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Unlock the Power of Data: A Comprehensive Guide to Collection, Processing, Wrangling, Visualization, and Modeling

David Peterson

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Published in Python Data Analysis: Perform Data Collection Data Processing Wrangling Visualization And Model Building Using Python 3rd Edition

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In today's data-driven world, the ability to effectively collect, process, wrangle, visualize, and model data is essential for businesses and individuals alike. This comprehensive guide will provide you with a step-by-step roadmap to master these critical data science skills, empowering you to make informed decisions and uncover actionable insights from your data. Whether you're a seasoned data scientist or just starting out, this book will equip you with the knowledge and tools you need to succeed.

Chapter 1: Data Collection

The first step in the data science process is collecting data. This chapter covers a wide range of data collection methods, including:

Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

by Avinash Navlani

4.7 out of 5

Language	:	English
File size	:	18586 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	478 pages
Screen Reader	:	Supported

Structured data sources (e.g., databases, spreadsheets)
Unstructured data sources (e.g., text documents, images, videos)
Web scraping
Social media listening
Sensor data

You will learn how to identify the appropriate data sources for your specific needs and how to collect data in a systematic and efficient manner.

Chapter 2: Data Processing

Once you have collected your data, it is important to process it to make it usable for analysis. This chapter covers a variety of data processing techniques, including:

Data cleaning and transformation
Data normalization
Feature engineering
Data integration
Data reduction

You will learn how to perform these techniques using popular data science tools such as Python and R.

Chapter 3: Data Wrangling

Data wrangling is the process of cleaning, transforming, and shaping data into a format that is suitable for analysis. This chapter covers a wide range of data wrangling techniques, including:

Data imputation
Data filtering
Data sorting
Data aggregation
Data merging

You will learn how to perform these techniques using popular data wrangling tools such as Pandas and dplyr.

Chapter 4: Data Visualization

Data visualization is the process of representing data in a visual form. This chapter covers a variety of data visualization techniques, including:

Charts (e.g., line charts, bar charts, pie charts)
Graphs (e.g., scatter plots, histograms, box plots)
Maps
Interactive visualizations
Dashboards

You will learn how to create these visualizations using popular data visualization tools such as Matplotlib, Seaborn, and Tableau.

Chapter 5: Data Modeling

Data modeling is the process of creating a representation of a real-world system using data. This chapter covers a variety of data modeling techniques, including:

Conceptual data modeling
Logical data modeling
Physical data modeling
Data warehousing
Machine learning

You will learn how to apply these techniques to create data models that meet the specific needs of your organization.

This comprehensive guide will provide you with the knowledge and skills you need to master the entire data science process, from data collection to data modeling. By following the step-by-step instructions in this book, you will be able to unlock the power of your data and gain actionable insights that can help you make informed decisions and achieve your business goals.

Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

by Avinash Navlani

4.7 out of 5