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| api for data science: Building Data Science Applications with FastAPI Francois Voron, 2021-10-08 Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended. |
| api for data science: R Data Science Quick Reference Thomas Mailund, 2019-08-07 In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended. |
| api for data science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 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 |
| api for data science: Hands-On APIs for AI and Data Science Ryan Day, 2025-06-03 To succeed in AI and data science, you must first master APIs. API skills are essential for AI and data science success. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. Part 1 takes you step-by-step through coding projects to build APIs using Python and FastAPI and deploy them in the cloud. Part 2 teaches you to consume APIs in a data science project using industry-standard tools. And in Part 3, you'll use ChatGPT, the LangChain framework, and other tools to access your APIs with generative AI and large language models (LLMs). As you complete the chapters in the book, you'll be creating a professional online portfolio demonstrating your new skill with APIs, AI, and data science. You'll learn how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Publish a developer portal for your API Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs Author Ryan Day is a data scientist in the financial services industry and an open source developer. |
| api for data science: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| api for data science: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover. |
| api for data science: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| api for data science: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| api for data science: Applied Spatial Data Analysis with R Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio, 2013-06-21 Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. |
| api for data science: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| api for data science: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website. |
| api for data science: The Well-Grounded Python Developer Doug Farrell, 2023-09-12 If you’re new to Python, it can be tough to understand when, where, and how to use all its language features. This friendly guide shows you how the Python ecosystem fits together, and grounds you in the skills you need to continue your journey to being a software developer. Summary Inside The Well-Grounded Python Developer you will discover: Building modules of functionality Creating a well-constructed web server application Integrating database access into your Python applications Refactor and decoupling systems to help scale them How to think about the big picture of your application The Well-Grounded Python Developer builds on Python skills you’ve learned in isolation and shows you how to unify them into a meaningful whole. It helps you understand the dizzying array of libraries and teaches important concepts, like modular construction, APIs, and the design of a basic web server. As you work through this practical guide, you’ll discover how all the bits of Python link up as you build and modify a typical web server application—the kind of web app that’s in high demand by modern businesses. About the technology As a new programmer, you’re happy just to see your code run. A professional developer, on the other hand, needs to create software that runs reliably. It must be fast, maintainable, scalable, secure, well designed and documented, easy for others to update, and quick to ship. This book teaches you the skills you need to go from Python programmer to Python developer. About the book The Well-Grounded Python Developer shows you why Python, the world’s most popular programming language, is a fantastic tool for professional development. It guides you through the most important skills, like how to name variables, functions, and classes, how to identify and write a good API, and how to use objects. You’ll also learn how to deal with inevitable failures, how to make software that connects to the internet, core security practices, and many other professional-grade techniques. What's inside Create a web application Connect to a database Design programs to handle big tasks About the reader For experienced beginners who want to learn professional-level skills. About the author Doug Farrell has been a professional developer since 1983, and has worked with Python for over 20 years. Table of Contents 1 Becoming a Pythonista PART 1 - GROUNDWORK 2 That’s a good name 3 The API: Let’s talk 4 The object of conversation 5 Exceptional events PART 2 - FIELDWORK 6 Sharing with the internet 7 Doing it with style 8 Do I know you? Authentication 9 What can you do? Authorization 10 Persistence is good: Databases 11 I’ve got something to say 12 Are we there yet? |
| api for data science: 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 |
| api for data science: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| api for data science: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 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! |
| api for data science: Data Science Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom, Lindsey Heagy, 2024-08-23 Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia. Key Features: Includes autograded worksheets for interactive, self-directed learning. Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair. Is designed for a broad audience of learners from all backgrounds and disciplines. |
| api for data science: Graph Algorithms Mark Needham, Amy E. Hodler, 2019-05-16 Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark |
| api for data science: Mastering Social Media Mining with Python Marco Bonzanini, 2016-07-29 Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data Who This Book Is For This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data. What You Will Learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products In Detail Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. Style and approach This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks. |
| api for data science: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| api for data science: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| api for data science: Algorithms for Data Science Brian Steele, John Chandler, Swarna Reddy, 2016-12-25 This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners. |
| api for data science: Data Science Fundamentals and Practical Approaches Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma, 2020-09-03 Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development. |
| api for data science: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| api for data science: Data Science for Web3 Gabriela Castillo Areco, 2023-12-29 Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internet Key Features Build a deep understanding of the fundamentals of blockchain analytics Extract actionable business insights by modeling blockchain data Showcase your work and gain valuable experience to seize opportunities in the Web3 ecosystem Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience. |
| api for data science: DATA SCIENCE WITH R PROGRAMMING Dr.CARMEL MARY BELINDA.M.J, Dr.K.NATTAR KANNAN, Dr.R.GNANAJEYARAMAN, Dr.U.ARUL, Dr.M.RAMA MOORTHY, 2024-02-07 Dr.CARMEL MARY BELINDA.M.J, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.K.NATTAR KANNAN, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.R.GNANAJEYARAMAN, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha University, Chennai, India. Dr.U.ARUL, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.RAMA MOORTHY, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. |
| api for data science: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| api for data science: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2019-02-27 The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction. |
| api for data science: Data Science with R Programming Basics Dr.Sudhakar.K, Mrs.Geethanjali.S.G, Mrs.Rashmi.D.M, Mrs.Sinchana K.P, 2024-08-13 Dr.Sudhakar.K, Associate Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. Mrs.Geethanjali.S.G, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Rashmi.D.M, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Sinchana K.P, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. |
| api for data science: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science. |
| api for data science: Data Science at the Command Line Jeroen Janssens, 2021-08-17 This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 80 tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, and engineers; software and machine learning engineers; and system administrators. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTM, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create reusable command-line tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| api for data science: Scala for Data Science Pascal Bugnion, 2016-01-28 Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided Who This Book Is For If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions. What You Will Learn Transform and filter tabular data to extract features for machine learning Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines Read, transform, and write data to both SQL and NoSQL databases in a functional manner Write robust routines to query web APIs Read data from web APIs such as the GitHub or Twitter API Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive In Detail Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala's emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions. Style and approach A tutorial with complete examples, this book will give you the tools to start building useful data engineering and data science solutions straightaway |
| api for data science: MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING Dilip Siddareddy, Dr. Haewon Byeon, Purvi Makwana, Dr. Vaibhav Bhatnagar, 2023-10-30 Because of the advancements that have been made in machine learning, the world is being changed in ways that are difficult to conceive. If you stop for a second and take a good look around, you'll see that the area of data science is everywhere you turn. Take, for example, Alexa from Amazon; she is an artificial intelligence that has been developed to be as simple and straightforward to use as is humanly conceivable. There are many other digital assistants similar to Alexa, such as Google Assistant, Cortana, and so on. Alexa is not the only one of its sort. Therefore, the question of why they were formed in the first place is the most crucial one to ask; the question of how they developed is the second most important one to ask. In any event, we are going to make an attempt to study each and every one of these issues, and we are also going to make an effort to devise answers that are both logical and technological in nature. Within the scope of this discussion, the question that has to be inquired about first and foremost is, What exactly are Machine Learning and Data Science? A widespread misconception is that data science and machine learning are interchangeable terms for the same thing. Those people do have a point, to some extent, considering that data science is nothing more than taking a huge amount of data and analyzing it using a variety of machine learning approaches, methodologies, and technologies. Therefore, in order to become an expert in data science, you need to have a solid understanding of mathematics and statistics, in addition to a profound comprehension of the area that you intend to specialize in. To be more specific, what does it mean to have subject expertise? Subject expertise is nothing more than the knowledge necessary about a given topic in order to be able to abstract and calculate the data that pertains to that field, as the name of this type of expertise indicates. In a nutshell, these three concepts are considered as the foundations of data science, and if you are successful in mastering all of them, then you should rejoice yourself because you have achieved the level of an A-level data scientist. |
| api for data science: Java for Data Science Richard M. Reese, Jennifer L. Reese, 2017-01-10 Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly. |
| api for data science: Learn Data Science from Scratch Pratheerth Padman, 2024-02-15 Turn raw data into meaningful solutions KEY FEATURES ● Complete guide to master data science basics. ● Practical and hands-on examples in ML, deep learning, and NLP. ● Drive innovation and improve decision making through the power of data. DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making. By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment. WHAT YOU WILL LEARN ● Master key data science tools like Python, NumPy, Pandas, and more. ● Build a strong foundation in statistics and probability for data analysis. ● Learn and apply machine learning, from regression to deep learning. ● Expertise in NLP and recommender systems for advanced analytics. ● End-to-end data project from data collection to model deployment, with planning and execution. WHO THIS BOOK IS FOR This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts. TABLE OF CONTENTS 1. Unraveling the Data Science Universe: An Introduction 2. Essential Python Libraries and Tools for Data Science 3. Statistics and Probability Essentials for Data Science 4. Data Mining Expedition: Web Scraping and Data Collection Techniques 5. Painting with Data: Exploration and Visualization 6. Data Alchemy: Cleaning and Preprocessing Raw Data 7. Machine Learning Magic: An Introduction to Predictive Modeling 8. Exploring Regression: Linear, Logistic, and Advanced Methods 9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting 11. Support Vector Machines: Simplifying Complexity 12. Dimensionality Reduction: From PCA to Advanced Methods 13. Unlocking Unsupervised Learning 14. The Essence of Neural Networks and Deep Learning 15. Word Play: Text Analytics and Natural Language Processing 16. Crafting Recommender Systems 17. Data Storage Mastery: Databases and Efficient Data Management 18. Data Science in Action: A Comprehensive End-to-end Project |
| api for data science: Spark for Data Science Srinivas Duvvuri, Bikramaditya Singhal, 2016-09-30 Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ҂ig Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included. |
| api for data science: 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. |
| api for data science: Practical Data Science with SAP Greg Foss, Paul Modderman, 2019-09-18 Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life |
| api for data science: The Book of Why Judea Pearl, Dana Mackenzie, 2018-05-15 A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence Correlation is not causation. This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why. |
| api for data science: Scala: Guide for Data Science Professionals Pascal Bugnion, Arun Manivannan, Patrick R. Nicolas, 2017-02-24 Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. Some knowledge of statistics is expected. What You Will Learn Transfer and filter tabular data to extract features for machine learning Read, clean, transform, and write data to both SQL and NoSQL databases Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Load data from HDFS and HIVE with ease Run streaming and graph analytics in Spark for exploratory analysis Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Master probabilistic models for sequential data In Detail Scala is especially good for analyzing large sets of data as the scale of the task doesn't have any significant impact on performance. Scala's powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks. Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You'll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You'll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You'll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You'll also explore machine learning topics such as clustering, dimentionality reduction, Naive Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala Data Analysis Cookbook, Arun Manivannan Scala for Machine Learning, Patrick R. Nicolas Style and approach A complete package with all the information necessary to start building useful data engineering and data science solutions straight away. It contains a diverse set of recipes that cover the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala. |
