Digital Garden
Search
Ctrl
+
K
Digital Garden
Search
Ctrl
+
K
01 Python Programming Language
Appendix
Common Keywords and Operators
Copying in Python
Debugging Tips
Python Comments
Python Data Type
Python Indexing
Python Iterables
Python Modules, Libraries, and Packages
Overview of Variables, Data Types and Data Structures
Type Conversions
Types of Errors
What is programming and Python?
Documentation
Data Structures
Python Dictionary
About Python Dictionaries
Accessing Key and Values in a Dictionary
Adding and Removing Items
Counting Occurrence of Words in a Text File
Counting Using Dictionaries
Indexing a Nested List
Looping Through Dictionaries
Sorting a Dictionary
Python Lists
Documentation
Lists
Changing Elements in a List
Concatenating Lists
Copying Lists
Creating a list
List Comprehension
Multidimensional Lists
List Methods
Using the `Insert()` Method
`join()` Method
Using the `sort()` Method
Using the `sorted()` Method
Adding Elements in a List
Removing Elements in a List
Python Sets
About Python Sets
Set Difference
Set Intersection
Set Union
Superset and Subsets
Symmetric Difference
Python Tuples
About Python Tuples
Accessing Tuple Elements
Tuple Unpacking
Data Types
Boolean Values and Expression
Python Strings
Python Classes and Inheritance
About Python Classes and Inheritance
Class Inheritance
Class vs Instance Variables
Creating a Class
Creating Subclasses
The __init__() Constructor Function
Python Conditionals
Python Conditional Statements
Comparison Operators
Logical Connectives
Nested Conditions
The IF Statement
The IF-ELIF-ELSE Statement
IF-ELSE Statement
Python File Handling
About Python File Handling
Closing a File
Exporting to a File
File Handling Using With
Getting the File
Writing to a File
Python Functions
About Python Functions
Arguments and Parameters
Docstrings
Function Compositions
Lambda Expressions
Local and Global Variables
Nested Functions
Return Values
User-defined functions
Python Loops
Python Loops
For Loops
Infinite Loops
Input-Controlled Loops
Keywords Within Loops
While Loops
Python Try-Except
About Try-Except
Additional Keywords
Raising an Exception
Useful Miscellaneous Codes
The Map() and Filter() Function
The Zip() Function
User Input
Python for Data Science
Data Manipulation
NumPy
Getting Started with NumPy
Pandas
Grouping & Aggregation
1. Group By Aggregation
2. Multi-Level Group By
Introduction
Getting Started with Pandas
Data Structures
Creating a DataFrame
Creating a Series
Indexing a DataFrame
6. Chained Indexing
Boolean Masking
Exporting a DataFrame to a CSV File
02 SQL Programming Language
Aggregate Functions
Untitled
COUNT Function
LISTAGG Function
MAX and MIN Functions
PERCENTILE_CONT Function
SUM Function
Appendix
_FAQ For SQL
Introduction to SQL
Terminology
Aliasing Attributes
Arthmetic Operations with SELECT
Checking for Missing Values
Creating Views (Virtual Tables)
Handling Ambiguous Attribute Names
Logical and Comparison Operators
Set Operations
USE Command
Wildcard Matching (Pattern Matching)
Basic SQL Statements
SQL CREATE SCHEMA Statement
SQL CREATE TABLE Statement
The DELETE Command
The GROUP BY Statement
The INSERT INTO Command
SQL JOIN Statements
The LIMIT Clause
The ORDER BY Keyword
SQL SELECT Statement
The TRUNCATE() Statement
The UPDATE Statement
The WHERE Clause (Dynamic)
SQL WHERE Clause
Complex Queries
Conditional Logic Using `Decode()`
Conditional Logic using CASE
Correlated Nested Queries
Keywords with Nested Queries
Generating Temp Result-set Using WITH
Loop Constructs
Multiway Joins
Nested Queries
Querying with JUNCTION Tables
