Data Analytics Using Python
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Data Analytics With Python
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Author : Frank Millstein
language : en
Publisher: Frank Millstein
Release Date : 2020-05-08
Data Analytics With Python written by Frank Millstein and has been published by Frank Millstein this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-08 with Computers categories.
Data Analytics With Python Data is the foundation of this digital age that we live in. With this book, you are going to learn how to organize and analyze data and how to interpret vast sources of information. This book covers various topics on data analytics such as data analytics applications, data analytics process, using Python for data analytics, Python libraries for data analytics and many other that will help you kick-start your data analytics journey from the very beginning. In this book you are going to learn how to use Python its tools in order to interpret data and examine those interesting data trends and information, which are important in predicting the future. Whether you are dealing with some medical data, sales data, web page data, you can use Python in order to interpret data, analyze it and obtain this valuable information. You can also use this data for creating data analytics models and predictions. Here Is A Brief Preview of What You’ll Learn In This Book… -Data analytics applications -Data analytics process -How to install and run Python -Python data structures and Python libraries -Python conditional construct and iteration -Data exploration using Pandas -Pandas series and dataframes -Data munging and distribution analysis -Carrying out binary operations -Data manipulation and categorical variable analysis -How to build a predictive model -And of course much, much more! Get this book NOW and learn more about Data Analytics With Python!
Data Analytics Using Python
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Author : Mahmoud Ahmad Al-Khasawneh
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-08-07
Data Analytics Using Python written by Mahmoud Ahmad Al-Khasawneh and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Computers categories.
Descriptive statistics are used to identify the fundamental characteristics of data in a research study. Simply summarized information about the sample and measurements is provided. Descriptive statistics provide information about the components and dissemination of values in single or multiple data set concisely. The classical illustrative statistics allow experts to get a quick sense of the central inclination and degree of diffusion of values in a dataset with a single glance. They are useful in gaining an understanding of data distribution as well as in comparing different data distributions, It is frequently necessary for human geographers to take into consideration the locational citations of the data they are working with. Using spatial descriptive statistics, analysts can determine the central propensity and variation of data in a given geographic area or region. The two types of illustrative analysis are mutually supportive of one another. Experts can research the geographic phenomena with which they are involved by combining both statistics and mathematics. Even though descriptive statistics are straightforward concepts in statistical assessment, they are essential and beneficial in today's world of massive amounts of data. The performance and efficacy of descriptive analysis should not be overshadowed in the face of ever-increasing huge quantities of data being generated continuously and distributed via the Internet. Descriptive statistics are characterized by inferential analysis in most cases. When you use descriptive statistics, you are merely explaining what is or what the information reveals about something. When using inferential analysis, you are attempting to draw conclusions that are not based solely on the available data. For example, we use inferential analysis to try to infer what the general public might think based on a sample of data. Alternatively, we use inferential analysis to make decisions about the likelihood that a difference between groups observed in this study is a dependable difference or one that could have occurred by chance. As a result, we use inferential analysis to conclude more general conditions from our data, whereas we use descriptive analysis to simply describe what is happening in our data. Descriptive statistics are used to present quantitative explanations in a manageable format. In a research study, we may have a large number of measures. Alternatively, we can quantify a huge number of participants using any measure.
Python Data Analysis
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Author : Avinash Navlani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-05
Python Data Analysis written by Avinash Navlani 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 2021-02-05 with Computers categories.
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
Python Data Analytics
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Author : Stephen Ward
language : en
Publisher:
Release Date : 2020-10-15
Python Data Analytics written by Stephen Ward and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with categories.
Unlock the programming skills you need to prepare for a lucrative career in Data Science with this comprehensive introduction to Python programming for data analytics! Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics? If you answered yes to any of the questions above, then keep reading... Data analysis has become a huge industry with tons of career potential and will remain relevant far into the foreseeable future. With the exponential growth and explosion of new data and the focus on using data to improve customer experiences and carry out research, data analysts will be needed to process and make sense of large amounts of information, with Python being the language of choice because of its versatility. In this guide, you're going to be shown everything you need to break into the world of Data Analysis with Python. Filled with tutorials for powerful libraries and practical, hands-on exercises, you're going to learn how to aggregate, munge, analyze and visualize data in Python. Here's a sample of what you're going to discover in Python Data Analytics Why Python is the perfect language to learn if you want to break into Big Data and data analytics Core statistical models and computation methods you need to know about as a budding data analyst How to master the CSV library for reading, writing and handling tabular data Using the Xlrd library to extract data from Microsoft Excel files How to convert text to speech using the powerful Win32.com library How to use the NumPy library to carry out fundamental and basic scientific and technical computing How to use the SciPy library to carry out advanced scientific and highly technical computing Surefire ways to manipulate the easy-to-use data structures of the Pandas framework for high-performance data analysis How to plot complex data, create figures and visualize data using the Python Matplotlib library ...and tons more! If you're completely new to programming and have never written a single line of code, but want to get started, this guide is perfect for as a crash guide to getting up to speed with programming in general. Whether you're a programmer looking to switch into an exciting new field with lots of potential for the future, or a regular data analyst looking to acquire the skills needed to remain relevant in a fast-changing world, this guide will teach you how to master powerful libraries used in the real-world by experienced data scientists.
