With this 100% online Postgraduate diploma, you will acquire advanced skills in the management of Big Data, being able to process and analyze large volumes of information in real time” 

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent.

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea.

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

The program will cover fundamental topics such as Big Data management, real-time data processing and the application of AI algorithms to optimize trading strategies. Therefore, predictive analytics and Machine Learning methodologies will be examined, as well as data visualization to facilitate decision making. In addition, ethical and regulatory aspects related to the use of AI in the financial sphere will be addressed, ensuring that entrepreneurs understand the importance of complying with regulations and implementing responsible practices. 

The content of the Postgraduate diploma has been designed to provide you with in-depth and practical knowledge about Artificial Intelligence tools and techniques used in financial data analysis” 

Syllabus

The use of Artificial Intelligence in data processing and trading has revolutionized the financial sector, improving the efficiency and accuracy of investment decisions. AI tools, such as machine learning, are being used to analyze large volumes of data in real time, allowing traders to identify market patterns and trends that would be difficult to detect manually. 

This is how this Postgraduate diploma was created, which will offer comprehensive training focused on mastering Big Data technologies, enabling entrepreneurs to manage and process large-scale, real-time financial data. This approach will focus on the efficiency and speed of data analysis, prioritizing security and privacy. 

In this sense, professionals will acquire practical skills in the implementation of tools and techniques that facilitate the analysis of large volumes of data. 
Likewise, the ethical and regulatory aspects of Artificial Intelligence in the financial field will be analyzed. In this way, experts will be able to promote responsible practices and comply with current regulations, ensuring that the use of AI is carried out in an ethical and transparent manner. 

In this way, TECH has developed a university program in a 100% online format, allowing graduates to access the teaching materials from any electronic device with an Internet connection. This will eliminate the need to move to a physical location and to comply with fixed schedules. In addition, the revolutionary Relearning methodology will be used, focusing on the repetition of fundamental ideas to ensure a deep understanding of the content. 

This Postgraduate diploma takes place over 6 months and is divided into 3 modules:

Module 1. Large Scale Financial Data Processing
Module 2. Algorithmic Trading Strategies
Module 3. Ethical and Regulatory Aspects of Artificial Intelligence in Finance 

 

Where, When and How is it Taught?

TECH offers the possibility of developing this Postgraduate diploma in Data Processing and Trading with Artificial Intelligence fully online. Throughout the 6 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

Module 1. Large Scale Financial Data Processing

1.1. Big Data in the Financial Context

1.1.1. Key Characteristics of Big Data in Finance
1.1.2. Importance of the 5 Vs (Volume, Velocity, Variety, Veracity, Value) in Financial Data
1.1.3. Use Cases of Big Data in Risk Analysis and Compliance

1.2. Technologies for Storage and Management of Financial Big Data

1.2.1. NoSQL Database Systems for Financial Warehousing
1.2.2. Using Data Warehouses and Data Lakes in the Financial Sector
1.2.3. Comparison between On-Premises and Cloud-Based Solutions

1.3. Real-Time Processing Tools for Financial Data

1.3.1. Introduction to Tools such as Apache Kafka and Apache Storm
1.3.2. Real-Time Processing Applications for Fraud Detection
1.3.3. Benefits of Real-Time Processing in Algorithmic Trading

1.4. Integration and Data Cleaning in Finance

1.4.1. Methods and Tools for Integrating Data from Multiple Sources
1.4.2. Data Cleaning Techniques to Ensure Data Quality and Accuracy
1.4.3. Challenges in the Standardization of Financial Data

1.5. Data Mining Techniques Applied to The Financial Markets

1.5.1. Classification and Prediction Algorithms in Market Data
1.5.2. Sentiment Analysis in Social Networks for Predicting Market Movements
1.5.3. Data Mining to Identify Trading Patterns and Investor Behavior

1.6. Advanced Data Visualization for Financial Analysis

1.6.1. Visualization Tools and Software for Financial Data
1.6.2. Design of Interactive Dashboards for Market Monitoring
1.6.3. The Role of Visualization in Risk Analysis Communication

1.7. Use of Hadoop and Related Ecosystems in Finance

1.7.1. Key Components of the Hadoop Ecosystem and Their Application in Finance
1.7.2. Hadoop Use Cases for Large Transaction Volume Analysis
1.7.3. Advantages and Challenges of Integrating Hadoop into Existing Financial Infrastructures

1.8. Spark Applications in Financial Analytics

1.8.1. Spark for Real-Time and Batch Data Analytics
1.8.2. Predictive Model Building Using Spark MLlib
1.8.3. Integration of Spark with Other Big Data Tools in Finance

1.9. Data Security and Privacy in the Financial Sector

1.9.1. Data Protection Rules and Regulations (GDPR, CCPA)
1.9.2. Encryption and Access Management Strategies for Sensitive Data
1.9.3. Impact of Data Breaches on Financial Institutions

1.10. Impact of Cloud Computing on Large-Scale Financial Analysis

1.10.1. Advantages of the Cloud for Scalability and Efficiency in Financial Analysis
1.10.2. Comparison of Cloud Providers and Their Specific Financial Services
1.10.3. Case Studies on Migration to the Cloud in Large Financial Institutions

Module 2. Algorithmic Trading Strategies

2.1. Fundamentals of Algorithmic Trading

2.1.1. Algorithmic Trading Strategies
2.1.2. Key Technologies and Platforms for the Development of Algorithmic Trading Algorithms
2.1.3. Advantages and Challenges of Automated Trading versus Manual Trading

