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Preface	p. xv
Introduction to Probability and Statistics	p. 1
What Is a Probability?	p. 1
Calculating the Expected Value	p. 2
Random Variables	p. 3
Discrete versus Continuous Random Variables	p. 4
Well-Known Probability Distributions	p. 4
Fundamental Concepts in Statistics	p. 5
The Mean	p. 5
The Median	p. 5
The Mode	p. 5
The Variance and Standard Deviation	p. 6
Population, Sample, and Population Variance	p. 6
Chebyshev's Inequality	p. 7
What Is a P-Value?	p. 7
The Moments of a Function (Optional)	p. 7
What Is Skewness?	p. 8
What Is Kurtosis?	p. 8
Data and Statistics	p. 9
The Central Limit Theorem	p. 9
Correlation versus Causation	p. 9
Statistical Inferences	p. 10
Statistical Terms RSS, TSS, R^2, and Fl Score	p. 10
What Is an Fl Score?	p. 11
Gini Impurity, Entropy, and Perplexity	p. 11
What Is Gini Impurity?	p. 12
What Is Entropy?	p. 12
Calculating Gini Impurity and Entropy Values	p. 12
Multidimensional Gini Index	p. 13
What Is Perplexity?	p. 14
Cross-Entropy and KX Divergence	p. 14
What Is Cross-Entropy?	p. 14
What Is KL Divergence?	p. 15
What Is Their Purpose?	p. 15
Covariance and Correlation Matrices	p. 16
The Covariance Matrix	p. 16
Covariance Matrix: An Example	p. 17
The Correlation Matrix	p. 17
Eigenvalues and Eigenvectors	p. 18
Calculating Eigenvectors: A Simple Example	p. 18
Gauss Jordan Elimination (Optional)	p. 19
PCA (Principal Component Analysis)	p. 20
The New Matrix of Eigenvectors	p. 21
Well-Known Distance Metrics	p. 22
Pearson Correlation Coefficient	p. 23
Jaccard Index (or Similarity)	p. 23
Local Sensitivity Hashing (Optional)	p. 24
Types of Distance Metrics	p. 24
What Is Bayesian Inference?	p. 26
Bayes' Theorem	p. 26
Some Bayesian Terminology	p. 26
What Is MAP?	p. 27
Why Use Bayes' Theorem?	p. 27
Summary	p. 27
Working with Data	p. 29
Dealing With Data: What Can Go Wrong?	p. 30
What Is Data Drift?	p. 30
What Are Datasets?	p. 30
Data Preprocessing	p. 31
Data Types	p. 32
Preparing Datasets	p. 33
Discrete Data versus Continuous Data	p. 33
"Binning" Continuous Data	p. 34
Scaling Numeric Data via Normalization	p. 34
Scaling Numeric Data via Standardization	p. 35
Scaling Numeric Data via Robust Standardization	p. 36
What to Look for in Categorical Data	p. 36
Mapping Categorical Data to Numeric Values	p. 37
Working With Dates	p. 39
Working With Currency	p. 39
Working With Outliers and Anomalies	p. 39
Outlier Detection/Removal	p. 40
Finding Outliers With Numpy	p. 41
Finding Outliers With Pandas	p. 44
Calculating Z-Scores to Find Outliers	p. 46
Finding Outliers With SkLearn (Optional)	p. 48
Working With Missing Data	p. 49
Imputing Values: When Is Zero a Valid Value?	p. 50
Dealing With Imbalanced Datasets	p. 51
What Is SMOTE?	p. 52
SMOTE Extensions	p. 52
The Bias-Variance Tradeoff	p. 53
Types of Bias in Data	p. 54
Analyzing Classifiers (Optional)	p. 55
What Is LIME?	p. 55
What Is ANOVA?	p. 56
Summary	p. 56
Introduction to Pandas	p. 57
What Is Pandas?	p. 57
Pandas DataFrames	p. 58
Pandas Operations: In-place or Not?	p. 58
Data Frames and Data Cleaning Tasks	p. 58
A Pandas DataFrame Example	p. 59