Data Science Using Python And R
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Author : Chantal D. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2019-03-21
Data Science Using Python And R written by Chantal D. Larose 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 2019-03-21 with Computers categories.
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.
Iot Data Analytics Using Python
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Author : M S Hariharan
language : en
Publisher: BPB Publications
Release Date : 2023-10-23
Iot Data Analytics Using Python written by M S Hariharan and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-23 with Computers categories.
Harness the power of Python to analyze your IoT data KEY FEATURES ● Learn how to build an IoT Data Analytics infrastructure. ● Explore advanced techniques for IoT Data Analysis with Python. ● Gain hands-on experience applying IoT Data Analytics to real-world situations. DESCRIPTION Python is a popular programming language for data analytics, and it is also well-suited for IoT Data Analytics. By leveraging Python's versatility and its rich ecosystem of libraries and tools, Data Analytics for IoT can unlock valuable insights, enable predictive capabilities, and optimize decision-making in various IoT applications and domains. The book begins with a foundation in IoT fundamentals, its role in digital transformation, and why Python is the preferred language for IoT Data Analytics. It then covers essential data analytics concepts, how to establish an IoT Data Analytics environment, and how to design and manage real-time IoT data flows. Next, the book discusses how to implement Descriptive Analytics with Pandas, Time Series Forecasting with Python libraries, and Monitoring, Preventive Maintenance, Optimization, Text Mining, and Automation strategies. It also introduces Edge Computing and Analytics, discusses Continuous and Adaptive Learning concepts, and explores data flow and use cases for Edge Analytics. Finally, the book concludes with a chapter on IoT Data Analytics for self-driving cars, using the CRISP-DM framework for data collection, modeling, and deployment. By the end of the book, you will be equipped with the skills and knowledge needed to extract valuable insights from IoT data and build real-world applications. WHAT YOU WILL LEARN ● Explore the essentials of IoT Data Analytics and the Industry 4.0 revolution. ● Learn how to set up the IoT Data Analytics environment. ● Equip Python developers with data analysis foundations. ● Learn to build data lakes for real-time IoT data streaming. ● Learn to deploy machine learning models on edge devices. ● Understand Edge Computing with MicroPython for efficient IoT Data Analytics. WHO THIS BOOK IS FOR If you are an experienced Python developer who wants to master IoT Data Analytics, or a newcomer who wants to learn Python and its applications in IoT, this book will give you a thorough understanding of IoT Data Analytics and practical skills for real-world use cases. TABLE OF CONTENTS 1. Necessity of Analytics Across IoT 2. Up and Running with Data Analytics Fundamentals 3. Setting Up IoT Analytics Environment 4. Managing Data Pipeline and Cleaning 5. Designing Data Lake and Executing Data Transformation 6. Implementing Descriptive Analytics Using Pandas 7. Time Series Forecasting and Predictions 8. Monitoring and Preventive Maintenance 9. Model Deployment on Edge Devices 10. Understanding Edge Computing with MicroPython 11. IoT Analytics for Self-driving Vehicles
Data Analytics For Finance Using Python
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Author : Nitin Jaglal Untwal
language : en
Publisher: CRC Press
Release Date : 2025-01-15
Data Analytics For Finance Using Python written by Nitin Jaglal Untwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-15 with Computers categories.
Unlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets. In this book, you’ll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction. Through real-world case studies and examples, you’ll learn how to: Uncover hidden patterns and trends in financial data Build predictive models that drive investment decisions Optimize portfolio performance using data-driven insights Stay ahead of the competition with cutting-edge data analytics techniques Whether you’re a finance professional seeking to enhance your data analytics skills or a researcher looking to advance the field of finance through data-driven insights, this book is an essential resource. Dive into the world of data analytics in finance and discover the power to make informed decisions, drive business success, and stay ahead of the curve. This book will be helpful for students, researchers, and users of machine learning and financial tools in the disciplines of commerce, management, and economics.
Hands On Data Preprocessing In Python
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Author : Roy Jafari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-21
Hands On Data Preprocessing In Python written by Roy Jafari 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 2022-01-21 with Computers categories.
Get your raw data cleaned up and ready for processing to design better data analytic solutions Key FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook Description Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools. What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is for This book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Data Analytics Using Open Source Tools
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Author : Jeffrey Strickland
language : en
Publisher: Lulu.com
Release Date : 2016-07-20
Data Analytics Using Open Source Tools written by Jeffrey Strickland and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-20 with Business & Economics categories.
This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.
Python Data Analytics And Visualization
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Author : Phuong Vo.T.H
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-03-31
Python Data Analytics And Visualization written by Phuong Vo.T.H 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 2017-03-31 with Computers categories.
Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization