Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

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.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Central limit theorem

Data analysis methods involve hypothesis testing and deciding confidence intervals. All statistical tests assume that the population is normally distributed. The central limit theorem is the core of hypothesis testing. According to this theorem, the sampling distribution approaches a normal distribution with an increase in the sample size. Also, the mean of the sample gets closer to the population means and the standard deviation of the sample gets reduced. This theorem is essential for working with inferential statistics, helping data analysts figure out how samples can be useful in getting insights about the population.

Does it provide answers to questions such as what size of sample should be taken or which sample size is an accurate representation of the population? You can understand this with the help of the following diagram:

In the preceding diagram, you can see four histograms for different-different sample sizes 50, 100, 200, and 500. If you observe...