science and Artificial Intelligence Rajiv Misra, Nishtha Kesswani, Muttukrishnan Rajarajan, Bharadwaj Veeravalli, Imene Brigui, Ashok Patel, T. N. Singh, 2023-05-13 With the intriguing development of technologies in several industries along with the advent of accrescent and ubiquitous computational resources, it creates an ample number of opportunities to develop innovative intelligence technologies in order to solve the wide range of uncertainties, imprecision, and vagueness issues in various real-life problems. Hybridizing modern computational intelligence with traditional computing methods has attracted researchers and academicians to focus on developing innovative AI techniques using data science. International Conference on Data Science and Artificial Intelligence (ICDSAI) 2022, organized on April 23-24, 2022 by the Indian Institute of Technology, Patna at NITIE Mumbai (India) in collaboration with the International Association of Academicians (IAASSE) USA collected scientific and technical contributions with respect to models, tools, technologies, and applications in the field of modern Artificial Intelligence and Data Science, covering the entire range of concepts from theory to practice, including case studies, works-in-progress, and conceptual explorations. |
| data science course in iit: Applied Statistics and Probability for Engineers Douglas C. Montgomery, George C. Runger, 2005-09-02 * More Motivation - A completely revised chapter 1 gets students motivated right from the beginning. * Revised Probability Topics - The authors have revised and enhanced probability topics to promote even easier understanding. * Chapter Reorganization - Chapters on hypothesis testing and confidence intervals have been reorganized and rewritten. There is now expanded treatment of confidence intervals, prediction intervals, and tolerance intervals. * Real Engineering Applications - Treatment of all topics is oriented towards real engineering applications. In the probability chapters, the authors do not emphasize counting methods or artificial applications such as gambling. * Real Data, Real Engineering Situations - Examples and exercises throughout text use real data and real engineering situations. This motivates students to learn new concepts and gives them a taste of practical engineering experience. Use of the Computer - Computer usage is closely integrated into the text and homework exercises. |
| data science course in iit: The Fourth IIT Surya Pratap Mehrotra, Prajapati Prasad Sah, 2015-11-10 The Fourth IIT traces the historical evolution of the Indian Institute of Technology Kanpur (IITK), established fourth in the chronological ladder of IITs after the institutes at Kharagpur, Bombay and Madras. The early beginnings of IITK are explored, with the appointment of Dr P.K. Kelkar as its founder-director, its humble commencement in the temporary premises of Harcourt Butler Technological Institute (HBTI) and the initiation of a traditional BTech programme. We see how rapid transformations enabled the institute to introduce and nurture a new academic culture in the country, illustrated by the paradigm shift in higher technical education and the freshness of a new spirit in higher education in general—the spirit of IITK. An inventive approach to faculty appointments, student admissions and the development of a novel academic structure are some of the deeply appreciated attributes that IITK has epitomized—and striven for. The book also captures IITK in the present times, in its pursuit of continually improving the material life of its students, staff members and the faculty, and the veritably important role played by the alumni, and also sheds light on the ‘new vision’ of the institute. Expertly and lovingly written by IITK insiders and long-timers, The Fourth IIT is ideal for past and present students and educators, and for anyone interested in an in-depth analysis of one of the most beloved and respected academic institutions in the country. |
| data science course in iit: Smarter Data Science Neal Fishman, Cole Stryker, 2020-04-14 Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations. |
| data science course in iit: Design and Optimization of Wearable, Implantable, and Edible Antennas Kavitha, K., Sabapathy, Thennarasan, Rajeshkumar, V., 2024-08-16 The demand for integration of smart devices into our daily lives has led to a pressing challenge – the effective design and optimization of antennas for wearable and implantable applications. As our reliance on interconnected devices grows, so does the need for antennas that transcend their conventional roles and adapt to the diverse, dynamic needs of users. Addressing these challenges is vital, considering the unique demands imposed by this technology, ranging from size constraints to energy efficiency, biocompatibility, and signal integrity. Design and Optimization of Wearable, Implantable, and Edible Antennas, is an innovative work that confronts these challenges head-on. In this exploration, the book sheds light on the evolving landscape where electromagnetic research intersects with the demands of human life. As antennas seamlessly weave into attire, revolutionize healthcare through implants, and even find their place in edibles, this book serves as a guide for academic scholars, researchers, engineers, and students navigating the intricate terrain of antenna engineering. |
| data science course in iit: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| data science course in iit: Cross Reality and Data Science in Engineering Michael E. Auer, Dominik May, 2020-08-20 Today, online technologies are at the core of most fields of engineering and society as a whole . This book discusses the fundamentals, applications and lessons learned in the field of online and remote engineering, virtual instrumentation, and other related technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Since the first Remote Engineering and Virtual Instrumentation (REV) conference in 2004, the event has focused on the use of the Internet for engineering tasks, as well as the related opportunities and challenges. In a globally connected world, interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In this context, the REV conferences discuss fundamentals, applications and experiences in the field of Online and Remote Engineering as well as Virtual Instrumentation. Furthermore, the conferences focus on guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and open resources. This book presents the proceedings of REV2020 on “Cross Reality and Data Science in Engineering” which was held as the 17th in series of annual events. It was organized in cooperation with the Engineering Education Transformations Institute and the Georgia Informatics Institutes for Research and Education and was held at the College of Engineering at the University of Georgia in Athens (GA), USA, from February 26 to 28, 2020. |
| data science course in iit: Executive Data Science Roger Peng, 2016-08-03 In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects. |
| data science course in iit: The Elements of Computing Systems Noam Nisan, Shimon Schocken, 2008 This title gives students an integrated and rigorous picture of applied computer science, as it comes to play in the construction of a simple yet powerful computer system. |
CERTIFICATE PROGRAMME IN DATA SCIENCE & MACHINE …
Become industry-ready with an in-depth understanding of in-demand data science and machine learning tools and techniques with Python. WHO IS THIS PROGRAMME FOR? The …
PROGRAMMING AND DATA SCIENCE - Indian Institute of …
Why Programming & Data Science? IIT Madras, India’s top technical institute, welcomes you to the world’s first online BSc Degree program in Programming and Data Science.
