Python is recognized for its adaptability, being used in applications ranging from web development to Artificial Intelligence. What are you waiting for to enroll?" 

Python is a high-level programming language, widely used among computer scientists, as it has a wide range of libraries and frameworks that simplify common tasks, allowing developers to focus on the logic of their applications instead of wasting time on technical details. Moreover, its versatility is another of its significant advantages, as it can be used in a variety of environments, from web development to data analysis and machine learning. 

This is the origin of this Professional master’s degree in Python Development , a program that will cover advanced data and type handling in Python, exploring identifiers, keywords, integral and Boolean types, as well as advanced strings formatting and encodings. Additionally, collections such as tuples, lists and dictionaries will be examined, along with iteration techniques and lambda functions, providing a solid foundation in the fundamentals of the language. 

Furthermore, you will delve into Python application development, with an emphasis on best practices and modern methodologies. From application architecture to deployment and maintenance, the course will cover aspects such as advanced design and modeling, dependency management, design patterns, testing and debugging, performance optimization, deployment and distribution strategies. 

Similarly, the computer scientist will delve into web and mobile development with Python, covering frameworkssuch as Django and Flask, as well as the development of APIs and web services. In addition, it will focus on interface design and user experience (UI/UX), from the use of design tools to improving accessibility and usability. Finally, data management and analysis will be addressed, using Python and tools such as NumPy, Pandas and Matplotlib. 

This program is presented as a unique opportunity, through a fully online and adaptable academic proposal. With this approach, professionals will enjoy greater freedom to manage their access time, allowing them to harmonize their daily personal and work commitments.

A comprehensive and specialized qualification in Python Development , which will prepare you to face real challenges in the world of Software Development"   

This Professional master’s degree in Python Development contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical cases presented by experts in Python Development 
• The graphic, schematic and practical contents of the book provide theoretical and practical information on those disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies  
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection 

Through this 100% online Professional master’s degree, you will delve into Frontend and Backend design, database management and app store publishing strategies"   

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.  

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.  

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.   

You will cover everything from the basics such as variables and data types, to the most advanced data visualization techniques and performance and storage optimization strategies. Enroll now!"

You'll delve into Object Oriented Programming (OOP), as well as topics such as classes, inheritance, polymorphism, abstract class creation and custom exceptions"

The syllabus has been meticulously designed to offer a comprehensive experience for graduates. From a deep dive into the syntax and advanced functionalities of Python, to specialization in Object Oriented Programming (OOP), web and mobile application design, and expert data management with libraries such as NumPy and Pandas, each module will be carefully structured to provide solid knowledge and practical skills. In addition, crucial topics such as user interface and user experience (UI/UX) design, advanced data handling, and performance and storage optimization will be explored. 

Not only will you gain complete mastery in Python, but you'll also prepare yourself to confidently tackle dynamic and tech industry challenges"  

Module 1. Python Programming

1.1. Creation and Execution of Python Programs

1.1.1. Configuration of the Development Environment
1.1.2. Execution of Python Scripts
1.1.3. Integrated Development Tools (IDEs)

1.2. Data in Python

1.2.1. Primitive Types (int, float, str)
1.2.2. Conversion and Casting of Data Types in Python
1.2.3. Immutability and Data Storage in Python

