Название: Ultimate Python Libraries for Data Analysis and Visualization: Leverage Pandas, NumPy, Matplotlib, Seaborn, Julius AI and No-Code Tools for Data Acquisition, Visualization, and Statistical AnalysisАвтор: Abhinaba Banerjee
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 507
Язык: английский
Формат: pdf, epub
Размер: 14.9 MB
Test your Data Analysis skills to its fullest using Python and other no-code toolsBook DescriptionUltimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets.Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more.By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax.
Chapter 1. Introduction to Data Analysis and Data Visualization Using The chapter starts with an introduction to the topic of data analysis and visualization, the importance of data analysis, and the role of data analysis in decision-making. It covers the main steps of the data analysis process, details on data visualization, the relevant tools and techniques used, fundamentals of Python and Anaconda installation, Jupyter exploration and usage, as well as data exploration with visualization using Jupyter.
Chapter 2. Data The chapter delves into the importance of data quality, discusses the various ways to acquire data, explains and demonstrates web scraping using different Python libraries and tools, and finally explains how data quality can be ensured.
Chapter 3. Data Cleaning and The chapter explains the process of Data Cleaning and Preparation in detail by explaining its importance, identifying and fixing errors in the data (such as handling missing values, inconsistencies, and outliers), data transformation, data integration, and some libraries dedicated to data cleaning.
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Chapter 8. Analyzing Real-World Data Sets using The chapter guides you in understanding real-world datasets, their characteristics, techniques to handle the datasets, in-depth data analysis, and statistical analysis of a real-world dataset using Python. It also discusses other low-code and no-code tools such as Julius, Gigasheet, Mito, PivotTableJs, Drawdata, PyGWalker, and so on.
Appendix A Python Cheat The chapter illustrates the Python programming language in the form of a cheat sheet that will give you an advantage while trying to solve data analysis, data cleaning, and data visualization problems.
Contents:
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