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#### Overview of this book

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.
Preface
Section 1: Foundation for Data Analysis
Free Chapter
Getting Started with Python Libraries
Section 2: Exploratory Data Analysis and Data Cleaning
Data Visualization
Cleaning Messy Data
Signal Processing and Time Series
Section 3: Deep Dive into Machine Learning
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Section 4: NLP, Image Analytics, and Parallel Computing
Analyzing Textual Data
Analyzing Image Data
Parallel Computing Using Dask
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# Performing parametric tests

The hypothesis is the main core topic of inferential statistics. In this section, we will focus on parametric tests. The basic assumption of a parametric test is the underlying statistical distribution. Most elementary statistical methods are parametric in nature. Parametric tests are used for quantitative and continuous data. Parameters are numeric quantities that represent the whole population. Parametric tests are more powerful and reliable than non-parametric tests. The hypothesis is developed on the parameters of the population distribution. Here are some examples of parametric tests:

• A t-test is a kind of parametric test that is used for checking if there is a significant difference between the means of the two groups concerned. It is the most commonly used inferential statistic that follows the normal distribution. A t-test has two types: a one-sample t-test and a two-sample t-test. A one-sample t-test is used for checking if there is a significant...