Download or read book PYTHON DATA SCIENCE From Beginner to Experts About Techniques of Data Mining Big Data Analytics and Science Python Programming and How to Use Them in Business written by Python School and published by Python School. This book was released on 2021-05-17 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★ 55% OFF for Bookstores! NOW at $26.95 instead of $39.95★ Have you ever been thought what it would be like if you dared to expand your python programming skills to include data science? Or are you looking for a new job in the technological and scientific world? Then keep reading because I have what you need! Working with machine learning is something that a lot of different companies want to focus on now. They like the idea of being able to get a system to learn while they are not there. They like to provide a better kind of customer service than they could have before. And they like all of the opportunities that are going to present themselves when it comes to this kind of programming. And when they can provide it all and learn how to do all the different parts with the help of Python, that can just make that much easier. This guidebook has explored a lot of the different topics that can come up with this. The purpose of the book is to help you to understand how to work with Python, what is all available with Python, and so much more. Some of the different topics we will discuss in this guidebook to help you to get started with coding in Python Data Science will include: - Techniques of Algorithmic programming - The Database Access with Python - What Can I Do with GUI Programming? - Recent Advancements in Data Analysis - Python Data Structures - Numba - Just in Time Python compiler - Comparing Pipeline Data Models: Is PODS Spatial the Right Solution? - Visualisation and Results - Most Common Data Science Problems: - Linear Classifiers - Setting Up PyCharm - Data frames - Why Python for Big Data? Are you wondering if that your PC can be an algorithms machine? Even if you have never heard that it's possibile, this book will deny it to you. If you want to know how, Scroll up and click the buy now button to get your copy.

Download or read book Python Data Science written by Austin Scratch and published by . This book was released on 2019-12-16 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Have you ever been thought what it would be like if you dared to expand your python programming skills to include data science? Or are you looking for a new job in the technological and scientific world? Then keep reading because I have what you need! Working with machine learning is something that a lot of different companies want to focus on now. They like the idea of being able to get a system to learn while they are not there. They like to provide a better kind of customer service than they could have before. And they like all of the opportunities that are going to present themselves when it comes to this kind of programming. And when they can provide it all and learn how to do all the different parts with the help of Python, that can just make that much easier. This guidebook has explored a lot of the different topics that can come up with this. The purpose of the book is to help you to understand how to work with Python, what is all available with Python, and so much more. Some of the different topics we will discuss in this guidebook to help you to get started with coding in Python Data Science will include: Techniques of Algorithmic programming The Database Access with Python What Can I Do with GUI Programming? Recent Advancements in Data Analysis Python Data Structures Numba - Just in Time Python compiler Comparing Pipeline Data Models: Is PODS Spatial the Right Solution? Visualization and Results Most Common Data Science Problems: Linear Classifiers Setting Up PyCharm Data frames Why Python for Big Data? Are you wondering if that your PC can be an algorithms machine? Even if you have never heard that it's possibile, this book will deny it to you. If you want to know how, scroll up and click the buy now button to get your copy.

Download or read book Data Science for Beginners written by Andrew Park and published by Independently Published. This book was released on 2020-01-26 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the world of Python, Data Analysis, Machine Learning and Data Science with this comprehensive 4-in-1 bundle. Do you want to learn more about the amazing world of Data Science? Or are you interested in becoming a Python geek? Then keep reading. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and Data Science, from basic code and concepts to complex Neural Networks and data manipulation. Inside, you'll discover everything you need to know to get started with Python and Data Science, and begin your journey to success! In book one, PYTHON FOR BEGINNERS, you will learn: How to install Python What are the different Python Data Types, Variables and Basic Operators Data Structures, Functions and Files Conditional and Loops in Python Object-Oriented Programming (OOP), Inheritance and Polymorphism Essential Programming Tools and Exception Handling An application to Decision Trees And Much More! In book two, PYTHON FOR DATA ANALYSIS, you will learn: What Data Analysis is all about and why businesses are investing in this sector The 5 steps of a Data Analysis Neural Network The 7 Python libraries that make Python one of the best choices for Data Analysis How Data Visualization and Matplotlib can help you to understand the data you are working with. Some of the main industries that are using data to improve their business with 14 real-world applications And Much More! In book three, PYTHON MACHINE LEARNING, you will learn: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Machine learning training models, Regression techniques and Linear Regression in Python How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python Artificial Neural Networks And Much More! And in book four, PYTHON DATA SCIENCE, you will learn: What Data Science is all about and why so many companies are using it to give them a competitive edge. Why Python and how to use it to implement Data Science The main Data Structures & Object-Oriented Programming, Functions and Modules in Python with practical codes and exercises The 7 most important algorithms and models in Data Science Data Aggregation, Group Operations, Databases and Data in the Cloud 9 important Data Mining techniques in Data Science And So Much More! Whether you're a complete beginner or a programmer looking to improve his skillset, Data Science for Beginners is your all-in-one solution to mastering the world of Python and Data Science. Would you like to know more?Scroll Up and Click on the BUY NOW Button to Get Your Copy!

