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Search Results for 'Data Science' (432 products)

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Cracking the Data Science Interview
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Cracking the Data Science Interview
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Feb 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (6)
Paperback 404 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (6)
Unlock insider tips from industry experts to master the data science field
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  • 9781805120193 eBook $15.99
  • 9781805120506 Paperback $29.99
$15.99 $23.99
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Cracking the Data Science Interview
Unlock insider tips from industry experts to master the data science field
Description
The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.
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Feb 2024 404 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Leondra R. Gonzalez
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Streamlit for Data Science
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Streamlit for Data Science
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Sep 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (32)
Paperback 300 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (32)
Create interactive data apps in Python
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  • 9781803232959 eBook $29.99
  • 9781803248226 Paperback $54.99
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Streamlit for Data Science
Create interactive data apps in Python
Description
If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.
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Sep 2023
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
300 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Tyler Richards
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Data Science for Marketing Analytics
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Data Science for Marketing Analytics
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Sep 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (203)
Paperback 636 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (203)
A practical guide to forming a killer marketing strategy through data analysis with Python
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  • 9781800563889 eBook $20.98
  • 9781800560475 Paperback $38.99
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Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python
Description
Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
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Sep 2021 636 pages
AI Assistant AI Assistant
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Authors
Author Mirza Rahim Baig
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Applied Geospatial Data Science with Python
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Applied Geospatial Data Science with Python
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Feb 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (9)
Paperback 308 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (9)
Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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  • 9781803240343 eBook $29.99
  • 9781803238128 Paperback $54.99
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Applied Geospatial Data Science with Python
Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
Description
Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
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Feb 2023 308 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author David S. Jordan
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Building Data Science Applications with FastAPI
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Building Data Science Applications with FastAPI
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Jul 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (9)
Paperback 422 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (9)
Develop, manage, and deploy efficient machine learning applications with Python
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  • 9781837637263 eBook $27.98
  • 9781837632749 Paperback $49.99
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Building Data Science Applications with FastAPI
Develop, manage, and deploy efficient machine learning applications with Python
Description
Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.
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Jul 2023 422 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author François Voron
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eBook $27.98 $39.99
Paperback $49.99
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Principles of Data Science
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Principles of Data Science
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Jan 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4)
Paperback 326 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4)
A beginner's guide to essential math and coding skills for data fluency and machine learning
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  • 9781837636006 eBook $21.99
  • 9781837636303 Paperback $39.99
$21.99 $31.99
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Principles of Data Science
A beginner's guide to essential math and coding skills for data fluency and machine learning
Description
Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.
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Jan 2024 326 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Sinan Ozdemir
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Graph Data Science with Neo4j
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Graph Data Science with Neo4j
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Jan 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (6)
Paperback 288 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (6)
Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project
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  • 9781804614907 eBook $25.99
  • 9781804612743 Paperback $46.99
$25.99 $37.99
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Graph Data Science with Neo4j
Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project
Description
Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
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Jan 2023 288 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Scifo
Author Estelle Scifo
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eBook $25.99 $37.99
Paperback $46.99
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Practical Data Science with Python
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Practical Data Science with Python
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Sep 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (19)
Paperback 620 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (19)
Learn tools and techniques from hands-on examples to extract insights from data
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  • 9781801076654 eBook $29.99
  • 9781801071970 Paperback $54.99
$29.99 $43.99
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Practical Data Science with Python
Learn tools and techniques from hands-on examples to extract insights from data
Description
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
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Sep 2021 620 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Nathan George
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Data Science with .NET and Polyglot Notebooks
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Data Science with .NET and Polyglot Notebooks
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Aug 2024
Paperback 404 pages
Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel
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  • 9781835882979 eBook $26.99
  • 9781835882962 Paperback $47.99
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Data Science with .NET and Polyglot Notebooks
Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel
Description
As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.
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Aug 2024 404 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Matt Eland
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eBook $26.99 $38.99
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Hands-On Data Science for Marketing
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Hands-On Data Science for Marketing
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Mar 2019
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (14)
Paperback 464 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (14)
Improve your marketing strategies with machine learning using Python and R
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  • 9781789348828 eBook $24.99
  • 9781789346343 Paperback $48.99
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Hands-On Data Science for Marketing
Improve your marketing strategies with machine learning using Python and R
Description
Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business.
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Mar 2019 464 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Yoon Hyup Hwang
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Paperback $48.99
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Data Science for Web3
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Data Science for Web3
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Dec 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3)
Paperback 344 pages
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3)
A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases
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  • 9781837635580 eBook $24.99
  • 9781837637546 Paperback $44.99
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Data Science for Web3
A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases
Description
Data 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.