Describing a Pandas Data Frame	p. 61
Pandas Boolean Data Frames	p. 63
Transposing a Pandas Data Frame	p. 63
Pandas Data Frames and Random Numbers	p. 64
Converting Categorical Data to Numeric Data	p. 66
Merging and Splitting Columns in Pandas	p. 69
Combining Pandas DataFrames	p. 71
Data Manipulation With Pandas DataFrames	p. 72
Pandas DataFrames and CSV Files	p. 73
Useful Options for the Pandas read_csv() Function	p. 76
Reading Selected Rows From CSV Files	p. 76
Pandas DataFrames and Excel Spreadsheets	p. 79
Useful Options for Reading Excel Spreadsheets	p. 80
Select, Add, and Delete Columns in Data frames	p. 80
Handling Outliers in Pandas	p. 82
Pandas DataFrames and Simple Statistics	p. 84
Finding Duplicate Rows in Pandas	p. 85
Finding Missing Values in Pandas	p. 87
Missing Values in Iris-Based Dataset	p. 90
Sorting Data Frames in Pandas	p. 93
Working With groupby() in Pandas	p. 94
Aggregate Operations With the titanic.csv Dataset	p. 96
Working With apply() and mapapply() in Pandas	p. 98
Useful One-Line Commands in Pandas	p. 101
Working With JSON-Based Data	p. 103
Python Dictionary and JSON	p. 103
Python, Pandas, and JSON	p. 104
Summary	p. 105
Introduction to RDBMS and SQL	p. 107
What Is an RDBMS?	p. 107
What Relationships Do Tables Have in an RDBMS?	p. 107
Features of an RDBMS	p. 108
What Is ACID?	p. 108
When Do We Need an RDBMS?	p. 109
The Importance of Normalization	p. 110
A Four-Table RDBMS	p. 111
Detailed Table Descriptions	p. 112
The customers Table	p. 112
The purchase_orders Table	p. 113
The line_items Table	p. 114
The item_desc Table	p. 115
What Is SQL?	p. 116
DCL, DDL? DQL, DML, and TCL	p. 117
SQL Privileges	p. 118
Properties of SQL Statements	p. 118
The CREATE Keyword	p. 119
What Is MySQL?	p. 119
What About MariaDB?	p. 119
Installing MySQL	p. 120
Data Types in MySQL	p. 120
The CHAR and VARCHAR Data Types	p. 120
String-Based Data Types	p. 121
FLOAT and DOUBLE Data Types	p. 121
BLOB and TEXT Data Types	p. 121
MySQL Database Operations	p. 122
Creating a Database	p. 122
Display a List of Databases	p. 122
Display a List of Database Users	p. 123
Dropping a Database	p. 123
Exporting a Database	p. 123
Renaming a Database	p. 124
The INFORMATION_SCHEMA Table	p. 125
The PROCESSLIST Table	p. 126
SQL Formatting Tools	p. 127
Summary	p. 127
Working with SQL and MySQL	p. 129
Create Database Tables	p. 130
Manually Creating Tables for mytools.com	p. 130
Creating Tables via an SQL Script for mytools.com	p. 131
Creating Tables With Japanese Test	p. 132
Creating Tables From the Command Line	p. 134
Drop Database Tables	p. 134
Dropping Tables via a SQL Script for mytools.com	p. 134
Altering Database Tables With the ALTER Keyword	p. 135
Add a Column to a Database Table	p. 135
Drop a Column From a Database Table	p. 137
Change the Data Type of a Column	p. 137
What Are Referential Constraints?	p. 139
Combining Data for a Table Update (Optional)	p. 139
Merging Data for a Table Update	p. 140
Appending Data to a Table From a CSV File	p. 141
Appending Table Data from CSV Files via SQL	p. 142
Inserting Data Into Tables	p. 144
Populating Tables From Text Files	p. 144
