Data Science Essentials In Python
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Data Science Essentials In Python
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Author : Dmitry Zinoviev
language : en
Publisher:
Release Date : 2016
Data Science Essentials In Python written by Dmitry Zinoviev and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
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 : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-28
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 2018-09-28 with Computers categories.
Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook 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 What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
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.
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.
Data Science Essentials For Dummies
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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2024-12-24
Data Science Essentials 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 2024-12-24 with Computers categories.
Feel confident navigating the fundamentals of data science Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. Strengthen your understanding of data science basics Review what you've already learned or pick up key skills Effectively work with data and provide accessible materials to others Jog your memory on the essentials as you work and get clear answers to your questions Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: O'Reilly Media, Inc.
Release Date : 2022-12-06
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 2022-12-06 with Computers categories.
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Data Science Essentials For Dummies
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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-13
Data Science Essentials 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 2024-11-13 with Computers categories.
Feel confident navigating the fundamentals of data science Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. Strengthen your understanding of data science basics Review what you've already learned or pick up key skills Effectively work with data and provide accessible materials to others Jog your memory on the essentials as you work and get clear answers to your questions Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.
Data Science Fundamentals Part 1
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Author : Jonathan Dinu
language : en
Publisher:
Release Date : 2017
Data Science Fundamentals Part 1 written by Jonathan Dinu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air...
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.