Data Science Fundamentals with R, Python, and Open Data

✍ Scribed by Marco Cremonini

• Publisher: WILEY
• Year: 2024
• Tongue: English
• Leaves: 1442
• Category: Library

✦ Synopsis

Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate.

✦ Table of Contents

Table of Contents
Title Page
Copyright
Preface
About the Companion Website
Introduction
Approach
Open Data
What You Don't Learn
1 Open-Source Tools for Data Science
1.1 R Language and RStudio
1.2 Python Language and Tools
1.3 Advanced Plain Text Editor
1.4 CSV Format for Datasets
Questions
2 Simple Exploratory Data Analysis
2.1 Missing Values Analysis
2.2 R: Descriptive Statistics and Utility Functions
2.3 Python: Descriptive Statistics and Utility Functions
Questions
3 Data Organization and First Data Frame Operations
Datasets
3.1 R: Read CSV Datasets and Column Selection
3.2 R: Rename and Relocate Columns
3.3 R: Slicing, Column Creation, and Deletion
3.4 R: Separate and Unite Columns
3.5 R: Sorting Data Frames
3.6 R: Pipe
3.7 Python: Column Selection
3.8 Python: Rename and Relocate Columns
3.9 Python: NumPy Slicing, Selection with Index, Column Creation and Deletion
3.10 Python: Separate and Unite Columns
3.11 Python: Sorting Data Frame
Questions
4 Subsetting with Logical Conditions
4.1 Logical Operators
4.2 R: Row Selection
5 Operations on Dates, Strings, and Missing Values
Datasets
5.1 R: Operations on Dates and Strings
5.2 R: Handling Missing Values and Data Type Transformations
5.3 R: Example with Dates, Strings, and Missing Values
5.4 Pyhton: Operations on Dates and Strings
5.5 Python: Handling Missing Values and Data Type Transformations
5.6 Python: Examples with Dates, Strings, and Missing Values
Questions
6 Pivoting and Wide-long Transformations
Datasets
6.1 R: Pivoting
6.2 Python: Pivoting
7 Groups and Operations on Groups
Dataset
7.1 R: Groups
7.2 Python: Groups
Questions
8 Conditions and Iterations
Datasets
8.1 R: Conditions and Iterations
8.2 Python: Conditions and Iterations
Questions
9 Functions and Multicolumn Operations
9.1 R: User-defined Functions
9.2 R: Multicolumn Operations
9.3 Python: User-defined and Lambda Functions
Questions
10 Join Data Frames
Datasets
10.1 Basic Concepts
10.2 Python: Join Operations
Questions
11 List/Dictionary Data Format
Datasets
11.1 R: List Data Format
11.2 R: JSON Data Format and Use Cases
11.3 Python: Dictionary Data Format
Questions
Index
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