2.2. Design of Automated Trading Systems

2.2.1. Structure and Components of an Automated Trading System
2.2.2. Algorithm Programming: from the Idea to the Implementation
2.2.3. Latency and Hardware Considerations in Trading Systems

2.3. Backtesting and Evaluation of Trading Strategies

2.3.1. Methodologies for Effective Backtesting of Algorithmic Strategies
2.3.2. Importance of Quality Historical Data in Backtesting
2.3.3. Key Performance Indicators for Evaluating Trading Strategies

2.4. Optimizing Strategies with Machine Learning

2.4.1. Applying Supervised Learning Techniques in Strategy Improvement
2.4.2. Using Particle Swarm Optimization and Genetic Algorithms
2.4.3. Challenges of Overfitting in Trading Strategy Optimization

2.5. High Frequency Trading (HFT)

2.5.1. Principles and Technologies behind HFT
2.5.2. Impact of HFT on Market Liquidity and Volatility
2.5.3. Common HFT Strategies and Their Effectiveness

2.6. Order Execution Algorithms

2.6.1. Types of Execution Algorithms and Their Practical Application
2.6.2. Algorithms for Minimizing the Market Impact
2.6.3. Using Simulations to Improve Order Execution

2.7. Arbitration Strategies in Financial Markets

2.7.1. Statistical Arbitrage and Price Merger in Markets
2.7.2. Index and ETF Arbitrage
2.7.3. Technical and Legal Challenges of Arbitrage in Modern Trading

2.8. Risk Management in Algorithmic Trading

2.8.1. Risk Measures for Algorithmic Trading
2.8.2. Integrating Risk Limits and Stop-Loss in Algorithms
2.8.3. Specific Risks of Algorithmic Trading and How to Mitigate Them

2.9. Regulatory Aspects and Compliance in Algorithmic Trading

2.9.1. Global Regulations Impacting Algorithmic Trading
2.9.2. Regulatory Compliance and Reporting in an Automated Environment
2.9.3. Ethical Implications of Automated Trading

2.10. Future of Algorithmic Trading and Emerging Trends

2.10.1. Impact of Artificial Intelligence on the Future Development of Algorithmic Trading
2.10.2. New Blockchain Technologies and Their Application in Algorithmic Trading
2.10.3. Trends in Adaptability and Customization of Trading Algorithms

Module 3. Ethical and Regulatory Aspects of Artificial Intelligence in Finance 

3.1. Ethics in Artificial Intelligence Applied to Finance

3.1.1. Fundamental Ethical Principles for the Development and Use of AI in Finance
3.1.2. Case Studies on Ethical Dilemmas in Financial AI Applications
3.1.3. Developing Ethical Codes of Conduct for Financial Technology Professionals

3.2. Global Regulations Affecting the Use of AI in Financial Markets

3.2.1. Overview of the Main International Financial Regulations on AI
3.2.2. Comparison of AI Regulatory Policies among Different Jurisdictions
3.2.3. Implications of AI Regulation on Financial Innovation

3.3. Transparency and Explainability of AI Models in Finance

3.3.1. Importance of Transparency in AI Algorithms for User Confidence
3.3.2. Techniques and Tools to Improve the Explainability of AI Models
3.3.3. Challenges of Implementing Interpretable Models in Complex Financial Environments

3.4. Risk Management and Ethical Compliance in the Use of AI

3.4.1. Risk Mitigation Strategies Associated with the Deployment of AI in Finance
3.4.2. Ethics Compliance in the Development and Application of AI Technologies
3.4.3. Ethical Oversight and Audits of AI Systems in Financial Operations

3.5. Social and Economic Impact of AI in Financial Markets

3.5.1. Effects of AI on the Stability and Efficiency of Financial Markets
3.5.2. AI and Its Impact on Employment and Professional Skills in Finance
3.5.3. Benefits and Social Risks of Large-Scale Financial Automation

3.6. Data Privacy and Protection in AI Financial Applications

3.6.1. Data Privacy Regulations Applicable to AI Technologies in Finance
3.6.2. Personal Data Protection Techniques in AI-Based Financial Systems
3.6.3. Challenges in Managing Sensitive Data in Predictive and Analytics Models

3.7. Algorithmic Bias and Fairness in AI Financial Models

3.7.1. Identification and Mitigation of Bias in Financial AI Algorithms
3.7.2. Strategies to Ensure Fairness in Automated Decision-Making Models
3.7.3. Impact of Algorithmic Bias on Financial Inclusion and Equity

3.8. Challenges of Regulatory Oversight in Financial AI

3.8.1. Difficulties in the Supervision and Control of Advanced AI Technologies
3.8.2. Role of Financial Authorities in the Ongoing Supervision of AI
3.8.3. Need for Regulatory Adaptation in the Face of Advancing AI Technology

3.9. Strategies for Responsible Development of AI Technologies in Finance

3.9.1. Best Practices for Sustainable and Responsible AI Development in the Financial Sector
3.9.2. Initiatives and Frameworks for Ethical Assessment of AI Projects in Finance
3.9.3. Collaboration between Regulators and Businesses to Encourage Responsible Practices

3.10. Future of AI Regulation in the Financial Sector

3.10.1. Emerging Trends and Future Challenges in AI Regulation in Finance
3.10.2. Preparation of Legal Frameworks for Disruptive Innovations in Financial Technology
3.10.3. International Dialogue and Cooperation for Effective and Unified Regulation of AI in Finance

Take the opportunity to learn about the latest advances in this field in order to apply it to your daily practice"