With this 100% online Postgraduate diploma, you will gain a solid foundation in the use of Python for data science and analysis, including the configuration of development environments and the use of essential libraries”

Data Analysis with Python is indispensable in business and science due, first, to its specialized libraries, such as Pandas, NumPy and Matplotlib, providing a robust and versatile platform to efficiently manipulate, visualize and analyze data. In addition, the active Python community is constantly contributing new libraries and resources, keeping pace with trends in data analysis. 

This is how this Postgraduate diploma was born, which will offer an extensive program focused on the development of key competencies for efficient data management and analysis. In this way, the professionals will focus on the fundamentals, covering from variables and data types, to control structures and best coding practices. 

Likewise, the computer scientist will delve into data structures and advanced functions, addressing file management and modeling techniques in Python. In this context, the practical application of structures, such as arrays and dictionaries, along with function handling and efficient file processing will be emphasized. Not to mention the advanced use of NumPy, Pandas and Matplotlib, providing advanced skills in arraysmanipulation, efficient structured data handling and advanced visualization techniques. 

Finally, the syllabus will address advanced data management with NumPy and Pandas, with a focus on performance and storage optimization strategies. In this way, data loading and warehousing from various sources, advanced cleansing and transformation strategies, as well as time series and complex data analysis will be covered. 

TECH will provide experts with an adaptable certification, giving them greater autonomy to manage their periods of participation, making it easier for them to reconcile their day-to-day responsibilities, whether personal or work-related. This method will be based on the Relearningmethodology, which involves the repetition of key concepts to enhance the assimilation of the contents. 

You will master Data Analytics with Python, streamlining the analysis process and improving the quality and interpretation of information, giving organizations a significant competitive advantage”

This Postgraduate diploma in Data Analysis with Python contains the most complete and up-to-date program on the market. The most important features include:

• Practical cases studies are presented by experts in Data Analysis with Python  
• 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 

From basic operations, to advanced visualization techniques, you will acquire skills to perform advanced data analysis and effective visualizations. What are you waiting for to enroll?"   

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 delve into advanced data management with NumPy and Pandas, with an emphasis on performance and storage optimization strategies, thanks to didactic resources at the forefront of technology and education"

Bet on TECH! You will cover fundamental aspects such as variables and control structures, as well as advanced techniques such as the use of IPython and Jupyter Notebooks"

The content has been meticulously designed, from essential fundamentals to advanced techniques, for professionals to acquire robust Python skills. Through immersion in vital libraries, such as NumPy, Pandas and Matplotlib, graduates will not only gain technical skills, but also develop the ability to approach complex challenges with creativity and confidence. In this regard, the program will also seek to cultivate an analytical mindset, foster best practices, and provide students with a deep understanding of how to apply these skills in real-world scenarios. 

Dive into the fascinating world of Data Analytics with Python, and equip yourself with the tools and insights needed to excel in a data-driven era”

Module 1. Data Processing and Big Data with Python 

1.1. Using Python on Data 

1.1.1. Python in Data Science and Analysis 
1.1.2. Essential Libraries for Data 
1.1.3. Applications and Examples 

1.2. Setting Up the Python Development Environment 

1.2.1. Python Installation and Tools  
1.2.2. Configuration of Virtual Environments 
1.2.3. Integrated Development Tools (IDE) 

1.3. Variables, Data Types and Operators in Python 

1.3.1. Variables and Primitive Data Types 
1.3.2. Data Structures 
1.3.3. Arithmetic and Logical Operators 

1.4. Flow Control: Conditionals and Loops 

1.4.1. Conditional Control Structures (if, else, elif) 
1.4.2. Loops (for, while) and Flow Control 
1.4.3. List Comprehensions and Generator Expressions 

