Data Science Fundamentals with R, Python, and Open Data

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. Cover 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 End User License Agreement