Statistics And Data Visualisation With Python
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Statistics And Data Visualisation With Python
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Author : Jesus Rogel-Salazar
language : en
Publisher: CRC Press
Release Date : 2023-01-31
Statistics And Data Visualisation With Python written by Jesus Rogel-Salazar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with Social Science categories.
This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
Data Visualization With Python
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Author : Dr. Pooja
language : en
Publisher: BPB Publications
Release Date : 2023-07-11
Data Visualization With Python written by Dr. Pooja and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-11 with Computers categories.
Transforming data into actionable insights using Python KEY FEATURES ● Gain a comprehensive understanding of data visualization and exploratory data analysis (EDA) using Python. ● Discover valuable insights and patterns in data through visual analysis. ● Master the art of effectively communicating complex concepts by creating compelling and impactful data visualizations. DESCRIPTION Python is a popular programming language for data visualization due to its rich ecosystem of libraries and tools. If you're interested in delving into data visualization in Python, this book is an excellent resource to begin your journey. With Matplotlib, you'll master the art of creating a wide range of charts, plots, and graphs. From basic line plots to complex 3D visualizations, you'll learn how to transform raw data into engaging visuals that tell compelling stories. Dive into Seaborn, a high-level library built on top of Matplotlib, and discover how to effortlessly create beautiful and informative statistical visualizations effortlessly. From heatmaps to distribution plots, you'll unleash the full potential of Seaborn in your data analysis endeavors. Lastly, you will learn how to unleash the true potential of Bokeh and create compelling data visualizations that allow users to explore and interact with data dynamically. By the end of the book, you will have acquired the knowledge and skills necessary to create a diverse range of visualizations proficiently. WHAT YOU WILL LEARN ● Utilize Matplotlib, Seaborn, and Bokeh to produce visually captivating visualizations. ● Gain expertise in various types of charts, plots, and graphs. ● Craft visually appealing and informative statistical visualizations. ● Construct interactive and adaptable plots using Bokeh. ● Explore various techniques for conducting Exploratory Data Analysis (EDA). WHO THIS BOOK IS FOR This book caters to a wide audience, including undergraduate and postgraduate students, researchers, data managers, and data analysts. It presents an all-encompassing exploration of data visualization, equipping you with the essential groundwork to progress as a data-driven professional. TABLE OF CONTENTS 1. Understanding Data 2. Data Visualization – Importance 3. Data Visualization Use Cases 4. Data Visualization Tools and Techniques 5. Data Visualization with Matplotlib 6. Data Visualization with Seaborn 7. Data Visualization with Bokeh 8. Exploratory Data Analysis
Interactive Data Visualization With Python
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Author : Abha Belorkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-14
Interactive Data Visualization With Python written by Abha Belorkar 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 2020-04-14 with Computers categories.
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.
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
Python Data Visualization Essentials Guide
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Author : Kallur Rahman
language : en
Publisher: BPB Publications
Release Date : 2021-07-30
Python Data Visualization Essentials Guide written by Kallur Rahman and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-30 with Computers categories.
Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources
Comparative Approaches To Using R And Python For Statistical Data Analysis
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Author : Sarmento, Rui
language : en
Publisher: IGI Global
Release Date : 2017-01-06
Comparative Approaches To Using R And Python For Statistical Data Analysis written by Sarmento, Rui and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-06 with Business & Economics categories.
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.
Python For Data Science For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2019-02-27
Python For Data Science For Dummies written by John Paul Mueller 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 2019-02-27 with Computers categories.
The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
Hands On Data Analysis And Visualization With Pandas
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Author : PURNA CHANDER RAO. KATHULA
language : en
Publisher: BPB Publications
Release Date : 2020-08-13
Hands On Data Analysis And Visualization With Pandas written by PURNA CHANDER RAO. KATHULA and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Computers categories.
Learn how to use JupyterLab, Numpy, pandas, Scipy, Matplotlib, and Seaborn for Data science KEY FEATURESÊÊ _ Get familiar with different inbuilt Data structures, Functional programming, and Datetime objects. _ Handling heavy Datasets to optimize the data types for memory management, reading files in chunks, dask, and modin pandas. _ Time-series analysis to find trends, seasonality, and cyclic components. _ Seaborn to build aesthetic plots with high-level interfaces and customized themes. _ Exploratory data analysis with real-time datasets to maximize the insights about data. DESCRIPTIONÊ The book will start with quick introductions to Python and its ecosystem libraries for data science such as JupyterLab, Numpy, Pandas, SciPy, Matplotlib, and Seaborn. This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. It also covers an in-depth understanding of Python data science packages where JupyterLab used as an IDE for writing, documenting, and executing the python code, Numpy used for computation of numerical operations, Pandas for cleaning and reorganizing the data, handling large datasets and merging the dataframes to get meaningful insights. You will go through the statistics to understand the relation between the variables using SciPy and building visualization charts using Matplotllib and Seaborn libraries. WHAT WILL YOU LEARNÊ _ Learn about Python data containers, their methods, and attributes. _ Learn Numpy arrays for the computation of numerical data. _ Learn Pandas data structures, DataFrames, and Series. _ Learn statistics measures of central tendency, central limit theorem, confidence intervals, and hypothesis testing. _ A brief understanding of visualization, control, and draw different inbuilt charts to extract important variables, detect outliers, and anomalies using Matplotlib and Seaborn. Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to use Python for Data Analysis and Visualization. This book is for novices as well as experienced readers with working knowledge of the pandas library. Basic knowledge of Python is a must.Ê TABLE OF CONTENTSÊ 1. Introduction to Data Analysis 2. Jupyter lab 3. Python overview 4. Introduction to Numpy 5. Introduction to PandasÊ 6. Data Analysis 7. Time-Series Analysis 8. Introduction to Statistics 9. Matplotlib 10. Seaborn 11. Exploratory Data Analysis
Interactive Data Visualization With Python
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Author : Abha Belorkar
language : en
Publisher:
Release Date : 2019-10-22
Interactive Data Visualization With Python written by Abha Belorkar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-22 with categories.
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key Features Study and use Python interactive libraries, such as Bokeh and Plotly Explore different visualization principles and understand when to use which one Create interactive data visualizations with real-world data Book Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learn Explore and apply different static and interactive data visualization techniques Make effective use of plot types and features from the Matplotlib, Seaborn, Altair, Bokeh, and Plotly libraries Master the art of selecting appropriate plotting parameters and styles to create attractive plots Choose meaningful and informative ways to present your stories through data Customize data visualization for specific scenarios, contexts, and audiences Avoid common errors and slip-ups in visualizing data Who this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.
Spss Statistics For Data Analysis And Visualization
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Author : Keith McCormick
language : en
Publisher: John Wiley & Sons
Release Date : 2017-05-01
Spss Statistics For Data Analysis And Visualization written by Keith McCormick 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 2017-05-01 with Computers categories.
Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.