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Dec 2023 344 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Gabriela Castillo Areco
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Hands-On Data Science with R
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Hands-On Data Science with R
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Nov 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1)
Paperback 420 pages
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1)
Techniques to perform data manipulation and mining to build smart analytical models using R
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  • 9781789135831 eBook $24.99
  • 9781789139402 Paperback $43.99
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Hands-On Data Science with R
Techniques to perform data manipulation and mining to build smart analytical models using R
Description
R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
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Nov 2018 420 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Dasgupta
Author Bianchi Lanzetta
Author Doug Ortiz
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Paperback $43.99
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Jupyter Notebook for Data Science
Jupyter Notebook for Data Science
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Aug 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
Video 3hrs 11mins
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
Collaborate, create interactive visualizations, and manipulate big data in the language of your choice.
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Video Video
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Jupyter Notebook for Data Science
Collaborate, create interactive visualizations, and manipulate big data in the language of your choice.
Description
This video course will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start from basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data. By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Jupyter-Notebook-for-Data-Science This course uses Jupyter 5.4.1, while not the latest version available, it provides relevant and informative content for data science enthusiasts.
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3hrs 11mins
Published : Aug 2018
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Drazen Lucanin
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Data Science Projects with Python
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Data Science Projects with Python
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Jul 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (60)
Paperback 432 pages
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (60)
A case study approach to gaining valuable insights from real data with machine learning
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  • 9781800569447 eBook $17.99
  • 9781800564480 Paperback $38.99
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Data Science Projects with Python
A case study approach to gaining valuable insights from real data with machine learning
Description
If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects. You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
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Jul 2021 432 pages
AI Assistant AI Assistant
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Author Stephen Klosterman
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Managing Data Science
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Managing Data Science
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Nov 2019
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Paperback 290 pages
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Effective strategies to manage data science projects and build a sustainable team
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Managing Data Science
Effective strategies to manage data science projects and build a sustainable team
Description
Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.
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Nov 2019 290 pages
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Author Dubovikov
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Reproducible Data Science with Pachyderm
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Reproducible Data Science with Pachyderm
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Mar 2022
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Paperback 364 pages
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Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Reproducible Data Science with Pachyderm
Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale. You’ll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you’ll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You’ll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you’ll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks. By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis.
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Mar 2022 364 pages
AI Assistant AI Assistant
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Author Svetlana Karslioglu
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Cleaning Data for Effective Data Science
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Cleaning Data for Effective Data Science
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Mar 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (18)
Paperback 498 pages
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Doing the other 80% of the work with Python, R, and command-line tools
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Cleaning Data for Effective Data Science
Doing the other 80% of the work with Python, R, and command-line tools
Description
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.
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Mar 2021 498 pages
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Author David Mertz
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Beginning Data Science with Python and Jupyter
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Beginning Data Science with Python and Jupyter
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Jun 2018
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Paperback 194 pages
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Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data
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Beginning Data Science with Python and Jupyter
Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data
Description
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
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Jun 2018 194 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Galea
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Python Data Science Essentials
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Python Data Science Essentials
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Sep 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2)
Paperback 472 pages
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A practitioner's guide covering essential data science principles, tools, and techniques
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  • 9781789531893 eBook $27.98
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Python Data Science Essentials
A practitioner's guide covering essential data science principles, tools, and techniques
Description
Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
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Sep 2018 472 pages
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Author Alberto Boschetti
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Hands-On Data Science and Python Machine Learning
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Jul 2017
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8 (4)
Paperback 420 pages
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Perform data mining and machine learning efficiently using Python and Spark
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  • 9781787280229 eBook $24.99
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Hands-On Data Science and Python Machine Learning
Perform data mining and machine learning efficiently using Python and Spark
Description
Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
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Jul 2017 420 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Frank Kane
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Simulation for Data Science with R
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Simulation for Data Science with R
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Jun 2016
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (5)
Paperback 398 pages
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Effective Data-driven Decision Making
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  • 9781785885877 eBook $29.99
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Simulation for Data Science with R
Effective Data-driven Decision Making
Description
Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
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Jun 2016 398 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Matthias Templ
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Data Science Algorithms in a Week
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Data Science Algorithms in a Week
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Oct 2018
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3)
Paperback 214 pages
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Top 7 algorithms for scientific computing, data analysis, and machine learning
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  • 9781789800968 eBook $24.99
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Data Science Algorithms in a Week
Top 7 algorithms for scientific computing, data analysis, and machine learning
Description
Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
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Oct 2018 214 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author David Toth
Author Natingga
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Statistics for Data Science
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Statistics for Data Science
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Nov 2017
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (5)
Paperback 286 pages
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Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
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Statistics for Data Science
Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
Description
Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
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Nov 2017 286 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author James D. Miller
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Building Data Science Applications with FastAPI
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Building Data Science Applications with FastAPI
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Oct 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (16)
Paperback 426 pages
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Develop, manage, and deploy efficient machine learning applications with Python
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  • 9781801074186 eBook $29.99
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Building Data Science Applications with FastAPI
Develop, manage, and deploy efficient machine learning applications with Python
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.