Certificate in Data Analytics - IIT Guwahati
Module 1 - Data Science Funda-mentals • Thought Experiment: Data Science from a layman’s perspective • Brief intro to Data Science • How companies use Data Science • Overview of …
Curriculum for M. Tech in Data Science (MDS)
The Department of Data Science at IIT Palakkad presents the curriculum of a transdisciplinary master program M. Tech degree in Data Science (MDS). The program is going to be jointly …
B.Tech in Data Science and Engineering - IIT Mandi
Given the mounting importance of the data science paradigm, IIT Mandi has decided to start a new 4 years bachelor program on Data Science and Engineering (DSE). The curriculum of the …
DA Data Science and Artificial Intelligence - IIT Roorkee
transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: …
M.Tech. Data Science - IIT Roorkee
M.Tech. (Data Science) Program Elective Courses Teaching Scheme Contact Hours/Week Exam Duration S.No. Subject Code Course Title Subject Area Credits L T P Theory Practical 1. DAL …
DEPARTMENT OF DATA SCIENCE & ARTIFICIAL INTELLIGENCE
In keeping with this tradition, the M.Tech. programme is designed to cater to students with different academic backgrounds. We expect the graduates of this program will be highly …
M.Tech. in Data Science (MDS) Information Brochure (Year 2024)
Department of Computer Science and Engineering, IIT Hyderabad has introduced the MTech in Data Sciences (MDS) program in the Academic Year 2018. MDS is a self-paced program of …
Statistics & Data Science Indian Institute of Technology ... - IIT …
Beginning academic year 2021-2022, the Department of Mathematics & Statistics at the Indian Institute of Technology, Kanpur will offer a new BS program in “Statistics and Data Science”.
DATA SCIENCE AND ARTIFICIAL INTELLIGENCE - LEADERSHIP …
programme on Data Science and Artificial Intelligence - Leadership Essentials. This is a 2 month course that aims to give a high level overview of machine learning/deep learning along with …
Advanced Certificate Programme in DATA SCIENCE
Data Science Upon succesful completion of the Advanced Certificate Programme in Data Science, get an opportunity to upgrade to the NASSCOM Certified Executive PG Programme …
B. TECH. (DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
MEHTA FAMILY SCHOOL FOR DATA SCIENCE AND ARTIFICIAL INTELLIGENCE Program Code : 125 B. Tech. (Data Science and Artificial Intelligence) Department : DSAI Mehta Family …
POST GRADUATE CERTIFICATE PROGRAM IN DATA SCIENCE …
Data Analytics Using Programming Tools. The course syllabus will be delivered through a combination of eLearning resources, digital lectures, community based digital classrooms as …
IIT Mandi, NSDC launches certificate programs in Data …
IIT Mandi, NSDC launches certificate programs in Data Science & ML The program will allow learners to build a strong foundation in Data Science and specialize in ML with Python for data …
M.Tech. in Data Science (MDS) Information Brochure (Year 2024)
Department of Computer Science and Engineering, IIT Hyderabad has introduced the MTech in Data Sciences (MDS) program in Academic Year 2018. the MDS is a self-paced program of …
Executive Program in Data Science & AI - IIT Guwahati
IIT Guwahati Executive Program in Data Science & AI 10 Months | Online Instructor Led Improve your career prospects by gaining expertise in Data Science, Machine Learning, and Artificial …
EXECUTIVE PROGRAM IN DATA SCIENCE
Equip students with practical data science and AI skills, including data cleaning, data visualization, machine learning algorithms, deep learning techniques, natural language processing, and …
M.Tech. in Data Science (MDS) - Department of CSE, IIT …
With these factors in mind, the CSE Department at IIT Hyderabad proposes a three-year course-work based M.Tech. program in Data Science area that is flexible and can be self-paced. This …
PROGRAMMING AND DATA SCIENCE - Indian Institute of …
Why Programming & Data Science? IIT Madras, India’s top technical institute, welcomes you to the world’s first online BSc Degree program in Programming and Data Science. For the first …
INTERNSHIP & PLACEMENT BROCHURE - 2023 - Indian …
B.S. Degree program in Data Science and Applications, providing learners with a comprehensive educational experience spanning four stages: 1. Foundation level, 2. Diploma level, 3. B.Sc. …
Natural numbers and their operations - Indian Institute of …
Mathematics for Data Science - 1 Frequently Asked Questions Week 1 1. Is 0 a natural number? There are two conventions: some books consider 0 as a natural number, and some do not. In …
Step 3. If subject marks > max then store subject marks in max
Similarly, in step 5 variable B gets updated only when Chemistry marks are lower than the current value of variable B which confirms that the variable B is used for Lowest marks in
