Python Para Data Science Y Big Data Esencial

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Python Para Data Science Y Big Data Esencial

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Author :
language : es
Publisher:
Release Date : 2018

Python Para Data Science Y Big Data Esencial written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

Python está viviendo una segunda juventud como lenguaje de programación, ajustado codo con codo a una de las grandes necesidades de ésta década: el trabajo con grandes volúmenes de datos. En este curso, vas a aprender en primer lugar los fundamentos de Python para Data Science y visualización de datos, para posteriormente aplicarlos en el análisis de grandes volúmenes de datos usando el entorno Spark, una de las herramientas más populares del sector Big Data. El contenido del curso es práctico. Vamos a sentar las bases para que aprendas a desarrollar tu proyecto de datos, de principio a fin.

Python Data Science Essentials

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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-28

Python Data Science Essentials written by Alberto Boschetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-28 with Computers categories.

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of 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. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Python Data Science Essentials

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Author : MARK JOHN LADO
language : en
Publisher: Amazon Digital Services LLC - Kdp
Release Date : 2024-03-18

Python Data Science Essentials written by MARK JOHN LADO and has been published by Amazon Digital Services LLC - Kdp this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-18 with Computers categories.

The field of data science has emerged as a critical component in extracting actionable insights and making informed decisions from vast amounts of data. This comprehensive guide explores the fundamentals of data science using the Python language, a versatile toolset widely adopted in the industry. The journey begins with an introduction to data science, outlining its principles, methodologies, and real-world applications. Next, the basics of Python programming are covered, providing a solid foundation for data manipulation and analysis. Data types and structures in Python are then explored, followed by an in-depth look at essential libraries such as NumPy and Pandas, which facilitate efficient data handling and manipulation. The importance of data visualization is emphasized through tutorials on Matplotlib and Seaborn, enabling effective communication of insights and trends. Data cleaning and preprocessing techniques are discussed, addressing common challenges in data quality and preparation. Statistical analysis is introduced as a fundamental aspect of data science, showcasing its applications in hypothesis testing, correlation analysis, and regression modeling using Python. Machine learning concepts are then explored, covering both supervised and unsupervised learning algorithms, including linear regression, decision trees, clustering, and dimensionality reduction. Model evaluation and validation techniques are essential for assessing model performance and generalization ability, ensuring robust and reliable predictions. Additionally, an introduction to deep learning with Python provides insights into advanced neural network architectures and their applications in solving complex problems. Handling big data is a critical aspect of modern data science, and this guide provides an overview of using Python and Spark for scalable and distributed data processing. Real-world case studies across various domains illustrate the practical applications of data science techniques, from e-commerce recommendation systems to healthcare analytics. Finally, best practices and tips for data science projects are discussed, highlighting key considerations for project success, including data exploration, feature engineering, model selection, and collaboration. By mastering these fundamentals, aspiring data scientists can embark on their journey with confidence, equipped to tackle real-world challenges and drive impactful insights from data.

Data Science Essentials In Python

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Author : Dmitry Zinoviev
language : en
Publisher: The Pragmatic Programmers LLC
Release Date : 2016-08-10

Data Science Essentials In Python written by Dmitry Zinoviev and has been published by The Pragmatic Programmers LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-10 with Business & Economics categories.

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Python Data Science Essentials Tools Techniques And Applications

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Author : Dr.R.Kavitha
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-11-22

Python Data Science Essentials Tools Techniques And Applications written by Dr.R.Kavitha and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-22 with Language Arts & Disciplines categories.

Dr.R.Kavitha, Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Dr.S.Ponmaniraj, Professor, Department of Computational Intelligence, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. Mrs.D.Poovizhi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Ms.R.Vinodharasi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Mrs.C.Ramya, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India.

Essential Pyspark For Scalable Data Analytics

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Author : Sreeram Nudurupati
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29

Essential Pyspark For Scalable Data Analytics written by Sreeram Nudurupati and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.

Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.

Essential Data Analytics Data Science And Ai

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Author : Maxine Attobrah
language : en
Publisher: Springer Nature
Release Date : 2024-12-18

Essential Data Analytics Data Science And Ai written by Maxine Attobrah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Computers categories.