Working With Simple SELECT Statements	p. 146
Duplicate versus Distinct Rows	p. 148
Unique Rows	p. 148
The EXISTS Keyword	p. 148
The LIMIT Keyword	p. 149
DELETE, TRUNCATE, and DROP in SQL	p. 149
More Options for the DELETE Statement in SQL	p. 150
Creating Tables From Existing Tables in SQL	p. 150
Working With Temporary Tables in SQL	p. 151
Creating Copies of Existing Tables in SQL	p. 152
What Is an SQL Index?	p. 152
Types of Indexes	p. 152
Creating an Index	p. 153
Disabling and Enabling an Index	p. 153
View and Drop Indexes	p. 154
Overhead of Indexes	p. 155
Considerations for Defining Indexes	p. 155
Selecting Columns for an Index	p. 156
Finding Columns Included in Indexes	p. 157
Export Data From MySQL	p. 157
Export the Result Set of a SQL Query	p. 157
Export a Database or Its Contents	p. 157
Using LOAD DATA in MySQL	p. 158
Data Cleaning in SQL	p. 158
Replace NULL With 0	p. 159
Replace NULL Values With Average Value	p. 159
Replace Multiple Values With a Single Value	p. 161
Handle Mismatched Attribute Values	p. 162
Convert Strings to Date Values	p. 163
Data Cleaning From the Command Line (Optional)	p. 165
Working With the sed Utility	p. 165
Working With the awk Utility	p. 167
Summary	p. 169
NLP and Data Cleaning	p. 171
NLP Tasks in ML	p. 171
NLP Steps for Training a Model	p. 172
Text Normalization and Tokenization	p. 172
Word Tokenization in Japanese	p. 173
Text Tokenization With Unix Commands	p. 175
Handling Stop Words	p. 175
What Is Stemming?	p. 176
Singular versus Plural Word Endings	p. 176
Common Stemmers	p. 176
Stemmers and Word Prefixes	p. 177
Over Stemming and Under Stemming	p. 177
What Is Lemmatization?	p. 178
Stemming/Lemmatization Caveats	p. 178
Limitations of Stemming and Lemmatization	p. 178
Working With Text: POS	p. 179
POS Tagging	p. 179
POS Tagging Techniques	p. 179
Cleaning Data With Regular Expressions	p. 180
Cleaning Data With the cleantext Library	p. 184
Handling Contracted Words	p. 185
What Is BeautifulSoup?	p. 187
Web Scraping With Pure Regular Expressions	p. 190
What Is Scrapy?	p. 192
Summary	p. 193
Data Visualization	p. 195
What Is Data Visualization?	p. 195
Types of Data Visualization	p. 196
What Is Matplotlib?	p. 196
Lines in a Grid in Matplotlib	p. 197
A Colored Grid in Matplotlib	p. 198
Randomized Data Points in Matplotlib	p. 199
A Histogram in Matplotlib	p. 200
A Set of Line Segments in Matplotlib	p. 201
Plotting Multiple Lines in Matplotlib	p. 202
Trigonometric Functions in Matplotlib	p. 203
Display IQ Scores in Matplotlib	p. 204
Plot a Best-Fitting Line in Matplotlib	p. 204
The Iris Dataset in Sklearn	p. 206
Sklearn, Pandas, and the Iris Dataset	p. 207
Working With Seaborn	p. 209
Features of Seaborn	p. 210
Seaborn Built-in Datasets	p. 210
The Iris Dataset in Seaborn	p. 211
The Titanic Dataset in Seaborn	p. 212
Extracting Data From the Titanic Dataset in Seaborn (1)	p. 212
Extracting Data from Titanic Dataset in Seaborn (2)	p. 215
Visualizing a Pandas Dataset in Seaborn	p. 217
Data Visualization in Pandas	p. 219
What Is Bokeh?	p. 220
Summary	p. 222
Index	p. 223
Table of Contents provided by Ingram. All Rights Reserved.