Download or read book Data Science for Beginners written by Andrew Park and published by . This book was released on 2021-02-09 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★ 55% OFF for Bookstores! Now at $39.95 instead of $49.95! ★ Your Customers Will Never Stop To Use This Complete Guide! Did you know that according to Harvard Business Review the Data Scientist is the sexiest job of the 21st century? And for a reason! If "sexy" means having rare qualities that are much in demand, data scientists are already there. They are expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren't a lot of people with their combination of scientific background and computational and analytical skills. Data Science is all about transforming data into business value using math and algorithms. And needless to say, Python is the must-know programming language of the 21st century. If you are interested in coding and Data Science, then you must know Python to succeed in these industries! Data Science for Beginners is the perfect place to start learning everything you need to succeed. Contained within these four essential books are the methods, concepts, and important practical examples to help build your foundation for excelling at the discipline that is shaping the modern word. This bundle is perfect for programmers, software engineers, project managers and those who just want to keep up with technology. With these books in your hands, you will: ● Learn Python from scratch including the basic operations, how to install it, data structures and functions, and conditional loops ● Build upon the fundamentals with advanced techniques like Object-Oriented Programming (OOP), Inheritance, and Polymorphism ● Discover the importance of Data Science and how to use it in real-world situations ● Learn the 5 steps of Data Analysis so you can comprehend and analyze data sitting right in front of you ● Increase your income by learning a new, valuable skill that only a select handful of people take the time to learn ● Discover how companies can improve their business through practical examples and explanations ● And Much More! This bundle is essential for anyone who wants to study Data Science and learn how the world is moving to an open-source platform. Whether you are a software engineer or a project manager, jump to the next level by developing a data-driven approach and learning how to define a data-driven vision of your business! Order Your Copy of the Bundle and Let Your Customers Start Their New Career Path Today!

Download or read book Data Science Using Python and R written by Chantal D. Larose and published by John Wiley & Sons. This book was released on 2019-04-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Download or read book Learning Data Mining with Python written by Robert Layton and published by Packt Publishing Ltd. This book was released on 2017-04-27 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner.

Download or read book Data Science for Beginners written by Andrew Park and published by . This book was released on 2020-05-14 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the world of Python, Data Analysis, Machine Learning and Data Science with this comprehensive 4-in-1 bundle. Are you interested in becoming a Python geek? Or do you want to learn more about the fascinating world of Data Science, and what it can do for you? Then keep reading. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and Data Science, from basic code and concepts to complex Neural Networks and data manipulation. Inside, you'll discover everything you need to know to get started with Python and Data Science, and begin your journey to success! In book one, PYTHON FOR BEGINNERS, you'll learn: How to install Python What are the different Python Data Types, Variables and Basic Operators Data Structures, Functions and Files Conditional and Loops in Python Object-Oriented Programming (OOP), Inheritance and Polymorphism Essential Programming Tools and Exception Handling An application to Decision Trees And Much More! In book two, PYTHON FOR DATA ANALYSIS, you will: What Data Analysis is all about and why businesses are investing in this sector The 5 steps of a Data Analysis Neural Network The 7 Python libraries that make Python one of the best choices for Data Analysis How Data Visualization and Matplotlib can help you to understand the data you are working with. Some of the main industries that are using data to improve their business with 14 real-world applications And Much More! In book three, PYTHON MACHINE LEARNING, you'll discover: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Machine learning training models, Regression techniques and Linear Regression in Python How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python Artificial Neural Networks And Much More! And in book four, PYTHON DATA SCIENCE, you will: What Data Science is all about and why so many companies are using it to give them a competitive edge. Why Python and how to use it to implement Data Science The main Data Structures & Object-Oriented Programming, Functions and Modules in Python with practical codes and exercises The 7 most important algorithms and models in Data Science Data Aggregation, Group Operations, Databases and Data in the Cloud 9 important Data Mining techniques in Data Science And So Much More! Whether you're a complete beginner or a programmer looking to improve his skillset, Data Science for Beginners is your all-in-one solution to mastering the world of Python and Data Science. Would you like to know more?Scroll Up and Click the BUY NOW Button to Get Your Copy!