1.3. References to Objects in Python

1.3.1. References in Memory
1.3.2. Identity vs. Equality
1.3.3. Reference Management and Garbage Collection

1.4. Collection Data in Python

1.4.1. Common Lists and Operations
1.4.2. Tuples and their Immutability
1.4.3. Dictionaries and Data Access

1.5. Logical Operations in Python

1.5.1. Boolean Operators
1.5.2. Conditional Expressions
1.5.3. Short-Circuit Evaluation

1.6. Arithmetic Operators in Python

1.6.1. Arithmetic Operations in Python
1.6.2. Division Operators
1.6.3. Precedence and Associativity

1.7. Input/output in Python

1.7.1. Reading Data from Standard Input
1.7.2. Writing Data to Standard Output
1.7.3. File Handling

1.8. Creating and Calling Python Functions

1.8.1. Function Syntax
1.8.2. Parameters and Arguments
1.8.3. Return Values and Anonymous Functions

1.9. Using Strings in Python

1.9.1. Manipulating and Formatting Strings
1.9.2. Common Strings Methods
1.9.3. Interpolation and F-strings

1.10. Error and Exception Handling in Python

1.10.1. Common Types of Exceptions
1.10.2. Try-except Blocks
1.10.3. Creating Custom Exceptions

Module 2. Advanced Data and Flow Control with Python

2.1. Identifiers and Keywords in Python

2.1.1. Variable Naming Rules
2.1.2. Python Reserved Words
2.1.3. Naming Conventions

2.2. Integral and Boolean Types in Python

2.2.1. Integral Types
2.2.2. Boolean Specific Operations
2.2.3. Conversions and Representations

2.3. Floating-point Types and Complex Numbers in Python

2.3.1. Accuracy and Representation
2.3.2. Floating Point Operations
2.3.3. Use of Complex Numbers in Calculations

2.4. String Formatting and Encodings in Python

2.4.1. Advanced Formatting Methods
2.4.2. Unicode and UTF-8 Encodings
2.4.3. Working with Special Characters

2.5. Collections: Tuples, Lists and Dictionaries in Python

2.5.1. Comparing and Contrasting Types
2.5.2. Type-specific Methods
2.5.3. Efficiency and Selection of Suitable Type

2.6. Sets and Frozen Sets in Python

2.6.1. Creation and Operations in Sets
2.6.2. Frozen Sets
2.6.3. Practical Applications and Performance

2.7. Iterating and Copying Collections in Python

2.7.1. For Loops and List Comprehensions
2.7.2. Shallow vs. Deep
2.7.3. Iterators and Generators

2.8. Using Lambda Functions in Python

2.8.1. Syntax and Creation of Lambda Functions
2.8.2. Applications in Filters and Maps
2.8.3. Limitations and Best Practices

2.9. Control Structures: Conditionals and Loops in Python

2.9.1. If-else and Elif Structures
2.9.2. While and For Loops
2.9.3. Flow Control with Break, Continue and Else

2.10. Advanced Functions and Methods in Python

2.10.1. Recursive functions
2.10.2. Higher Order Functions
2.10.3. Function Decorators

Module 3. Object Oriented Programming (OOP) in Python

3.1. Object Oriented Programming (OOP) in Python

3.1.1. Classes and Objects
3.1.2. Encapsulation and Abstraction
3.1.3. Object Oriented Programming (OOP) in Python

3.2. Creation of Classes and Objects in Python

3.2.1. Classes in Python OOP
3.2.2. Instantiation and Initialization Methods
3.2.3. Attributes and Methods

3.3. Attributes and Methods in Python

3.3.1. Instance Attributes vs. Class
3.3.2. Instance, Class and Static Methods
3.3.3. Encapsulation and Information Hiding

3.4. Inheritance and Polymorphism in Python

3.4.1. Single and Multiple Inheritance
3.4.2. Overwriting and Method Extensions
3.4.3. Polymorphism and Duck Typing

3.5. Properties and Attribute Access in Python

3.5.1. Getters and Setters
3.5.2. Decorator @property
3.5.3. Access Control and Validation

3.6. Custom Classes and Collections in Python

3.6.1. Creating Collection Types
3.6.2. Special Methods (__len__, __getitem_,)
3.6.3. Custom Iterators

3.7. Aggregation and Composition in Python Classes

3.7.1. Relationships Between Classes
3.7.2. Aggregation vs. Composition
3.7.3. Object Lifecycle Management

3.8. Use of Decorators in Python Classes

3.8.1. Use of Decorators in Python Classes
3.8.2. Class Decorators
3.8.3. Applications and Use Cases

3.9. Abstract Classes and Methods in Python

3.9.1. Abstract Classes
3.9.2. Abstract Methods and Implementation
3.9.3. Use of ABC (Abstract Base Class)

3.10. Python OOP Exceptions and Error Handling

3.10.1. Custom Exceptions in Classes
3.10.2. Exception Handling in Methods
3.10.3. Best Practices in Exceptions and OOP

Module 4. Application Development in Python

4.1. Python Application Architecture

4.1.1. Software Design
4.1.2. Common Architectural Patterns
4.1.3. Requirements and Needs Assessment

4.2. Design and Modeling of Python Applications

4.2.1. Use of UML and Diagrams
4.2.2. Modeling Data and Information Flow
4.2.3. SOLID Principles and Modular Design

4.3. Dependency and Library Management in Python

4.3.1. Package Management with Pip
4.3.2. Use of Virtual Environments
4.3.3. Resolving Dependency Conflicts

4.4. Design Patterns in Python Development

4.4.1. Creative, Structural and Behavioral Patterns
4.4.2. Practical Application of Patterns
4.4.3. Refactoring and Patterns