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Product Details

FormatPaperback

LanguageEnglish

PublisherMercury Learning & Information

Publication DateMay. 26th, 2022

ISBN-139781683928201

ISBN-101683928202

Shipping Weight0.88 lbs

Preface	p. xv
Introduction to Probability and Statistics	p. 1
What Is a Probability?	p. 1
Calculating the Expected Value	p. 2
Random Variables	p. 3
Discrete versus Continuous Random Variables	p. 4
Well-Known Probability Distributions	p. 4
Fundamental Concepts in Statistics	p. 5
The Mean	p. 5
The Median	p. 5
The Mode	p. 5
The Variance and Standard Deviation	p. 6
Population, Sample, and Population Variance	p. 6
Chebyshev's Inequality	p. 7
What Is a P-Value?	p. 7
The Moments of a Function (Optional)	p. 7
What Is Skewness?	p. 8
What Is Kurtosis?	p. 8
Data and Statistics	p. 9
The Central Limit Theorem	p. 9
Correlation versus Causation	p. 9
Statistical Inferences	p. 10
Statistical Terms RSS, TSS, R^2, and Fl Score	p. 10
What Is an Fl Score?	p. 11
Gini Impurity, Entropy, and Perplexity	p. 11
What Is Gini Impurity?	p. 12
What Is Entropy?	p. 12
Calculating Gini Impurity and Entropy Values	p. 12
Multidimensional Gini Index	p. 13
What Is Perplexity?	p. 14
Cross-Entropy and KX Divergence	p. 14
What Is Cross-Entropy?	p. 14
What Is KL Divergence?	p. 15
What Is Their Purpose?	p. 15
Covariance and Correlation Matrices	p. 16
The Covariance Matrix	p. 16
Covariance Matrix: An Example	p. 17
The Correlation Matrix	p. 17
Eigenvalues and Eigenvectors	p. 18
Calculating Eigenvectors: A Simple Example	p. 18
Gauss Jordan Elimination (Optional)	p. 19
PCA (Principal Component Analysis)	p. 20
The New Matrix of Eigenvectors	p. 21
Well-Known Distance Metrics	p. 22
Pearson Correlation Coefficient	p. 23
Jaccard Index (or Similarity)	p. 23
Local Sensitivity Hashing (Optional)	p. 24
Types of Distance Metrics	p. 24
What Is Bayesian Inference?	p. 26
Bayes' Theorem	p. 26
Some Bayesian Terminology	p. 26
What Is MAP?	p. 27
Why Use Bayes' Theorem?	p. 27
Summary	p. 27
Working with Data	p. 29
Dealing With Data: What Can Go Wrong?	p. 30
What Is Data Drift?	p. 30
What Are Datasets?	p. 30
Data Preprocessing	p. 31
Data Types	p. 32
Preparing Datasets	p. 33
Discrete Data versus Continuous Data	p. 33
"Binning" Continuous Data	p. 34
Scaling Numeric Data via Normalization	p. 34
Scaling Numeric Data via Standardization	p. 35
Scaling Numeric Data via Robust Standardization	p. 36
What to Look for in Categorical Data	p. 36
Mapping Categorical Data to Numeric Values	p. 37
Working With Dates	p. 39
Working With Currency	p. 39
Working With Outliers and Anomalies	p. 39
Outlier Detection/Removal	p. 40
Finding Outliers With Numpy	p. 41
Finding Outliers With Pandas	p. 44
Calculating Z-Scores to Find Outliers	p. 46
Finding Outliers With SkLearn (Optional)	p. 48
Working With Missing Data	p. 49
Imputing Values: When Is Zero a Valid Value?	p. 50
Dealing With Imbalanced Datasets	p. 51
What Is SMOTE?	p. 52
SMOTE Extensions	p. 52
The Bias-Variance Tradeoff	p. 53
Types of Bias in Data	p. 54
Analyzing Classifiers (Optional)	p. 55
What Is LIME?	p. 55
What Is ANOVA?	p. 56
Summary	p. 56
Introduction to Pandas	p. 57
What Is Pandas?	p. 57
Pandas DataFrames	p. 58
