Python and SQL Bible : From Beginner to World Expert / Cuantum Technologies LLC, issuing body.

Format:

Book

Author/Creator:

LLC, Cuantum Technologies, Author.

Contributor:

Cuantum Technologies LLC, issuing body.

Language:

English

Subjects (All):

Computer programming.

Python (Computer program language).

SQL (Computer program language).

Physical Description:

1 online resource (519 pages)

Edition:

First edition.

Place of Publication:

Dallas, TX : Cuantum Technologies, [2023]

Biography/History:

LLC Cuantum Technologies: Cuantum Technologies is a leading innovator in the realm of software development and education, with a special focus on leveraging the power of Artificial Intelligence and cutting-edge technology. They specialize in web-based software development, authoring insightful programming and AI literature, and building captivating web experiences with the intricate use of HTML, CSS, JavaScript, and Three. js. Their diverse array of products includes CuantumAI, a pioneering SaaS offering, and an array of books spanning from Python, NLP, PHP, JavaScript, and beyond. Through their services, they are constantly striving to demystify AI and technology, making it accessible, understandable, and useable for all.

Summary:

Dive into comprehensive learning with Python and SQL Bible. This course covers everything from Python fundamentals to advanced SQL, empowering technical professionals with essential programming and data analysis skills.Key FeaturesComprehensive coverage of Python and SQL from basics to advanced techniques.Equip yourself with essential programming and data analysis skills for the tech industry.Learn through detailed explanations, interactive exercises, and real-world projects.Book DescriptionEmbark on a transformative journey with this course designed to equip you with robust Python and SQL skills. Starting with an introduction to Python, you'll delve into fundamental building blocks, control flow, functions, and object-oriented programming. As you progress, you'll master data structures, file I/O, exception handling, and the Python Standard Library, ensuring a solid foundation in Python. The course then transitions to SQL, beginning with an introduction and covering basics, and proceeding to advanced querying techniques. You'll learn about database administration and how Python integrates seamlessly with SQL, enhancing your data manipulation capabilities. By combining Python with SQLAlchemy, you'll perform advanced database operations and execute complex data analysis tasks, preparing you for real-world challenges. By the end of this course, you will have developed the expertise to utilize Python and SQL for scientific computing, data analysis, and database management. This comprehensive learning path ensures you can tackle diverse projects, from basic scripting to sophisticated data operations, making you a valuable asset in the tech industry. You'll also gain hands-on experience with real-world datasets, enhancing your problem-solving skills and boosting your confidence.What you will learnUnderstand and apply Python fundamentals.Master control flow and object-oriented programming in Python.Perform advanced SQL queries and database administration.Integrate Python with SQL for enhanced data manipulation.Conduct complex data analysis using Python and SQLAlchemy.Manage files and handle exceptions in Python effectively.Who this book is forThis course is ideal for a wide range of learners, including technical professionals, aspiring data scientists, software developers, and database administrators looking to enhance their skill set. It's perfect for beginners with little to no programming experience, as well as those with some background in coding who want to deepen their knowledge of Python and SQL. Additionally, it serves business analysts and IT professionals aiming to leverage data analysis and database management in their roles.

Contents:

Intro

Who we are

Our Philosophy:

Our Expertise:

Introduction

Chapter 1: Python: An Introduction

1.1 Brief History of Python

1.2 Benefits of Python

1.2.1 Readability and Simplicity

1.2.2 High-Level Language

1.2.3 Extensive Libraries

1.2.4 Cross-Platform Compatibility

1.2.5 Dynamically Typed

1.2.6 Support for Multiple Programming Paradigms

1.2.7 Strong Community and Widespread Adoption

1.2.8 Integration with Other Languages

1.2.9 Versatility

1.3 Python Applications

1.3.1 Web Development

1.3.2 Data Analysis and Data Visualization

1.3.3 Machine Learning and Artificial Intelligence

1.3.4 Game Development

1.3.5 Automation and Scripting

1.3.6 Cybersecurity

1.3.7 Internet of Things (IoT)

1.3.8 Robotics

1.3.9 Bioinformatics and Computational Biology

1.3.10 Education

1.4 Setting up the Python Environment and Writing Your First Python Program

1.4.1 Setting up Python Environment

1.4.2 Your First Python Program

Chapter 1 Conclusion

Chapter 2: Python Building Blocks

2.1 Python Syntax and Semantics

2.1.1 Python Syntax

2.1.2 Python Semantics

2.2 Variables and Data Types

2.2.1 Integers

2.2.2 Floating-Point Numbers

2.2.3 Strings

2.2.4 Booleans

2.2.5 Lists

2.2.6 Tuples

2.2.7 Dictionaries

2.2.8 Type Conversion

2.2.9 Dynamic Typing

2.2.10 Variable Scope

2.3 Basic Operators

2.3.1 Arithmetic Operators

2.3.1 Comparison Operators

2.3.2 Logical Operators

2.3.3 Assignment Operators

2.3.4 Bitwise Operators

2.3.5 Membership Operators

2.3.6 Identity Operators

2.3.6 Operator Precedence

2.4 Practice Exercises

Chapter 2 Conclusion

Chapter 3: Controlling the Flow

3.1 Control Structures in Python

3.1.1 Conditional Statements (if, elif, else)

3.1.2 Loop Structures (for, while).

3.2 Error and Exception Handling

3.2.1 Handling Exceptions with try and except

3.2.2 The else and finally Clauses

3.2.3 Raising Exceptions

3.2.4 The assert Statement

3.3 Understanding Iterables and Iterators

3.3.1 Iterators in Python

3.3.2 The for loop and Iterators

3.3.3 Iterators and Built-in Types

3.3.4 Python's itertools Module

3.3.5 Python Generators

3.4 Practice Exercises

Exercise 1: Conditional Statements

Exercise 2: Loops

Exercise 3: Error and Exception Handling

Exercise 4: Iterables and Iterators

Chapter 3 Conclusion

Chapter 4: Functions, Modules, and Packages

4.1 Function Definition and Call

4.1.1 Function Definition

4.1.2 Function Call

4.1.3 Function Parameters

4.1.4 Docstrings

4.1.5 Local and Global Variables

4.2 Scope of Variables

4.2.1 Global Scope

4.2.2 Local Scope

4.2.3 Nonlocal Scope

4.2.4 Built-In Scope

4.2.5 Best Practices for Variable Scope

4.3 Modules and Packages

4.3.1 Modules in Python

4.3.2 Packages in Python

4.3.3 Python's import system

4.4 Recursive Functions in Python

4.4.1 Understanding Recursion

4.4.2 Recursive Functions Must Have a Base Case

4.4.3 The Call Stack and Recursion

4.5 Practical Exercises

Exercise 1: Writing and Calling a Function

Exercise 2: Understanding Variable Scope

Exercise 3: Importing and Using a Module

Exercise 4: Recursive Function

Exercise 5: Error Handling

Chapter 4 Conclusion

Chapter 5: Deep Dive into Data Structures

5.1 Advanced Concepts on Lists, Tuples, Sets, and Dictionaries

5.1.1 Advanced Concepts on Lists

5.1.2 Advanced Concepts on Tuples

5.1.3 Advanced Concepts on Sets

5.1.4 Advanced Concepts on Dictionaries

5.1.5 Combining Different Data Structures

5.1.6 Immutable vs Mutable Data Structures

5.1.7 Iterating over Data Structures.

5.1.8 Other Built-in Functions for Data Structures

5.2 Implementing Data Structures (Stack, Queue, Linked List, etc.)

5.2.1 Stack

5.2.2 Queue

5.2.3 Linked Lists

5.2.4 Trees

5.3 Built-in Data Structure Functions and Methods

5.4 Python's Collections Module

5.5 Mutability and Immutability

5.6 Practical Exercises

Exercise 1: Implementing a Stack

Exercise 2: Implementing a Queue

Exercise 3: Using List Comprehensions

Exercise 4: Implementing a Linked List

Chapter 5 Conclusion

Chapter 6: Object-Oriented Programming in Python

6.1 Classes, Objects, and Inheritance

6.2 Polymorphism and Encapsulation

6.2.1 Polymorphism

6.2.2 Encapsulation

6.3 Python Special Functions

6.4 Abstract Base Classes (ABCs) in Python

6.4.1 ABCs with Built-in Types

6.5 Operator Overloading

6.6 Metaclasses in Python

6.7 Practical Exercises

Exercise 6.7.1: Class Definition and Object Creation

Exercise 6.7.2: Inheritance and Polymorphism

Exercise 6.7.3: Encapsulation