4.5. Testing and Debugging in Python Applications

4.5.1. Testing Strategies (Unitary, Integration)
4.5.2. Use of Testing Frameworks
4.5.3. Debugging Techniques and Tools

4.6. Security and Authentication in Python

4.6.1. Application Security
4.6.2. Implementation of Authentication and Authorization
4.6.3. Vulnerability Prevention

4.7. Optimization and Performance of Python Applications

4.7.1. Performance Analysis
4.7.2. Code Optimization Techniques
4.7.3. Efficient Resource and Data Management

4.8. Deployment and Distribution of Python Applications

4.8.1. Deployment Strategies
4.8.2. Use of Containers and Orchestrators
4.8.3. Distribution and Continuous Updates

4.9. Maintenance and Updating in Python

4.9.1. Software Lifecycle Management
4.9.2. Maintenance and Refactoring Strategies
4.9.3. System Upgrade and Migration

4.10. Documentation and Technical Support in Python

4.10.1. Creating Effective Documentation
4.10.2. Documentation Tools
4.10.3. Strategies for Supporting and Communicating with Users

Module 5. Web and Mobile Development with Python

5.1. Web Development with Python

5.1.1. Structure and Components of a Website
5.1.2. Technologies in Web Development
5.1.3. Trends in Web Development

5.2. Popular Web Frameworks with Python

5.2.1. Django, Flask and Other Options
5.2.2. Frameworks Comparison and Selection
5.2.3. Frontend Integration

5.3. FrontEnd Development: HTML, CSS and JavaScript with Python

5.3.1. HTML and CSS
5.3.2. JavaScript and DOM Manipulation
5.3.3. Frameworks and Frontend Libraries

5.4. Backend and Databases with Python

5.4.1. Backend Development with Python
5.4.2. Relational and Non-relational Database Management
5.4.3. Backend-Frontend Integration

5.5. APIs and Web Services with Python

5.5.1. Designing RESTful APIs
5.5.2. Implementing and Documenting APIs
5.5.3. API Consumption and Security

5.6. Mobile Development with Python

5.6.1. Mobile Development Platforms (Native, Hybrid)
5.6.2. Development Tools and Environments
5.6.3. Adaptation of Applications for Mobile Devices

5.7. Mobile Development Platforms with Python

5.7.1. Android and IOS
5.7.2. Frameworks for Cross-development
5.7.3. Testing and Deployment on Mobile Devices

5.8. Design and UX in Mobile Applications with Python

5.8.1. Mobile Interface Design
5.8.2. Usability and User Experience with Python
5.8.3. Prototyping and Design Tools

5.9. Mobile Testing and Debugging with Python

5.9.1. Testing Strategies on Mobile Devices
5.9.2. Debugging and Monitoring Tools
5.9.3. Test automation

5.10. Publishing in Application Stores with Python

5.10.1. Publication Process in App Store and Google Play
5.10.2. Apps Compliance and Policies
5.10.3. Marketing and Promotion Strategies

Module 6. User Interface and User Experience with Python

6.1. User Interface Design with Python

6.1.1. UI Design with Python
6.1.2. User-computer Interaction with Python
6.1.3. User-centered Design with Python

6.2. UI/UX Design Tools with Python

6.2.1. Design and Prototyping Software
6.2.2. Collaboration and Feedback Tools
6.2.3. Integration of Design into the Development Process

6.3. Responsive and Adaptive Design with Python

6.3.1. Responsive Design Techniques
6.3.2. Adaptation to Different Devices and Screens
6.3.3. Testing and Quality Assurance

6.4. Animations and Transitions with Python

6.4.1. Creating Effective Animations with Python
6.4.2. Tools and Libraries for Animations
6.4.3. Impact on UX and Performance

