Data Science Workflow For Beginners

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Data Science Workflow For Beginners

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Author : Alejandro Garcia
language : en
Publisher: Alejandro Garcia
Release Date :

Data Science Workflow For Beginners written by Alejandro Garcia and has been published by Alejandro Garcia this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

This book brings to you a simple yet effective 40 to 60 mins introduction that will clear all your doubts about Data Sience and will answer some important questions like: What is data Science ? The book explores all the initial concepts a person might want to know about the data science workflow. There’s not coding, math or statistics required to successfully understand the goals and end results of this process. This book takes you on an exclusive tour of datasets and sites to download your first datasets. Then jumps into a comprehensive and easy-to-follow data science process letting you go through 3 data visualization projects. (Python Code Understanding is Recommended for the Data Visualization projects) - 40 to 60 mins reading time. - 3 Data Visualization projects. - 10 Datasets sources. - 26 Quality datasets for your first visualizations. - Get the code and reuse in your own projects. The ebook covers: - Intro to Data Science. - The Workflow of Data Science. - Data Science and Machine Learning. - Datasets to start right away. - Data Visualization Projects. (Python Code Understanding Recommended)

R For Data Science

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12

R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.

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

The Beginner S Guide To Data Science

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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2024-03-27

The Beginner S Guide To Data Science written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-27 with Education categories.

In today’s data-driven world, businesses and industries constantly seek insights to drive innovation, enhance decision-making, and stay ahead of the curve. Data science is not just a skill but a superpower that empowers you to extract meaningful patterns and knowledge from raw data, unlocking limitless opportunities. The theme of data science is to tell a story from data. There are many tools to help you build a narrative, but you should be focused on something other than the tool since the end is more important than the means. If you are a beginner, how should you embark on data science? You can learn many models, read many examples, and eventually gain the right mindset to handle a data science project. You can also learn the data science mindset first and then learn models that fit the picture when needed. The Beginner’s Guide to Data Science is your gateway to learn the data science mindset from examples. This ebook is written in the engaging and approachable style you are familiar with from Machine Learning Mastery. Discover exactly how to start and what the thought process is in dealing with a data science project.

Confident Data Skills

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Author : Kirill Eremenko
language : en
Publisher:
Release Date : 2021-02-03

Confident Data Skills written by Kirill Eremenko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-03 with categories.

Understand the basics of data and learn to utilise its innovative potential, giving your career a valuable and cutting-edge boost.

Machine Learning In Production

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Author : Andrew Kelleher
language : en
Publisher: Addison-Wesley Professional
Release Date : 2019-02-27

Machine Learning In Production written by Andrew Kelleher and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-27 with Computers categories.

Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Beginning Data Science In R

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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2017-03-09

Beginning Data Science In R written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-09 with Computers categories.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Data Science For Dummies

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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-20

Data Science For Dummies written by Lillian Pierson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-20 with Computers categories.

Discover how data science can help you gain in-depth insight into your business – the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer covering all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad aspects of the topic, including the sometimes intimidating field of big data and data science, it is not an instructional manual for hands-on implementation. Here’s what to expect in Data Science for Dummies: Provides a background in big data and data engineering before moving on to data science and how it’s applied to generate value. Includes coverage of big data frameworks and applications like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL. Explains machine learning and many of its algorithms, as well as artificial intelligence and the evolution of the Internet of Things. Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate. It’s a big, big data world out there – let Data Science For Dummies help you get started harnessing its power so you can gain a competitive edge for your organization.

Metaflow For Data Science Workflows

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-13

Metaflow For Data Science Workflows written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-13 with Computers categories.

"Metaflow for Data Science Workflows" "Metaflow for Data Science Workflows" is an authoritative guide to building, managing, and scaling modern data science workflows using the Metaflow framework. This comprehensive book opens with a critical analysis of the evolution of data science pipelines, examining the challenges of reproducibility, scalability, and complexity that confront today’s practitioners. Readers are introduced to the transformative potential of orchestration tools within MLOps and DataOps, placing Metaflow in context through in-depth comparisons with Airflow and Kubeflow, while establishing a strong foundation in core concepts such as Flows, Steps, Artifacts, and the Directed Acyclic Graph (DAG) paradigm. Spanning Metaflow’s robust architecture and its integration with cloud and enterprise environments, the book delves into technical mechanisms essential for workflow composition, dynamic branching, parallel execution, and advanced artifact management. It empowers readers to develop resilient, production-ready data pipelines through best practices in parameterization, modular step design, error handling, and collaboration. Extensive attention is given to scalable deployment strategies—from local testing to distributed cloud execution on AWS, Kubernetes, and serverless platforms—and to maintaining fault tolerance, cost efficiency, and regulatory compliance at enterprise scale. The discussion extends beyond theory with practical guidance on experiment management, CI/CD integration, and operational monitoring, ensuring reproducibility and traceability through versioning, tagging, and comprehensive audit trails. Real-world case studies, patterns for hybrid and multi-cloud orchestration, and insights into emerging trends position this book as an indispensable resource for data scientists, engineers, and technical leaders seeking to implement robust and future-proof data science workflows with Metaflow.

Python Data Science Handbook

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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21

Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.

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

Data Science On Aws

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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07

Data Science On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.

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 up your 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