Pandas Operations: In-place or Not?	p. 58
Data Frames and Data Cleaning Tasks	p. 58
A Pandas DataFrame Example	p. 59
Describing a Pandas Data Frame	p. 61
Pandas Boolean Data Frames	p. 63
Transposing a Pandas Data Frame	p. 63
Pandas Data Frames and Random Numbers	p. 64
Converting Categorical Data to Numeric Data	p. 66
Merging and Splitting Columns in Pandas	p. 69
Combining Pandas DataFrames	p. 71
Data Manipulation With Pandas DataFrames	p. 72
Pandas DataFrames and CSV Files	p. 73
Useful Options for the Pandas read_csv() Function	p. 76
Reading Selected Rows From CSV Files	p. 76
Pandas DataFrames and Excel Spreadsheets	p. 79
Useful Options for Reading Excel Spreadsheets	p. 80
Select, Add, and Delete Columns in Data frames	p. 80
Handling Outliers in Pandas	p. 82
Pandas DataFrames and Simple Statistics	p. 84
Finding Duplicate Rows in Pandas	p. 85
Finding Missing Values in Pandas	p. 87
Missing Values in Iris-Based Dataset	p. 90
Sorting Data Frames in Pandas	p. 93
Working With groupby() in Pandas	p. 94
Aggregate Operations With the titanic.csv Dataset	p. 96
Working With apply() and mapapply() in Pandas	p. 98
Useful One-Line Commands in Pandas	p. 101
Working With JSON-Based Data	p. 103
Python Dictionary and JSON	p. 103
Python, Pandas, and JSON	p. 104
Summary	p. 105
Introduction to RDBMS and SQL	p. 107
What Is an RDBMS?	p. 107
What Relationships Do Tables Have in an RDBMS?	p. 107
Features of an RDBMS	p. 108
What Is ACID?	p. 108
When Do We Need an RDBMS?	p. 109
The Importance of Normalization	p. 110
A Four-Table RDBMS	p. 111
Detailed Table Descriptions	p. 112
The customers Table	p. 112
The purchase_orders Table	p. 113
The line_items Table	p. 114
The item_desc Table	p. 115
What Is SQL?	p. 116
DCL, DDL? DQL, DML, and TCL	p. 117
SQL Privileges	p. 118
Properties of SQL Statements	p. 118
The CREATE Keyword	p. 119
What Is MySQL?	p. 119
What About MariaDB?	p. 119
Installing MySQL	p. 120
Data Types in MySQL	p. 120
The CHAR and VARCHAR Data Types	p. 120
String-Based Data Types	p. 121
FLOAT and DOUBLE Data Types	p. 121
BLOB and TEXT Data Types	p. 121
MySQL Database Operations	p. 122
Creating a Database	p. 122
Display a List of Databases	p. 122
Display a List of Database Users	p. 123
Dropping a Database	p. 123
Exporting a Database	p. 123
Renaming a Database	p. 124
The INFORMATION_SCHEMA Table	p. 125
The PROCESSLIST Table	p. 126
SQL Formatting Tools	p. 127
Summary	p. 127
Working with SQL and MySQL	p. 129
Create Database Tables	p. 130
Manually Creating Tables for mytools.com	p. 130
Creating Tables via an SQL Script for mytools.com	p. 131
Creating Tables With Japanese Test	p. 132
Creating Tables From the Command Line	p. 134
Drop Database Tables	p. 134
Dropping Tables via a SQL Script for mytools.com	p. 134
Altering Database Tables With the ALTER Keyword	p. 135
Add a Column to a Database Table	p. 135
Drop a Column From a Database Table	p. 137
Change the Data Type of a Column	p. 137
What Are Referential Constraints?	p. 139
Combining Data for a Table Update (Optional)	p. 139
Merging Data for a Table Update	p. 140
Appending Data to a Table From a CSV File	p. 141
Appending Table Data from CSV Files via SQL	p. 142
Inserting Data Into Tables	p. 144
