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


The SEMMA acronym's full form is Sample, Explore, Modify, Model, and Assess. This sequential data mining process is developed by SAS. The SEMMA process has five major phases:

  1. Sample: In this phase, we identify different databases and merge them. After this, we select the data sample that's sufficient for the modeling process.
  2. Explore: In this phase, we understand the data, discover the relationships among variables, visualize the data, and get initial interpretations.
  3. Modify: In this phase, data is prepared for modeling. This phase involves dealing with missing values, detecting outliers, transforming features, and creating new additional features.
  4. Model: In this phase, the main concern is selecting and applying different modeling techniques, such as linear and logistic regression, backpropagation networks, KNN, support vector machines, decision trees, and Random Forest.
  5. Assess: In this last phase, the predictive models that have been developed are evaluated using performance evaluation measures.

The following diagram shows this process:

The preceding diagram shows the steps involved in the SEMMA process. SEMMA emphasizes model building and assessment. Now, let's discuss the CRISP-DM process.