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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

The standard process of data analysis

Data analysis refers to investigating the data, finding meaningful insights from it, and drawing conclusions. The main goal of this process is to collect, filter, clean, transform, explore, describe, visualize, and communicate the insights from this data to discover decision-making information. Generally, the data analysis process is comprised of the following phases:

  1. Collecting Data: Collect and gather data from several sources.
  2. Preprocessing Data: Filter, clean, and transform the data into the required format.
  3. Analyzing and Finding Insights: Explore, describe, and visualize the data and find insights and conclusions.
  4. Insights Interpretations: Understand the insights and find the impact each variable has on the system.
  5. Storytelling: Communicate your results in the form of a story so that a layman can understand them.

We can summarize these steps of the data analysis process via the following process diagram:

In this section, we have covered the standard data analysis process, which emphasizes finding interpretable insights and converting them into a user story. In the next section, we will discuss the KDD process.

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Python Data Analysis - Third Edition
Published in: Feb 2021
Publisher: Packt
ISBN-13: 9781789955248
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