1.5. Functions and Modularity with Python 

1.5.1. Use of Functions 
1.5.2. Parameters, Arguments and Return Values 
1.5.3. Modularity and Code Reuse 

1.6. Error and Exception Handling with Python 

1.6.1. Errors and Exceptions 
1.6.2. Exception Handling with Try-Except 
1.6.3. Creating Custom Exceptions 

1.7. IPython Tool 

1.7.1. IPython Tool 
1.7.2. Using IPython for Data Analysis 
1.7.3. Differences with the Standard Python Interpreter 

1.8. Jupyter Notebooks 

1.8.1. Jupyter Notebooks 
1.8.2. Use of Notebooks for Data Analysis 
1.8.3. Publication of Jupyter Notebooks 

1.9. Python Coding Best Practices 

1.9.1. Style and Conventions (WBS 8) 
1.9.2. Documentation and Comments 
1.9.3. Testing and Debugging Strategies 

1.10. Python Resources and Communities 

1.10.1. Online Resources and Documentation 
1.10.2. Communities and Forums 
1.10.3. Learning and Updating in Python 

Module 2. Data Structures and Functions in Python 

2.1. Sets in Python 

2.1.1. Operations and Methods 
2.1.2. Differences and Practical Application 
2.1.3. Iteration and Comprehensions 

2.2. Dictionaries and their Use in Python 

2.2.1. Dictionary Creation and Manipulation 
2.2.2. Data Access and Management 
2.2.3. Patterns and Advanced Techniques 

2.3. List and Dictionary Comprehensions in Python 

2.3.1. Syntax and Examples 
2.3.2. Efficiency and Readability 
2.3.3. Practical Applications 

2.4. Functions on Data in Python 

2.4.1. Creating Functions 
2.4.2. Scope and Namespace 
2.4.3. Anonymous and Lambda Functions 

2.5. Function Arguments and Return Values in Python 

2.5.1. Positional and Named Arguments  
2.5.2. Multiple Return Values 
2.5.3. Variable and Keyword Arguments 

2.6. Lambda Functions and Higher-Order Functions in Python 

2.6.1. Use of Lambda Functions 
2.6.2. Map, Filter and Reduce Functions 
2.6.3. Data Processing Applications 

2.7. File Handling in Python 

2.7.1. Reading and Writing Files 
2.7.2. Handling Binary and Text Files 
2.7.3. Best Practices and Exception Handling 

2.8. Reading and Writing Text and Binary Files in Python 

2.8.1. File Formats and Encoding 
2.8.2. Handling Large Files 
2.8.3. Serialization and Deserialization (JSON, pickle) 

2.9. Contexts and File Operations 

2.9.1. Using the Context Manager (with) 
2.9.2. File Processing Techniques 
2.9.3. Security and Error Handling 

2.10. Python Modeling Libraries 

2.10.1. Scikit-learn 
2.10.2. TensorFlow 
2.10.3. PyTorch 

Module 3. Data Handling in Python with NumPy and Pandas 

3.1. Creating and Manipulating Arrays in NumPy 

3.1.1. NumPy 
3.1.2. Basic Operations with Arrays 
3.1.3. Arrays Manipulation and Transformation 

3.2. Vectorized Operations with Arrays 

3.2.1. Vectorization 
3.2.2. Universal Functions (ufunc) 
3.2.3. Efficiency and Performance 

3.3. Indexing and Segmentation in NumPy 

3.3.1. Access to Elements and Slicing 
3.3.2. Advanced and Boolean Indexing 
3.3.3. Reordering and Selection 

3.4. Pandas Series and DataFrames 

3.4.1. Pandas 
3.4.2. Data Structures in Pandas 
3.4.3. DataFrames Manipulation 

3.5. Indexing and Selection in Pandas 

3.5.1. Access to Data in Series and DataFrames 
3.5.2. Selection and Filtering Methods 
3.5.3. Use of loc e iloc 

3.6. Operations with Pandas 

3.6.1. Arithmetic Operations and Alignment 
3.6.2. Aggregation and Statistics Functions 
3.6.3. Transformations and Application of Functions 

3.7. Handling Incomplete Data in Pandas 

3.7.1. Detection and Handling of Null Values 
3.7.2. Filling and Elimination of Incomplete Data 
3.7.3. Strategies for Handling Incomplete Data 

3.8. Strategies for Handling Incomplete Data 

3.8.1. Concatenation and Data Merging 
3.8.2. Grouping and Aggregation (groupby) 
3.8.3. Pivot Tables and Crosstabs 

3.9. Visualization with Matplotlib 

3.9.1. Matplotlib 
3.9.2. Graphics Creation and Customization 
3.9.3. Integration with Pandas 

3.10. Customizing Graphics in Matplotlib 

3.10.1. Styles and Settings 
3.10.2. Advanced Graphics (scatter, bar, etc.) 
3.10.3. Creating Complex Visualizations 

Module 4. Advanced Techniques and Practical Applications in NumPy and Pandas 

4.1. Loading Data from Different Sources 

4.1.1. Importing from CSV, Excel and Databases 
4.1.2. Reading Data from APIs and Web 
4.1.3. Big Data Management Strategies 

4.2. Data Storage in Python 

4.2.1. Exporting to Different Formats 
4.2.2. Storage Efficiency 
4.2.3. Data Security and Privacy 

4.3. Data Cleansing Strategies in Python 

4.3.1. Identification and Correction of Inconsistencies 
4.3.2. Data Normalization and Transformation 
4.3.3. Automation of Cleaning Processes 

4.4. Advanced Data Transformation in Pandas 

4.4.1. Manipulation and Transformation Techniques 
4.4.2. Combining and Restructuring DataFrames 
4.4.3. Use of Regular Expressions in Pandas 

4.5. Combination of DataFrames in Pandas  

4.5.1. Merge, Join and Concatenation 
4.5.2. Handling of Conflicts and Keys 
4.5.3. Efficient Combination Strategies 

4.6. Advanced Transformation and Pivoting of Data in Pandas  

4.6.1. Pivot and Melt 
4.6.2. Reshaping and Transposition Techniques 
4.6.3. Applications in Data Analysis 

4.7. Time Series in Pandas  

4.7.1. Handling of Dates and Times 
4.7.2. Resampling and Window Functions 
4.7.3. Trend and Seasonality Analysis 

4.8. Advanced Index Management in Pandas  

4.8.1. Multilevel and Hierarchical Indexes  
4.8.2. Advanced Selection and Manipulation 
4.8.3. Query Optimization 

4.9. Performance Optimization Strategies 

4.9.1. Speed and Efficiency Improvements 
4.9.2. Use of Cython and Numba 
4.9.3. Parallelization and Distributed Processing 

4.10. Practical Data Manipulation Projects 

4.10.1. Development of Real Examples of Use 
4.10.2. Integration of Python Techniques 
4.10.3. Strategies for Solving Complex Data Problems

This program represents not only an investment in knowledge, but an exciting opportunity to transform your full potential into Postgraduate diploma qualification”