| api for data science: Graph Data Science with Python and Neo4j Timothy Eastridge , 2024-03-11 Practical approaches to leveraging graph data science to solve real-world challenges. KEY FEATURES ● Explore the fundamentals of graph data science, its importance, and applications. ● Learn how to set up Python and Neo4j environments for graph data analysis. ● Discover techniques to visualize complex graph networks for better understanding. DESCRIPTION Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph data science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. You'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. This book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data. WHAT WILL YOU LEARN ● Set up and utilize Python and Neo4j environments effectively for graph analysis. ● Import and manipulate data within the Neo4j graph database using Cypher Query Language. ● Visualize complex graph networks to gain insights into data relationships and patterns. ● Enhance data analysis by integrating ChatGPT for context-rich data enrichment. ● Explore advanced topics including Neo4j vector indexing and Retrieval-Augmented Generation (RAG). ● Develop recommendation engines leveraging graph embeddings for personalized suggestions. ● Build and deploy recommendation systems and fraud detection models using graph techniques. ● Gain insights into the future trends and advancements shaping the field of graph data science. WHO IS THIS BOOK FOR? This book caters to a diverse audience interested in leveraging the power of graph data science using Python and Neo4j. It includes Data Science Professionals, Software Engineers, Academic Researchers, Business Analysts, and Technology Hobbyists. This comprehensive book equips readers from various backgrounds to effectively utilize graph data science in their respective fields. TABLE OF CONTENTS 1. Introduction to Graph Data Science 2. Getting Started with Python and Neo4j 3. Import Data into the Neo4j Graph Database 4. Cypher Query Language 5. Visualizing Graph Networks 6. Enriching Neo4j Data with ChatGPT 7. Neo4j Vector Index and Retrieval-Augmented Generation (RAG) 8. Graph Algorithms in Neo4j 9. Recommendation Engines Using Embeddings 10. Fraud Detection CLOSING SUMMARY The Future of Graph Data Science Index |
API - Wikipedia
An application programming interface (API) is a connection between computers or between computer programs. It is a type of …
What is an API (Application Programming Interface)
Apr 9, 2025 · Let's break down the concept of an API in simple terms. What is an API? An API is a set of rules that allow different …
What is an API? A Beginner's Guide to APIs - Postman
An API, which stands for application programming interface, is a set of protocols that enable different software components …
What is an API and How Does it Work? APIs for Beginners - freeCo…
Dec 5, 2022 · API stands for Application Programming Interface. The application can be any software that performs a specific …
What is an API? - Application Programming Interfaces Explaine…
API stands for Application Programming Interface. In the context of APIs, the word Application refers to any software with a …
API - Wikipedia
An application programming interface (API) is a connection between computers or between computer programs. It is a type of software interface, offering a service to other pieces of …
What is an API (Application Programming Interface)
Apr 9, 2025 · Let's break down the concept of an API in simple terms. What is an API? An API is a set of rules that allow different software applications to communicate with each other.
What is an API? A Beginner's Guide to APIs - Postman
An API, which stands for application programming interface, is a set of protocols that enable different software components to communicate and transfer data. Developers use APIs to bridge …
What is an API and How Does it Work? APIs for Beginners
Dec 5, 2022 · API stands for Application Programming Interface. The application can be any software that performs a specific task and the interface is a point where two applications …
What is an API? - Application Programming Interfaces Explained
API stands for Application Programming Interface. In the context of APIs, the word Application refers to any software with a distinct function. Interface can be thought of as a contract of …
API | Definition, Meaning, Examples, & Facts | Britannica
API, sets of standardized requests that allow different computer programs to communicate with each other. APIs establish the proper way for a developer to request services from a program. …
What is an API? How APIs work, simply explained | Contentful
Jan 15, 2025 · In this guide, you'll find everything you need to understand the fundamentals of APIs, how they enable seamless integration between systems, and why they are essential for modern …
What is an API? | API definition - Cloudflare
What is an application programming interface (API)? An application programming interface (API) is a set of rules that enables a software program to transmit data to another software program.
What Is an API (Application Programming Interface)? Definition …
When running on a server, an API is a set of coded routines that receives requests from and sends responses to other programs. API designers implement that code through standardized …
What Is an API? (+ How Do They Work?) - Coursera
Aug 13, 2024 · API stands for application programming interface. An API is a set of protocols and instructions written in programming languages such as C++ or JavaScript that determine how …