Recursive Query
UNIONs vs JOINs
Using "Select 1 from" to Check Existence
Scalar Functions
The GREATEST() Function
Schema Changes
ALTER TABLE
DROP TABLE
SQL Constraints
Attribute Constraints
Attribute Domains
Candidate Keys
Foreign Keys
Primary Keys
Semantic Constraints
The CONSTRAINT Keyword
Useful SQL Functions
The Conditional Aggregation
The DECODE() Function
The FIRST_VALUE() Function
The LAG() Function
The NVL Function (Oracle)
The OFFSET Keyword
The OVER() and PARTITION BY() Function
WINDOW Functions
03 Statistical Applications
Additional Statistical Analyses
Statistical Power (Analysis)
Power Curves
Reporting Effect Sizes
Data Transformation Techniques
Kolmogorov-Smirnov Test
Levene's Test for Homogeneity of Variance
Shapiro-Wilk Test
Transforming Skewed Data
Winsorizing Outliers
Hypothesis Testing
_Introduction to Hypothesis Testing
Alternative Hypothesis Testing
Interpreting P-values
Null Sampling Distributions
Pitfalls to Statistial Analysis
Type I and Type II Errors
Understanding Confidence Intervals
Inferential Statistics
Analysis of Variances (ANOVA)
Introduction to ANCOVA
1. Introduction to ANOVAs
5. Bonferroni Correction
Interaction Plot
Post-Hoc Tests
One-way ANCOVA
2. One-way ANOVA
3. Two-way ANOVA
Welch's ANOVA
T_tests
Conducting t_tests
Confidence Interval for Population Mean (One-Sample t-test)
Computing the Confidence Interval (CI)
Effect Size
How to Compute the t-critical Values
One-Sample T-tests
Paired T-test
Two-Sample (Independent) T-tests
Welch's T-tests for Unequal Variances
Miscellaneous Code Snippets
Formatting Test Statistics and P-values in Python
Unpacking Lists for Statistical Tests
Python Modules
Introduction to SciPy
Introduction to statsmodel.api
Sampling Distributions
The F-Distribution
04 Applied Data Science in Python
Appendix
Ensemble Learning
Introduction to scikit-learn
Steps of Building a Model
Data Partitioning
K-Fold Cross-Validation
Partitioning the Dataset
Data Preprocessing
Feature Selection
Filter-based Method
Wrapper-Methods
Finding Optimal n Using RFECV
Importance of Feature Selection
Other
Dummy Encoding
Label Encoding
Normalizing or Standardizing Features
Ordinal Encoding
Exploratory Data Analysis
Displaying Dataset Description
Checking for Duplicate Rows
Checking for Missing Values
Getting Number of Unique Values
Checking for Samples and Target Sizes
Multicolinearity
Getting a Quick Statistical Summary
8. Univariate Analysis
9. Bivariate Analysis
10. Multivariate Analysis
11. Data Transformations
Model Selection and Hyperparameter Tuning
Finding the Best Model
GridSearchCV
05 Machine Learning Algorithms
Supervised ML Algorithms
Classification Algorithm
Decision Trees
About Decision Trees
Interpreting Decision Tree Output
Understanding Entropy and Information Gain
Running Decision Trees in sklearn
k-Nearest Neighbours
About kNN
Finding the Optimal k
Running k-NN with sklearn
Logit
About Logistic Regression
Classification Reports
Confusion Matrix
Running Logit in OLS
Naive Bayes
_About Naive Bayes
Gaussian Naive Bayes with sklearn
Regression Algorithm
About Linear Regression
Interpreting the LR Model
Performance Metrics for Regression Models
Regression Summary
Running Linear Regression with sklearn
Running Linear Regression
Simple vs Multiple Linear Regression
Visualizing Predicted vs Actual
_Appendix
I. Python Programming
II. Python for Data Science
III. General Data Science
IV. ML Models
V. Statistics
VI. SQL
Enter your search text in the box above
Select a result to preview
Getting Started with NumPy
Powered by Forestry.md