Chapter 6 Conclusion

Chapter 7: File I/O and Resource Management

7.1 File Operations

7.1.1 Opening a file

7.1.2 Exception handling during file operations

7.1.3 The with statement for better resource management

7.1.4 Working with Binary Files

7.1.5 Serialization with pickle

7.1.6 Working with Binary Files

7.1.7 Serialization with pickle

7.1.8 Handling File Paths

7.1.9 The pathlib Module

7.2 Context Managers

7.3 Directories and Filesystems

7.4 Working with Binary Data: The pickle and json modules

7.5 Working with Network Connections: The socket Module

7.6 Memory Management in Python

7.6.1 Reference Counting

7.6.2 Garbage Collection

7.7 Practical Exercises

Exercise 1

Exercise 2

Exercise 3

Chapter 7 Conclusion

Chapter 8: Exceptional Python

8.1 Error and Exception Handling.

8.1.1 Else Clause

8.1.2 Finally Clause

8.1.3 Custom Exceptions

8.2 Defining and Raising Custom Exceptions

8.2.1 Defining Custom Exceptions

8.2.2 Adding More Functionality to Custom Exceptions

8.2.3 Raising Custom Exceptions

8.3 Good practices related to raising and handling exceptions

8.4 Logging in Python

8.5 Practical Exercises

Exercise 1: Creating a custom exception

Exercise 2: Adding exception handling

Exercise 3: Logging

Exercise 4: Advanced logging

Chapter 8 Conclusion

Chapter 9: Python Standard Library

9.1 Overview of Python Standard Library

9.1.1 Text Processing Services

9.1.2 Binary Data Services

9.1.3 Data Types

9.1.4 Mathematical Modules

9.1.5 File and Directory Access

9.1.6 Functional Programming Modules

9.1.7 Data Persistence

9.1.8 Data Compression and Archiving

9.1.9 File Formats

9.2 Exploring Some Key Libraries

9.2.1 numpy

9.2.2 pandas

9.2.3 matplotlib

9.2.4 requests

9.2.5 flask

9.2.6 scipy

9.2.7 scikit-learn

9.2.8 beautifulsoup4

9.2.9 sqlalchemy

9.2.10 pytorch and tensorflow

9.3 Choosing the Right Libraries

9.3.1 Suitability for Task

9.3.2 Maturity and Stability

9.3.3 Community and Support

9.3.4 Documentation and Ease of Use

9.3.5 Performance

9.3.6 Community Support

9.4 Practical Exercises

Exercise 1: Exploring the Math Library

Exercise 2: Data Manipulation with Pandas

Exercise 3: File Operations with os and shutil Libraries

Chapter 9 Conclusion

Chapter 10: Python for Scientific Computing and Data Analysis

10.1 Introduction to NumPy, SciPy, and Matplotlib

10.1.1 Understanding NumPy Arrays

10.1.2 Efficient Mathematical Operations with NumPy

10.1.3 Linear Algebra with SciPy

10.1.4 Data Visualization with Matplotlib

10.2 Digging Deeper into NumPy.

10.2.1 Array slicing and indexing

10.2.2 Array reshaping and resizing

10.3 Working with SciPy

10.3.1 Optimization with SciPy

10.3.2 Statistics with SciPy

10.4 Visualizing Data with Matplotlib

10.4.1 Basic Plotting with Matplotlib

10.4.2 Creating Subplots

10.4.3 Plotting with Pandas

10.5 Exploring Pandas for Data Analysis

10.5.1 Creating a DataFrame

10.5.2 Data Selection

10.5.3 Data Manipulation

10.5.4 Reading Data from Files

10.6 Introduction to Scikit-Learn

10.7 Introduction to Statsmodels

10.8 Introduction to TensorFlow and PyTorch

10.9 Practical Exercises

Exercise 10.1

Exercise 10.2

Exercise 10.3

Exercise 10.4

Chapter 10: Conclusion

Chapter 11: Testing in Python

11.1 Unit Testing with unittest

11.1.1 setUp and tearDown

11.1.2 Test Discovery

11.1.3 Testing for Exceptions

11.2 Mocking and Patching

11.2.1 Mock and Side Effects

11.2.2 PyTest

11.3 Test-Driven Development

11.4 Doctest

11.5 Practical Exercises

Exercise 1: Unit Testing

Exercise 2: Mocking and Patching

Exercise 3: Test-Driven Development

Chapter 11 Conclusion

Chapter 12: Introduction to SQL

12.1 Brief History of SQL

12.2 SQL Syntax

12.2.1 Basic Query Structure

12.2.2 SQL Keywords

12.2.3 SQL Statements

12.2.4 SQL Expressions

12.3 SQL Data Types

12.3.1 Numeric Types

12.3.2 Date and Time Types

12.3.3 String Types

12.3.4 SQL Constraints

12.4 SQL Operations

12.4.1 Data Definition Language (DDL)

12.4.2 Data Manipulation Language (DML)

12.5 SQL Queries

12.5.1 Filtering with the WHERE clause

12.5.2 Sorting with the ORDER BY clause

12.5.3 Grouping with the GROUP BY clause

12.5.4 Joining Tables

12.6 Practical Exercises

Exercise 4

Exercise 5

Exercise 6

Exercise 7.

Chapter 12 Conclusion.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: LLC, Cuantum Technologies Python and SQL Bible

ISBN:

9781836206262

OCLC:

1463579768