6.5. Accessibility and Usability with Python

6.5.1. Web Accessibility
6.5.2. Evaluation Tools and Techniques
6.5.3. Implementation of Best Practices

6.6. Prototyping and Wireframes with Python

6.6.1. Wireframes and Mockups Creation
6.6.2. Rapid Prototyping Tools
6.6.3. Usability Testing and Feedback

6.7. Usability Testing with Python

6.7.1. Usability Testing Methods and Techniques
6.7.2. Results-based Analysis and Improvements
6.7.3. Usability Testing Tools

6.8. User Behavior Analysis with Python

6.8.1. Analysis and Tracking Techniques
6.8.2. Data Interpretation and Metrics
6.8.3. Continuous Improvement Based on Data

6.9. Feedback-based Enhancements with Python

6.9.1. Feedback Management and Analysis
6.9.2. Feedback Cycles and Continuous Improvement
6.9.3. Strategies for Implementing Effective Change

6.10. Future Trends in UI/UX with Python

6.10.1. Innovations and Emerging Trends
6.10.2. Impact of New Technologies on UI/UX
6.10.3. Preparing for the Future of Design

Module 7. Data Processing and Big Data with Python

7.1. Using Python on Data

7.1.1. Python in Data Science and Analysis
7.1.2. Essential Libraries for Data
7.1.3. Applications and Examples

7.2. Setting Up the Python Development Environment

7.2.1. Python Installation and Tools
7.2.2. Configuration of Virtual Environments
7.2.3. Integrated Development Tools (IDE)

7.3. Variables, Data Types and Operators in Python

7.3.1. Variables and Primitive Data Types
7.3.2. Data Structures
7.3.3. Arithmetic and Logical Operators

7.4. Flow Control: Conditionals and Loops

7.4.1. Conditional Control Structures (if, else, elif)
7.4.2. Loops (for, while) and Flow Control
7.4.3. List Comprehensions and Generator Expressions