In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging. The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies. Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI. What you will learn: What are Synthetic data and Telemetry data How to analyze data using programming languages like Python and Tableau. What is feature engineering What are the practical Implications of Artificial Intelligence Who this book is for: Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.

45 Essential Concepts In Data Science In 7 Minutes Each

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Author : Nietsnie Trebla
language : en
Publisher: Shelf Indulgence
Release Date :

45 Essential Concepts In Data Science In 7 Minutes Each written by Nietsnie Trebla and has been published by Shelf Indulgence this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

### Book Description: '45 Essential Concepts in Data Science in 7 Minutes Each' Unlock the world of data science with *45 Essential Concepts in Data Science in 7 Minutes Each*, an engaging and insightful guide designed for professionals and enthusiasts alike. This book offers succinct overviews of key topics that shape the data science landscape, allowing readers to grasp fundamental concepts quickly and effectively. #### Why This Book? In today's fast-paced environment, understanding data science is essential, but time is often limited. Each chapter is crafted to be read in just seven minutes, making it the perfect resource for busy individuals seeking to enhance their knowledge without the overwhelm of lengthy texts. From foundational theories to cutting-edge techniques, this book covers a comprehensive range of topics that are pivotal for success in the data-driven world. #### What You'll Learn: - **Introduction to Data Science**: Discover the basics and the significance of data science in various industries. - **Data Types and Data Structures**: Understand how data is categorized and organized. - **Data Collection Methods**: Explore the different ways to gather data effectively. - **Data Cleaning and Preprocessing**: Learn the essential steps to prepare data for analysis. - **Exploratory Data Analysis (EDA)**: Uncover patterns and insights through initial investigations. - **Descriptive Statistics**: Get to know the tools for summarizing and describing data sets. - **Data Visualization Techniques**: Master the art of making data accessible and interpretable through visuals. - **Probability Basics & Statistical Inference**: Dive into the concepts that form the backbone of data analysis. - **Hypothesis Testing & Confidence Intervals**: Develop your skills in making data-driven decisions. - **Regression Analysis, Classification Techniques, and Clustering Methods**: Familiarize yourself with essential modeling techniques. - **Machine Learning Algorithms**: Gain insight into Decision Trees, Support Vector Machines, Neural Networks, and more. - **Natural Language Processing (NLP)**: Explore the intersection of language and data. - **Feature Engineering & Dimensionality Reduction**: Learn to enhance and streamline your datasets. - **Model Evaluation Metrics, Overfitting and Underfitting**: Understand how to measure the success of your models. - **Big Data Technologies & Distributed Computing**: Navigate the complexities of modern data environments. - **Data Ethics and Privacy**: Reflect on the moral responsibilities of data scientists in today's world. - **Deployment of Data Science Models & Data Pipelines**: Understand how to implement models in real-world settings. - **Cloud Computing in Data Science & Working with APIs**: Learn how the cloud empowers data science infrastructure. - **Data Governance & Interpretation**: Explore frameworks for maintaining data integrity and conveying results effectively. - **Industry Applications and Future Trends**: Gain insights into how data science is transforming various sectors. Each chapter concisely introduces you to the topic, breaking down complex concepts into digestible insights. Whether you are a student, a working professional, or just someone curious about the data science field, this book equips you with the essential knowledge to thrive in a data-centric world. ### Get ready to embark on your data journey and transform your understanding of data science in just seven minutes at a time!

Educational Data Science Essentials Approaches And Tendencies

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Author : Alejandro Peña-Ayala
language : en
Publisher: Springer Nature
Release Date : 2023-04-29

Educational Data Science Essentials Approaches And Tendencies written by Alejandro Peña-Ayala and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-29 with Computers categories.

This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well-–sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge aboutlearning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning! Chapter “Sync Ratio and Cluster Heat Map for Visualizing Student Engagement” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Python Programming Exam Essentials

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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :

Python Programming Exam Essentials written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Study Aids categories.

Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cuttign-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com