Populating Tables From Text Files	p. 144
Working With Simple SELECT Statements	p. 146
Duplicate versus Distinct Rows	p. 148
Unique Rows	p. 148
The EXISTS Keyword	p. 148
The LIMIT Keyword	p. 149
DELETE, TRUNCATE, and DROP in SQL	p. 149
More Options for the DELETE Statement in SQL	p. 150
Creating Tables From Existing Tables in SQL	p. 150
Working With Temporary Tables in SQL	p. 151
Creating Copies of Existing Tables in SQL	p. 152
What Is an SQL Index?	p. 152
Types of Indexes	p. 152
Creating an Index	p. 153
Disabling and Enabling an Index	p. 153
View and Drop Indexes	p. 154
Overhead of Indexes	p. 155
Considerations for Defining Indexes	p. 155
Selecting Columns for an Index	p. 156
Finding Columns Included in Indexes	p. 157
Export Data From MySQL	p. 157
Export the Result Set of a SQL Query	p. 157
Export a Database or Its Contents	p. 157
Using LOAD DATA in MySQL	p. 158
Data Cleaning in SQL	p. 158
Replace NULL With 0	p. 159
Replace NULL Values With Average Value	p. 159
Replace Multiple Values With a Single Value	p. 161
Handle Mismatched Attribute Values	p. 162
Convert Strings to Date Values	p. 163
Data Cleaning From the Command Line (Optional)	p. 165
Working With the sed Utility	p. 165
Working With the awk Utility	p. 167
Summary	p. 169
NLP and Data Cleaning	p. 171
NLP Tasks in ML	p. 171
NLP Steps for Training a Model	p. 172
Text Normalization and Tokenization	p. 172
Word Tokenization in Japanese	p. 173
Text Tokenization With Unix Commands	p. 175
Handling Stop Words	p. 175
What Is Stemming?	p. 176
Singular versus Plural Word Endings	p. 176
Common Stemmers	p. 176
Stemmers and Word Prefixes	p. 177
Over Stemming and Under Stemming	p. 177
What Is Lemmatization?	p. 178
Stemming/Lemmatization Caveats	p. 178
Limitations of Stemming and Lemmatization	p. 178
Working With Text: POS	p. 179
POS Tagging	p. 179
POS Tagging Techniques	p. 179
Cleaning Data With Regular Expressions	p. 180
Cleaning Data With the cleantext Library	p. 184
Handling Contracted Words	p. 185
What Is BeautifulSoup?	p. 187
Web Scraping With Pure Regular Expressions	p. 190
What Is Scrapy?	p. 192
Summary	p. 193
Data Visualization	p. 195
What Is Data Visualization?	p. 195
Types of Data Visualization	p. 196
What Is Matplotlib?	p. 196
Lines in a Grid in Matplotlib	p. 197
A Colored Grid in Matplotlib	p. 198
Randomized Data Points in Matplotlib	p. 199
A Histogram in Matplotlib	p. 200
A Set of Line Segments in Matplotlib	p. 201
Plotting Multiple Lines in Matplotlib	p. 202
Trigonometric Functions in Matplotlib	p. 203
Display IQ Scores in Matplotlib	p. 204
Plot a Best-Fitting Line in Matplotlib	p. 204
The Iris Dataset in Sklearn	p. 206
Sklearn, Pandas, and the Iris Dataset	p. 207
Working With Seaborn	p. 209
Features of Seaborn	p. 210
Seaborn Built-in Datasets	p. 210
The Iris Dataset in Seaborn	p. 211
The Titanic Dataset in Seaborn	p. 212
Extracting Data From the Titanic Dataset in Seaborn (1)	p. 212
Extracting Data from Titanic Dataset in Seaborn (2)	p. 215
Visualizing a Pandas Dataset in Seaborn	p. 217
Data Visualization in Pandas	p. 219
What Is Bokeh?	p. 220
Summary	p. 222
Index	p. 223
Table of Contents provided by Ingram. All Rights Reserved.

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