Data Analytics Using Python
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Data Analytics With Python
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
Author: Mahmoud Ahmad Al-Khasawneh
language: en
Publisher: Xoffencer international book publication house
Release Date: 2024-08-07
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
Author: Avinash Navlani
language: en
Publisher: Packt Publishing Ltd
Release Date: 2021-02-05
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.