The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters

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The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What You Need to Know about Data Science and why it Matters

Author : Vlad Sozonov
Publisher : Vinco Publishing
ISBN 13 : 9781950766857
Total Pages : 118 pages
Book Rating : 4.7/5 (668 download)

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Book Synopsis The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What You Need to Know about Data Science and why it Matters by : Vlad Sozonov

Download or read book The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What You Need to Know about Data Science and why it Matters written by Vlad Sozonov and published by Vinco Publishing. This book was released on 2019-11-21 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is no easy term to define. While there are many definitions available that point out its statistical or logical aspects, others focus on its machine learning impacts. Today only, get this Amazon book for just $19.99 for a limited time. Regularly priced at $35.99. The truth is, data science is a process that requires an understanding of multiple fields, methods, techniques, and more. Data science cannot be easily labeled because, when applied, it looks different to each person, business, or organization utilizing it. While the term may not be easy to define, what it is used for, can be used for, and approaches to it can be more easily understood. And that is precisely what this book aims to do. Scroll Up & Click to Buy Now! Here Is A Preview Of What You'll Discover...In this step-by-step book: This book will not only thoroughly go over all the skills, people, and steps involved in data science, it will also look closely at: ● What big data is and how data science came from it. ● How data has evolved, resulting in new methods for understanding it. ● How data science influenced artificial intelligence. ● How data science is used in machine learning and deep learning. ● How data science revolutionizes the way we train machines and set up neural networks. Data science, big data, machine learning, and deep learning tend to intimidate people. Many believe it is too complicated or technology-centered for them to break into these fields. This book is designed to simplify these complex areas in a way that anyone can understand the fundamentals. Whether you are just hearing about data science, are a student studying it in college, or looking to expand your career, this book has something to offer every type of data enthusiast. Order your copy today! Take action right away by purchase this book "The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What you need to know about data science and why it matters.", for a limited time discount of only $19.99! Hurry Up!! Tags: ● data science quick ● data science strategy ● data science trading ● data science journal ● insight data science ● data science salary ● data science jobs ● data science espanol ● data science case study ● data science beginner guide

The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What You Need to Know about Data Science and why it Matters

Author : Vlad Sozonov
Publisher : Vinco Publishing
ISBN 13 : 9781950766857
Total Pages : 118 pages
Book Rating : 4.7/5 (668 download)

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Book Synopsis The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What You Need to Know about Data Science and why it Matters by : Vlad Sozonov

Deep Learning: Convergence to Big Data Analytics

Author : Murad Khan
Publisher : Springer
ISBN 13 : 9811334595
Total Pages : 79 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan

Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Introduction to Data Science and Machine Learning

Author : Keshav Sud
Publisher : BoD – Books on Demand
ISBN 13 : 1838803335
Total Pages : 233 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Introduction to Data Science and Machine Learning by : Keshav Sud

Download or read book Introduction to Data Science and Machine Learning written by Keshav Sud and published by BoD – Books on Demand. This book was released on 2020-03-25 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.

Data Science

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262535432
Total Pages : 282 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Data Science by : John D. Kelleher

Download or read book Data Science written by John D. Kelleher and published by MIT Press. This book was released on 2018-04-13 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Deep Learning in Data Analytics

Author : Debi Prasanna Acharjya
Publisher : Springer Nature
ISBN 13 : 3030758559
Total Pages : 271 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Deep Learning in Data Analytics by : Debi Prasanna Acharjya

Download or read book Deep Learning in Data Analytics written by Debi Prasanna Acharjya and published by Springer Nature. This book was released on 2021-08-11 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.

Data Science Fundamentals Part 1

Author : Jonathan Dinu
Publisher :
ISBN 13 : 9780134660141
Total Pages : pages
Book Rating : 4.6/5 (61 download)

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Book Synopsis Data Science Fundamentals Part 1 by : Jonathan Dinu

Download or read book Data Science Fundamentals Part 1 written by Jonathan Dinu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air...

Deep Learning for Beginners

Author : Steven Cooper
Publisher : Roland Bind
ISBN 13 :
Total Pages : 83 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Deep Learning for Beginners by : Steven Cooper

Download or read book Deep Learning for Beginners written by Steven Cooper and published by Roland Bind. This book was released on 2018-11-06 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: ☆★The Best Deep Learning Book for Beginners★☆ If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading. This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley. This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible. ★★ Grab your copy today and learn ★★ ♦ Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google. ♦ The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems. ♦ The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it. ♦ The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions. ♦ And much more... The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch. When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? If you want to get started on deep learning and the concepts that run artificial technologies, don't wait any longer. Scroll up and click the buy now button to get this book today!

Fundamentals of Data Science DataMining MachineLearning DeepLearning and IoTs

Author : Dr. P. Kavitha
Publisher : Leilani Katie Publication
ISBN 13 : 8196856768
Total Pages : 162 pages
Book Rating : 4.1/5 (968 download)

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Book Synopsis Fundamentals of Data Science DataMining MachineLearning DeepLearning and IoTs by : Dr. P. Kavitha

Download or read book Fundamentals of Data Science DataMining MachineLearning DeepLearning and IoTs written by Dr. P. Kavitha and published by Leilani Katie Publication. This book was released on 2023-12-23 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. P. Kavitha, Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India. Mr. P. Jayasheelan, Assistant Professor, Department of Computer Science, Sri Krishna Aditya College of arts and Science, Coimbatore, Tamil Nadu, India. Ms. C. Karpagam, Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India. Dr. K. Prabavathy, Assistant Professor, Department of Data Science and Analytics, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore, Tamil Nadu, India.

Data Science

Author : Herbert Jones
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781729642399
Total Pages : 128 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Data Science by : Herbert Jones

Download or read book Data Science written by Herbert Jones and published by Createspace Independent Publishing Platform. This book was released on 2018-11 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Did you know that the value of data usage has increased job opportunities, but that there are few specialists? These days, everyone is aware of the role that data can play, whether it is an election, business or education. But how can you start working in a wide interdisciplinary field that is occupied with so much hype? This book, Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't, presents you with a step-by-step approach to Data Science as well as secrets only known by the best Data Scientists. It combines analytical engineering, Machine Learning, Big Data, Data Mining, and Statistics in an easy to read and digest method. Data gathered from scientific measurements, customers, IoT sensors, and so on is very important only when one can draw meaning from it. Data Scientists are professionals that help disclose interesting and rewarding challenges of exploring, observing, analyzing, and interpreting data. To do that, they apply special techniques that help them discover the meaning of data. Becoming the best Data Scientist is more than just mastering analytic tools and techniques. The real deal lies in the way you apply your creative ability like expert Data Scientists. This book will help you discover that and get you there. The goal with Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't is to help you expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data centric issues. At the end of this book, you will learn how to combine Machine Learning, Data Mining, analytics, and programming, and extract real knowledge from data. As you read, you will discover important statistical techniques and algorithms that are helpful in learning Data Science. When you have finished, you will have a strong foundation to help you explore many other fields related to Data Science. This book will discuss the following topics: What Data Science is What it takes to become an expert in Data Science Best Data Mining techniques to apply in data Data visualization Logistic regression Data engineering Machine Learning Big Data Analytics And much more! Don't waste any time. Grab your copy today and learn quick tips from the best Data scientists!

Fundamentals of Data Science

Author : Samuel Burns
Publisher :
ISBN 13 : 9781693798924
Total Pages : 134 pages
Book Rating : 4.7/5 (989 download)

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Book Synopsis Fundamentals of Data Science by : Samuel Burns

Download or read book Fundamentals of Data Science written by Samuel Burns and published by . This book was released on 2019-09-17 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is for students or anyone, with limited or no prior programming, statistics, and data analytics knowledge. This short guide is ideal for absolute beginners, or anyone who wants to acquire a basic working knowledge of data science. It is an excellent guide if you want to learn about the principals of data science from scratch, in just a few hours. The author discussed everything that you need to know about data science. First, you are guided to learn the meaning of data science. The history of data science has been discussed to help you know how people came to realize that data is a rich source of knowledge and intelligence. The theories underlying data science have been discussed. Examples include decision and estimation theories. The author discussed the various machine learning algorithms used in data science and the various steps one has to undergo when performing data science tasks, from data collection to data presentation and visualization. The author helps you to know the various ways through which you can apply data science in your business for increased profits. A simple language has been used to ensure ease of understanding, especially for beginners." --

Data Science Programming All-in-One For Dummies

Author : John Paul Mueller
Publisher : John Wiley & Sons
ISBN 13 : 1119626110
Total Pages : 768 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Data Science Programming All-in-One For Dummies by : John Paul Mueller

Download or read book Data Science Programming All-in-One For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Author : R. Sujatha
Publisher : CRC Press
ISBN 13 : 1000454533
Total Pages : 216 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by : R. Sujatha

Download or read book Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics written by R. Sujatha and published by CRC Press. This book was released on 2021-09-22 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

Machine Learning for Data Science Handbook

Author : Lior Rokach
Publisher : Springer Nature
ISBN 13 : 3031246284
Total Pages : 975 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning for Data Science Handbook by : Lior Rokach

Download or read book Machine Learning for Data Science Handbook written by Lior Rokach and published by Springer Nature. This book was released on 2023-08-17 with total page 975 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.

A Beginner's Guide To DATA SCIENCE

Author : Enamul Haque
Publisher :
ISBN 13 : 9781447826552
Total Pages : 0 pages
Book Rating : 4.8/5 (265 download)

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Book Synopsis A Beginner's Guide To DATA SCIENCE by : Enamul Haque

Download or read book A Beginner's Guide To DATA SCIENCE written by Enamul Haque and published by . This book was released on 2023-01-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed for aspiring data scientists who want to start their careers in data science, even if they don't have coding skills. It provides a comprehensive introduction to the foundations of data science and its applications, using simple language that is easy for beginners to understand. No technical expertise is required to master the material in this book. It is an ideal resource for anyone looking to learn about data science in an accessible and straightforward way. Key features include: Introduction to data science History of data science Data science life-cycle Data science tools and technologies Data science methodology Data science models Developing data science business strategy Managing data science projects Becoming a data scientist, data engineer etc. Big data Data Mining Artificial intelligence Machine learning Deep learning Neural networks Mathematical analysis Statistical modelling Understanding the fundamentals of data science programming languages Database structures and principles Robotic Process Automation Data science acronyms You need to know And a lot more.

A Beginners Guide To DATA SCIENCE

Author : Enamul Haque
Publisher :
ISBN 13 :
Total Pages : 288 pages
Book Rating : 4.7/5 (312 download)

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Book Synopsis A Beginners Guide To DATA SCIENCE by : Enamul Haque

Download or read book A Beginners Guide To DATA SCIENCE written by Enamul Haque and published by . This book was released on 2021-03-31 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Calling all the Aspiring Data Scientists! This book is your "one-stop-shop" to kick start your data science career without knowing how to code! In fact, data science doesn't have to be complicated! With this book, you will grow an understanding of the foundations of data science and its applications. To master this book, you don't need technical abilities. This book is recommended for beginners and anybody who want to understand data science conveniently. You don't need a big textbook to master data science today. A straightforward language has been used to ensure ease of understanding, especially for beginners. Key features include: Introduction to data scienceHistory of data scienceData science life-cycleData science tools and technologiesData science methodologyData science modelsDeveloping data science business strategyManaging data science projectsBecoming a data scientist, data engineers etc.Doing data science without codingBig dataData MiningArtificial intelligenceMachine learningDeep learningNeural networksMathematical analysisStatistical modellingUnderstanding the fundamentals of Python and RDatabase structures and principlesRobotic Process AutomationData science acronyms you need to knowOnline free data science learning resources And a lot more