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01 Python Programming Language

Appendix

Common Keywords and Operators

Copying in Python

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

Changing Elements in a List

Concatenating 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 Intersection

Superset and Subsets

Symmetric Difference

Python Tuples

About Python Tuples

Accessing Tuple Elements

Tuple Unpacking

Data Types

Boolean Values and Expression

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

Exporting to a File

File Handling Using With

Getting the File

Writing to a File

Python Functions

About Python Functions

Arguments and Parameters

Function Compositions

Lambda Expressions

Local and Global Variables

Nested Functions

User-defined functions

Python Loops

Input-Controlled Loops

Keywords Within Loops

Python Try-Except

About Try-Except

Additional Keywords

Raising an Exception

Useful Miscellaneous Codes

The Map() and Filter() Function

The Zip() Function

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

LISTAGG Function

MAX and MIN Functions

PERCENTILE_CONT Function

Appendix

Introduction to SQL

Aliasing Attributes

Arthmetic Operations with SELECT

Checking for Missing Values

Creating Views (Virtual Tables)

Handling Ambiguous Attribute Names

Logical and Comparison Operators

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

Querying with JUNCTION Tables

Recursive Query

UNIONs vs JOINs

Using "Select 1 from" to Check Existence

Scalar Functions

The GREATEST() Function

Schema Changes

SQL Constraints

Attribute Constraints

Attribute Domains

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)

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

2. One-way ANOVA

3. Two-way ANOVA

T_tests

Conducting t_tests

Confidence Interval for Population Mean (One-Sample t-test)

Computing the Confidence Interval (CI)

How to Compute the t-critical Values

One-Sample T-tests

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

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

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

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

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DROP TABLE

tags: #sql/schema_changes

To drop a table from an existing database:

DROP TABLE table_name;

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