7.5. Functions and Modularity with Python

7.5.1. Use of Functions
7.5.2. Parameters, Arguments and Return Values
7.5.3. Modularity and Code Reuse

7.6. Error and Exception Handling with Python

7.6.1. Errors and Exceptions
7.6.2. Exception Handling with Try-Except
7.6.3. Creating Custom Exceptions

7.7. Ipython Tool

7.7.1. Ipython Tool
7.7.2. Using IPythonfor Data Analysis
7.7.3. Differences with the Standard Python Interpreter

7.8. Jupyter Notebooks

7.8.1. Jupyter Notebooks
7.8.2. Use of Notebooks for Data Analysis
7.8.3. Publication of Jupyter Notebooks

7.9. Python Coding Best Practices

7.9.1. Style and Conventions (WBS 8)
7.9.2. Documentation and Comments
7.9.3. Testing and Debugging Strategies

7.10. Python Resources and Communities

7.10.1. Online Resources and Documentation
7.10.2. Communities and Forums
7.10.3. Learning and Updating in Python

Module 8. Data Structures and Functions in Python

8.1. Sets in Python

8.1.1. Operations and Methods
8.1.2. Differences and Practical Application
8.1.3. Iteration and Comprehensions

8.2. Dictionaries and their Use in Python

8.2.1. Dictionary Creation and Manipulation
8.2.2. Data Access and Management
8.2.3. Patterns and Advanced Techniques

8.3. List and Dictionary Comprehensions in Python

8.3.1. Syntax and Examples
8.3.2. Efficiency and Readability
8.3.3. Practical Applications

8.4. Functions on Data in Python

8.4.1. Creating Functions
8.4.2. Scope and Namespace
8.4.3. Anonymous and Lambda Functions

8.5. Function Arguments and Return Values in Python

8.5.1. Positional and Named Arguments
8.5.2. Multiple Return Values
8.5.3. Variable and Keyword Arguments

8.6. Lambda Functions and Higher-Order Functions in Python

8.6.1. Use of Lambda Functions
8.6.2. Map, Filter and Reduce Functions
8.6.3. Data Processing Applications

8.7. File Handling in Python

8.7.1. Reading and Writing Files
8.7.2. Handling Binary and Text Files
8.7.3. Best Practices and Exception Handling

8.8. Reading and Writing Text and Binary Files in Python

8.8.1. File Formats and Encoding
8.8.2. Handling Large Files
8.8.3. Serialization and Deserialization (JSON, pickle)

8.9. Contexts and File Operations

8.9.1. Using the Context Manager (with)
8.9.2. File Processing Techniques
8.9.3. Security and Error Handling

8.10. Python Modeling Libraries

8.10.1. Scikit-learn
8.10.2. TensorFlow
8.10.3. PyTorch

Module 9. Data Handling in Python with NumPy and Pandas

9.1. Creating and Manipulating Arrays in NumPy

9.1.1. NumPy
9.1.2. Basic Operations with Arrays
9.1.3. Arrays Manipulation and Transformation

9.2. Vectorized Operations with Arrays

9.2.1. Vectorization
9.2.2. Universal Functions (ufunc)
9.2.3. Efficiency and Performance

9.3. Indexing and Segmentation in NumPy

9.3.1. Access to Elements and Slicing
9.3.2. Advanced and Boolean Indexing
9.3.3. Reordering and Selection

9.4. Pandas Series and DataFrames

9.4.1. Pandas
9.4.2. Data Structures in Pandas
9.4.3. DataFrames Manipulation

9.5. Indexing and Selection in Pandas

9.5.1. Access to Data in Series and DataFrames
9.5.2. Selection and Filtering Methods
9.5.3. Use of loc e iloc

9.6. Operations with Pandas

9.6.1. Arithmetic Operations and Alignment
9.6.2. Aggregation and Statistics Functions
9.6.3. Transformations and Application of Functions

9.7. Handling Incomplete Data in Pandas

9.7.1. Detection and Handling of Null Values
9.7.2. Filling and Elimination of Incomplete Data
9.7.3. Strategies for Handling Incomplete Data

9.8. Strategies for Handling Incomplete Data

9.8.1. Concatenation and Data Merging
9.8.2. Grouping and Aggregation (groupby)
9.8.3. Pivot Tables and Crosstabs

9.9. Visualization with Matplotlib

9.9.1. Matplotlib
9.9.2. Graphics Creation and Customization
9.9.3. Integration with Pandas

9.10. Customizing Graphics in Matplotlib

9.10.1. Styles and Settings
9.10.2. Advanced Graphics (scatter, bar, etc.)
9.10.3. Creating Complex Visualizations

Module 10. Advanced Techniques and Practical Applications in NumPy and Pandas

10.1. Loading Data from Different Sources

10.1.1. Importing from CSV, Excel and Databases
10.1.2. Reading Data from APIs and Web
10.1.3. Big Data Management Strategies

10.2. Data Storage in Python

10.2.1. Exporting to Different Formats
10.2.2. Storage Efficiency
10.2.3. Data Security and Privacy

10.3. Data Cleansing Strategies in Python

10.3.1. Identification and Correction of Inconsistencies
10.3.2. Data Normalization and Transformation
10.3.3. Automation of Cleaning Processes

10.4. Advanced Data Transformation in Pandas

10.4.1. Manipulation and Transformation Techniques
10.4.2. Combining and Restructuring DataFrames
10.4.3. Use of Regular Expressions in Pandas

10.5. Combination of DataFrames in Pandas

10.5.1. Merge, Join and Concatenation
10.5.2. Handling of Conflicts and Keys
10.5.3. Efficient Combination Strategies

10.6. Advanced Transformation and Pivoting of Data in Pandas

10.6.1. Pivot and Melt
10.6.2. Reshaping and Transposition Techniques
10.6.3. Applications in Data Analysis

10.7. Time Series in Pandas

10.7.1. Handling of Dates and Times
10.7.2. Resampling and Window Functions
10.7.3. Trend and Seasonality Analysis

10.8. Advanced Index Management in Pandas

10.8.1. Multilevel and Hierarchical Indexes
10.8.2. Advanced Selection and Manipulation
10.8.3. Query Optimization

10.9. Performance Optimization Strategies

10.9.1. Speed and Efficiency Improvements
10.9.2. Use of Cython and Numba
10.9.3. Parallelization and Distributed Processing

10.10. Practical Data Manipulation Projects

10.10.1. Development of Real Examples of Use
10.10.2. Integration of Python Techniques
10.10.3. Strategies for Solving Complex Data Problems 

This innovative program will adapt to you to keep you up to date with the latest trends and technologies, guaranteeing you to be at the forefront of innovation in software development"