Chapter 1, Exploratory Data Analysis Fundamentals, will help us learn and revise the fundamental aspects of EDA. We will dig into the importance of EDA and the main data analysis tasks, and try to make sense out of data. In addition to that, we will use Python to explore different types of data, including numerical data, time-series data, geospatial data, categorical data, and others.
Chapter 2, Visual Aids for EDA, will help us gain proficiency with different tools for visualizing the information that we get from investigation and make analysis much clearer. We will figure out how to use data visualization tools such as box plots, histograms, multi-variate charts, and more. Notwithstanding that, we will get our hands dirty in plotting an enlightening visual graph using real databases. Finally, we will investigate the intuitive forms of these plots.
Chapter 3, EDA with Personal Email, will help us figure out how to import a dataset from your personal Gmail account and work on analyzing the extracted dataset. We will perform basic EDA techniques, including data loading, data cleansing, data preparation, data visualization, and data analysis, on the extracted dataset.
Chapter 4, Data Transformation, is where you will take your first steps in data wrangling. We will see how to merge database-style DataFrames, merge on the index, concatenate along an axis, combine data with overlaps, reshape with hierarchical indexing, and pivot from long to wide format. We will look at what needs to be done with a dataset before analysis takes place, such as removing duplicates, replacing values, renaming axis indexes, discretization and binning, and detecting and filtering outliers. We will work on transforming data using a function or mapping, permutation, and random sampling and computing indicators/dummy variables.
Chapter 5, Descriptive Statistics, will teach you about essential statistical measures for gaining insights about data that are not noticeable at the surface level. We will become familiar with the equations for computing the variance and standard deviation of datasets as well as figuring out percentiles and quartiles. Furthermore, we will envision those factual measures with visualization. We will use tools such as box plots to gain knowledge from statistics.
Chapter 6, Grouping Datasets, will cover the rudiments of grouping and how it can change our datasets in order to help us to analyze them better. We will look at different group-by mechanics that will amass our dataset into various classes in which we can perform aggregate activities. We will also figure out how to dissect categorical data with visualizations, utilizing pivot tables and cross-tabulations.
Chapter 7, Correlation, will help us to understand the correlation between different factors and to identify to what degree different factors are relevant. We will learn about the different kinds of examinations that we can carry out to discover the relationships between data, including univariate analysis, bivariate analysis, and multivariate analysis on the Titanic dataset, as well as looking at Simpson's paradox. We will observe how correlation does not always equal causation.
Chapter 8, Time Series Analysis, will help us to understand time-series data and how to perform EDA on it. We will use the open power system data for time series analysis.
Chapter 9, Hypothesis Testing and Regression, will help us learn about hypothesis testing and linear, non-linear, and multiple linear regression. We will build a basis for model development and evaluation. We will be using polynomial regression and pipelines for model evaluation.
Chapter 10, Model Development and Evaluation, will help us learn about a unified machine learning approach, discuss different types of machine learning algorithms and evaluation techniques. Moreover, in this chapter, we are going to perform the unsupervised learning task of clustering with text data. Furthermore, we will discuss model selection and model deployment techniques.
Chapter 11, EDA on Wine Quality Data, will teach us how to use all the techniques learned throughout the book to perform advanced EDA on a wine quality dataset. We will import the dataset, research the variables, slice the data based on different points of interest, and perform data analysis.