The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market.
While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science.
Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
Author(s): Chandril Ghosh
Publisher: Springer
Year: 2022
Language: English
Pages: 168
Acknowledgement
Contents
About the Author
Chapter 1: Introduction
1.1 Before Getting Started
1.1.1 Overview (of Old Ways to Analyse Data and Some Problems Related to Them)
1.1.2 Who Am I?
1.1.3 How Did I Get There?
1.1.4 Who Is This Book For?
1.1.5 Who Is This Book NOT For?
1.1.6 What’s Special You Get in This Book?
1.1.7 So, What Does This Book Have?
1.1.8 How to Best Make Use of This Book?
1.2 Types of Research Studies
1.2.1 Explanatory Research
1.2.2 Predictive Research
1.2.3 Exploratory Research
1.3 Data
1.3.1 To Collect or Not Collect Your Own Data
1.3.2 Where to Get the Data From?
1.3.3 Ways in Which Data Is Divided
1.3.4 Five Lessons
1.4 Statistics: A Refresher Before Getting into Machine Learning
References
Chapter 2: Python Programming
2.1 But Do I Have to Learn to Code for Data Analysis?
2.2 How to Install Python?
2.3 Variables
2.4 Operators
2.4.1 Arithmetic Operators
2.4.2 Comparison Operators
2.5 Statements
2.6 Loops
2.7 Data Structure
2.8 Methods and Functions (Built-Ins) in Python
2.8.1 Methods
2.8.2 Function
2.9 Error Resolution
2.10 Last Words
Chapter 3: Data Pre-processing
3.1 Introduction
3.2 Data Cleaning
3.2.1 Problem 1: Duplicate Columns and Categorical Variables
3.2.2 Problem 2: Outliers
3.2.3 Problem 3: Missing Values
3.3 Data Transformation
3.3.1 Converting Categorical Variables into Numeric Variables
3.3.2 Converting Continuous Variables into Categorical Variables
3.3.3 Feature Scaling
3.4 Data Reduction
3.4.1 Strategy 1
3.4.2 Strategy 2
3.4.3 Strategy 3
3.4.4 Strategy 4
3.4.5 Strategy 5
3.5 Final Words
References
Chapter 4: Machine Learning
4.1 Introduction
4.2 Classification
4.2.1 Getting Started with Supervised Machine Learning
4.2.2 Machine Learning (Classifier): The Leak-Proof Approach
4.2.3 Confidence Interval
4.2.4 Choosing the Best Model for Classification
4.2.5 Optimising the Predictive Accuracies of the Model with Hyperparameter Tuning
4.3 Regression
4.3.1 Regression Using Machine Learning and How to Interpret the Results
4.3.2 Feature Importance
4.3.3 Exploratory Research Using Unsupervised Machine Learning
4.4 Clustering
4.4.1 Hierarchical Clustering
4.4.2 K-Means Clustering
4.5 Principal Component Analysis (PCA)
4.6 Rule Mining
References
Chapter 5: End Note
Index