Download or read book Python for Data Analysis written by Oliver R Simpson and published by . This book was released on 2020-11-02 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a new business owner? Or an entrepreneur looking to catch up to the big companies in your industrial sector? If you want to be a data analysis expert, and looking to develop a sound understanding of all the nitty-gritty of this field, then this book is here to rescue you by simplifying and providing a working definition of "Big Data" and "Big Data Analytics." In addition to that, this book will also provide you a concise overview of the fundamentals of machine learning, the underlying challenges and limitations of engineering machines to 'think' using open source data analysis libraries built on Python such as "Scikit-Learn" and "Pandas", with example from open source data sets that you can easily access and get your hands dirty. Thanks to the smart and savvy customer of today, the competition to gain new customers while retaining existing customers is fierce. As a result, companies are increasingly relying upon cutting edge technologies such as big data analytics, data mining technology, machine learning, and artificial intelligence technology to gain an edge over the competition. Few of the many reasons why you should buy this book include: - Learn how our increasing online presence has led to the development of large volumes of data called "Big Data" and its significance in our modern lives. - Learn all about the historical development of the current explosion in this field of Big Data Analytics and how it differs from data visualization techniques. - Dig deep into the data mining process, the benefits of using data mining technology, the challenges facing the data mining technology, and learn about some data mining tools that you can leverage for your business. - Get familiar with the "Python" programming language with a detailed overview of a variety of Data Analysis libraries, including "Django," "Scikit-Learn," "NumPy," "Pandas," and "IPython" among others. - Deep dive into the concept of personalized marketing, predictive analytics, customer analytics, and exploratory data analysis presented with details on how you can make sense out of all your customer behavioral data. - Get a step-by-step walkthrough of how the "Scikit-Learn" platform can be used to create your own predictive data analysis model by processing Big Data to produce high-quality training and test data sets. - Learn how big data and big data analytics are being leveraged by businesses across the industrial spectrum, with a focus on the eCommerce, healthcare, and entertainment industry. This book is filled with real-life examples to help you understand the nitty-gritty of all the concepts as well as names and descriptions of multiple tools that you can further explore and selectively implement in your business to reap the benefits of these cutting-edge technologies. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. So, be a Good Samaritan and spread the word to your friends and family, help them get access to this power! If this is the book you need to understand and master the fundamentals and importance of big data science technologies to kick start your business or take it to the next level, Scroll Up and Click the Buy Now Button.

Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2019-11-05 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Download or read book Data Science for Beginners written by Andrew Park and published by . This book was released on 2021-02-09 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★ 55% OFF for Bookstores! Now at $49.95 instead of $59.95! ★ Your Customers Will Never Stop To Use This Complete Guide! Did you know that according to Harvard Business Review the Data Scientist is the sexiest job of the 21st century? And for a reason! If "sexy" means having rare qualities that are much in demand, data scientists are already there. They are expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren't a lot of people with their combination of scientific background and computational and analytical skills. Data Science is all about transforming data into business value using math and algorithms. And needless to say, Python is the must-know programming language of the 21st century. If you are interested in coding and Data Science, then you must know Python to succeed in these industries! Data Science for Beginners is the perfect place to start learning everything you need to succeed. Contained within these four essential books are the methods, concepts, and important practical examples to help build your foundation for excelling at the discipline that is shaping the modern word. This bundle is perfect for programmers, software engineers, project managers and those who just want to keep up with technology. With these books in your hands, you will: ● Learn Python from scratch including the basic operations, how to install it, data structures and functions, and conditional loops ● Build upon the fundamentals with advanced techniques like Object-Oriented Programming (OOP), Inheritance, and Polymorphism ● Discover the importance of Data Science and how to use it in real-world situations ● Learn the 5 steps of Data Analysis so you can comprehend and analyze data sitting right in front of you ● Increase your income by learning a new, valuable skill that only a select handful of people take the time to learn ● Discover how companies can improve their business through practical examples and explanations ● And Much More! This bundle is essential for anyone who wants to study Data Science and learn how the world is moving to an open-source platform. Whether you are a software engineer or a project manager, jump to the next level by developing a data-driven approach and learning how to define a data-driven vision of your business! Order Your Copy of the Bundle and Let Your Customers Start Their New Career Path Today!

Download or read book Python Data Mining Quick Start Guide written by Nathan Greeneltch and published by Packt Publishing Ltd. This book was released on 2019-04-25 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.

Download or read book Mastering Python for Data Science written by Samir Madhavan and published by Packt Publishing Ltd. This book was released on 2015-08-31 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Download or read book Python Data Analysis Cookbook written by Ivan Idris and published by Packt Publishing Ltd. This book was released on 2016-07-22 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.

Download or read book Python for Data Analysis written by Oliver SIMPSON and published by . This book was released on 2019-10-24 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a new business owner? Or an entrepreneur looking to catch up to the big companies in your industrial sector? If you want to be a data analysis expert, and looking to develop a sound understanding of all the nitty-gritty of this field, then this book is here to rescue you by simplifying and providing a working definition of "Big Data" and "Big Data Analytics." In addition to that, this book will also provide you a concise overview of the fundamentals of machine learning, the underlying challenges and limitations of engineering machines to 'think' using open source data analysis libraries built on Python such as "Scikit-Learn" and "Pandas", with example from open source data sets that you can easily access and get your hands dirty. Thanks to the smart and savvy customer of today, the competition to gain new customers while retaining existing customers is fierce. As a result, companies are increasingly relying upon cutting edge technologies such as big data analytics, data mining technology, machine learning, and artificial intelligence technology to gain an edge over the competition. Few of the many reasons why you should buy this book include: * Learn how our increasing online presence has led to the development of large volumes of data called "Big Data" and its significance in our modern lives. * Learn all about the historical development of the current explosion in this field of Big Data Analytics and how it differs from data visualization techniques. * Dig deep into the data mining process, the benefits of using data mining technology, the challenges facing the data mining technology, and learn about some data mining tools that you can leverage for your business. * Get familiar with the "Python" programming language with a detailed overview of a variety of Data Analysis libraries, including "Django," "Scikit-Learn," "NumPy," "Pandas," and "IPython" among others. * Deep dive into the concept of personalized marketing, predictive analytics, customer analytics, and exploratory data analysis presented with details on how you can make sense out of all your customer behavioral data. * Get a step-by-step walkthrough of how the "Scikit-Learn" platform can be used to create your own predictive data analysis model by processing Big Data to produce high-quality training and test data sets. * Learn how big data and big data analytics are being leveraged by businesses across the industrial spectrum, with a focus on the eCommerce, healthcare, and entertainment industry. This book is filled with real-life examples to help you understand the nitty-gritty of all the concepts as well as names and descriptions of multiple tools that you can further explore and selectively implement in your business to reap the benefits of these cutting-edge technologies. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. So, be a Good Samaritan and spread the word to your friends and family, help them get access to this power! If this is the book you need to understand and master the fundamentals and importance of big data science technologies to kick start your business or take it to the next level, Scroll Up and Click the Buy Now Button

Download or read book Python for Data Science For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2015-07-07 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

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