Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization.

You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding.

Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression.

What You’ll Learn

• Understand installation procedures and valuable insights into Python, data types, typecasting
• Examine the fundamental statistical analysis required in most data science and analytics reports
• Clean the most common data set problems
• Use linear progression for data prediction

Who This Book Is For

Data Analysts, data scientists, Python programmers, and software developers new to data science.

Table of Contents

Chapter 1: Data Science in Action

Chapter 2: Getting Started

Chapter 3: Data Visualizations

Chapter 4: Statistical Analysis and Linear Models

Chapter 5: Data Preprocessing and Feature Engineering

Chapter 6: Preparing Data for Analysis

Chapter 7: Regression

About the Author

Engy Fouda is an adjunct lecturer at SUNY New Paltz teaching Intro to Data Science using SAS Studio and Introduction to Machine Learning using Python. She is an Apress and Packt Publishing author. Currently, she teaches SAS Fundamentals, Intermediate SAS, Advanced SAS, SAS SQL, Introduction to Python, Python for Data Science, Docker Fundamentals, Docker Enterprise for Developers, Docker Enterprise for Operations, Kubernetes, and DCA and SAS exams test-prep courses tracks at several venues as a freelance instructor.

She also works as a freelance writer for Geek Culture, Towards Data Science, and Medium Partner Program. She holds two master’s degrees: one in journalism from Harvard University, the Extension School, and the other in computer engineering from Cairo University. Moreover, she earned a Data Science Graduate Professional Certificate from Harvard University, the Extension School. She volunteers as the chair of Egypt Scholars board and is former executive manager and former Momken team leader (Engineering for the Blind). She is the author of the books Learn Data Science Using SAS Studio and A Complete Guide to Docker for Operations and Development published by Apress and a co-author of The Docker Workshop published by Packt.