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E-raamat: Data Analysis with Machine Learning for Psychologists: Crash Course to Learn Python 3 and Machine Learning in 10 hours

  • Formaat: EPUB+DRM
  • Ilmumisaeg: 17-Oct-2022
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031146343
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Data Analysis with Machine Learning for Psychologists: Crash Course to Learn Python 3 and Machine Learning in 10 hoursSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 17-Oct-2022
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031146343
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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.
1 Introduction
1(32)
1.1 Before Getting Started
3(7)
1.1.1 Overview (of Old Ways to Analyse Data and Some Problems Related to Them)
3(3)
1.1.2 Who Am I?
6(1)
1.1.3 How Did I Get There?
6(1)
1.1.4 Who Is This Book For?
7(1)
1.1.5 Who Is This Book NOT For?
8(1)
1.1.6 What's Special You Get in This Book?
9(1)
1.1.7 So, What Does This Book Have?
9(1)
1.1.8 How to Best Make Use of This Book?
9(1)
1.2 Types of Research Studies
10(7)
1.2.1 Explanatory Research
11(4)
1.2.2 Predictive Research
15(2)
1.2.3 Exploratory Research
17(1)
1.3 Data
17(9)
1.3.1 To Collect or Not Collect Your Own Data
17(2)
1.3.2 Where to Get the Data From?
19(4)
1.3.3 Ways in Which Data Is Divided
23(1)
1.3.4 Five Lessons
24(2)
1.4 Statistics: A Refresher Before Getting into Machine Learning
26(7)
References
30(3)
2 Python Programming
33(22)
2.1 But Do I Have to Learn to Code for Data Analysis?
34(1)
2.2 How to Install Python?
34(4)
2.3 Variables
38(3)
2.4 Operators
41(3)
2.4.1 Arithmetic Operators
41(2)
2.4.2 Comparison Operators
43(1)
2.5 Statements
44(1)
2.6 Loops
44(3)
2.7 Data Structure
47(1)
2.8 Methods and Functions (Built-Ins) in Python
48(3)
2.8.1 Methods
48(2)
2.8.2 Function
50(1)
2.9 Error Resolution
51(1)
2.10 Last Words
52(3)
3 Data Pre-processing
55(32)
3.1 Introduction
55(3)
3.2 Data Cleaning
58(9)
3.2.1 Problem 1: Duplicate Columns and Categorical Variables
59(3)
3.2.2 Problem 2: Outliers
62(2)
3.2.3 Problem 3: Missing Values
64(3)
3.3 Data Transformation
67(7)
3.3.1 Converting Categorical Variables into Numeric Variables
67(1)
3.3.2 Converting Continuous Variables into Categorical Variables
67(3)
3.3.3 Feature Scaling
70(4)
3.4 Data Reduction
74(10)
3.4.1 Strategy 1
77(1)
3.4.2 Strategy 2
78(1)
3.4.3 Strategy 3
78(1)
3.4.4 Strategy 4
79(3)
3.4.5 Strategy 5
82(2)
3.5 Final Words
84(3)
References
85(2)
4 Machine Learning
87(70)
4.1 Introduction
88(13)
4.2 Classification
101(26)
4.2.1 Getting Started with Supervised Machine Learning
101(11)
4.2.2 Machine Learning (Classifier): The Leak-Proof Approach
112(4)
4.2.3 Confidence Interval
116(3)
4.2.4 Choosing the Best Model for Classification
119(4)
4.2.5 Optimising the Predictive Accuracies of the Model with Hyperparameter Tuning
123(4)
4.3 Regression
127(9)
4.3.1 Regression Using Machine Learning and How to Interpret the Results
127(3)
4.3.2 Feature Importance
130(6)
4.3.3 Exploratory Research Using Unsupervised Machine Learning
136(1)
4.4 Clustering
136(12)
4.4.1 Hierarchical Clustering
136(7)
4.4.2 K-Means Clustering
143(5)
4.5 Principal Component Analysis (PCA)
148(1)
4.6 Rule Mining
149(8)
References
154(3)
5 End Note
157(4)
Index 161
Dr Chandril Ghosh is a UK-based chartered psychologist and is currently working as the Lecturer in Clinical/Counselling Psychology at the Bath Spa University. He completed his BSc in Psychology (honours) and MSc in Clinical Psychology from India. After completing his MSc, Ghosh began to study machine learning and python programming through books and online materials on the subject. He had no background or prior experience with coding or computer science back then. During his doctoral studies, he utilised his knowledge on the subject to employ machine learning techniques to explore psychopathology. Around the same time, he was hired multiple times to design and deliver a crash course on python 3 and machine learning for postgraduate students at the Queens University Belfast. Furthermore, he also runs online courses on the subject outside the University, and gets students from about 56 countries. This book is a product of an accumulation of his hundreds of hours of teaching and feedback from students with social science backgrounds. 















 
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