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Oct 2021 426 pages
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Author Voron
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Advanced Statistics and Data Mining for Data Science
Advanced Statistics and Data Mining for Data Science
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Feb 2018
Video 2hrs 53mins
Dive deep into Statistical and Data Mining Techniques
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Advanced Statistics and Data Mining for Data Science
Dive deep into Statistical and Data Mining Techniques
Description
Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques. The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis. Towards the end of the course, you will work with association modeling, which will allow you to perform market basket analysis. This course uses SPSS v25, while not the latest version available, it provides relevant and informative content for legacy users of SPSS. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Advanced-Statistics-and-Data-Mining-for-Data-Science
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2hrs 53mins
Published : Feb 2018
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Author Jesus Salcedo
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Getting Started with Streamlit for Data Science
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Getting Started with Streamlit for Data Science
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Aug 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (21)
Paperback 282 pages
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Create and deploy Streamlit web applications from scratch in Python
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  • 9781800563209 eBook $38.99
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Getting Started with Streamlit for Data Science
Create and deploy Streamlit web applications from scratch in Python
Description
Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
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Aug 2021 282 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Tyler Richards
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The Applied Data Science Workshop
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The Applied Data Science Workshop
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Jul 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
Paperback 352 pages
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Get started with the applications of data science and techniques to explore and assess data effectively
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  • 9781800207004 eBook $17.99
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The Applied Data Science Workshop
Get started with the applications of data science and techniques to explore and assess data effectively
Description
From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects.
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Jul 2020 352 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Galea
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Learn Python by Building Data Science Applications
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Learn Python by Building Data Science Applications
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Aug 2019
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.8 (4)
Paperback 482 pages
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A fun, project-based guide to learning Python 3 while building real-world apps
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  • 9781789533064 eBook $17.99
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Learn Python by Building Data Science Applications
A fun, project-based guide to learning Python 3 while building real-world apps
Description
Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
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Aug 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
482 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Kats
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Clojure for Data Science
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Clojure for Data Science
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Sep 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (4)
Paperback 608 pages
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Statistics, big data, and machine learning for Clojure programmers
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  • 9781784397500 eBook $29.99
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Clojure for Data Science
Statistics, big data, and machine learning for Clojure programmers
Description
The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
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Sep 2015 608 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Garner
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Java for Data Science
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Java for Data Science
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Jan 2017
Paperback 386 pages
Examine the techniques and Java tools supporting the growing field of data science
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  • 9781785281242 eBook $29.99
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Java for Data Science
Examine the techniques and Java tools supporting the growing field of data science
Description
para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
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Jan 2017 386 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Richard M. Reese
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Applied Data Science with Python and Jupyter
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Applied Data Science with Python and Jupyter
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Oct 2018
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (3)
Paperback 192 pages
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Use powerful industry-standard tools to unlock new, actionable insights from your data
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  • 9781789951929 eBook $17.99
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Applied Data Science with Python and Jupyter
Use powerful industry-standard tools to unlock new, actionable insights from your data
Description
Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
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Oct 2018 192 pages
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Authors
Author Galea
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R for Data Science
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R for Data Science
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Dec 2014
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (5)
Paperback 364 pages
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Learn and explore the fundamentals of data science with R
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  • 9781784392659 eBook $22.99
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R for Data Science
Learn and explore the fundamentals of data science with R
Description
If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
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Dec 2014 364 pages
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Authors
Author Toomey
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R for Data Science Cookbook (n)
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R for Data Science Cookbook (n)
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Jul 2016
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Paperback 452 pages
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Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
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  • 9781784392048 eBook $27.98
  • 9781784390815 Paperback $48.99
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R for Data Science Cookbook (n)
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
Description
This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
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Jul 2016 452 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Yu-Wei, Chiu (David Chiu)
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Julia for Data Science
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Julia for Data Science
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Sep 2016
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (6)
Paperback 346 pages
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high-performance computing simplified
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  • 9781783553860 eBook $29.99
  • 9781785289699 Paperback $54.99
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Julia for Data Science
high-performance computing simplified
Description
Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
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Sep 2016 346 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Anshul Joshi
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Mastering Java for Data Science
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Mastering Java for Data Science
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Apr 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
Paperback 364 pages
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Analytics and more for production-ready applications
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  • 9781785887390 eBook $29.99
  • 9781782174271 Paperback $54.99
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Mastering Java for Data Science
Analytics and more for production-ready applications
Description
Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
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Apr 2017 364 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Alexey Grigorev
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Comet for Data Science
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
AI Assistant
Comet for Data Science
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Aug 2022
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Paperback 402 pages
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Enhance your ability to manage and optimize the life cycle of your data science project
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  • 9781801814355 eBook $25.99
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Comet for Data Science
Enhance your ability to manage and optimize the life cycle of your data science project
Description
This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You’ll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet.
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Aug 2022 402 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Angelica Lo Duca
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