Introduction to Blockchain and Cryptocurrencies:
History and evolution of blockchain technology and cryptocurrencies.
Fundamental principles of blockchain: cryptography, distributed consensus and smart contracts.
Types of cryptocurrencies and tokens, and their role in the financial ecosystem.

Advanced blockchain technology:
Blockchain architectures: public, private and permissioned.
Development of smart contracts and their integration with blockchain platforms.
Use of Ethereum, Binance Smart Chain, and other platforms for the creation of decentralized applications (dApps).

Decentralized Finance (DeFi):
Key DeFi concepts: decentralized lending, decentralized exchanges (DEX), and stablecoins.
Architecture and operation of DeFi protocols such as Uniswap, Compound, and Aave.
Risks and opportunities in the DeFi ecosystem: security, volatility, and regulation.

Smart Contracts and Decentralized Applications (dApps):
Development and deployment of smart contracts using Solidity and other programming languages.
Creation and management of decentralized applications for financial use cases.
Integration of oracles and scalability solutions to improve the efficiency of dApps.

Blockchain and DeFi security:
Main vulnerabilities and attacks in blockchain and DeFi systems: 51% attacks, exploits in smart contracts and phishing.
Strategies for auditing and securing smart contracts and DeFi platforms.
Best practices for protecting digital assets and data in the blockchain ecosystem.

Regulation and Compliance in DeFi:
Global and local regulatory framework for blockchain and DeFi: GDPR, AML/KYC, and financial licensing.
Legal and ethical challenges in the development and use of decentralized solutions.
Strategies to ensure compliance and regulatory risk mitigation.

Token Economics and Governance Models:
Token design and issuance: fungible (ERC-20) and non-fungible (NFTs).
Decentralized governance models and their implementation in DeFi protocols.
Analysis of token economics and its impact on the sustainability of DeFi projects.

Case Studies and Future Trends:
Analysis of success and failure cases in the DeFi and blockchain ecosystem.
Exploration of emerging trends and future innovations in blockchain technology.
Evaluation of the potential impact of blockchain and DeFi in industries such as finance, insurance, and real estate.

Final Certification Project:
Development of a hands-on project in which participants will design and deploy a DeFi solution or a dApp on a blockchain platform.
The project will include the implementation of smart contracts, the integration of DeFi functionalities and the evaluation of its performance and security.
Presentation of the project to a panel of blockchain and DeFi experts, with detailed feedback and recommendations for improvements.

Additional Benefits:

International Certification in Blockchain and DeFi:
Upon completion of the course, you will receive a prestigious certification that validates your proficiency in blockchain and decentralized finance, making you stand out in the competitive tech job market.

Access to Cutting-Edge Tools and Resources:
You’ll get access to blockchain development software, test environments and DeFi platforms for hands-on practice, along with exclusive educational resources.

Expert Mentoring and Global Networking:
You’ll have the opportunity to receive guidance from leaders in the field, participate in one-on-one mentoring sessions, and connect with a global network of blockchain professionals and entrepreneurs.

Course Duration: 10 months

Modality: 100% online, with live classes, hands-on labs, and access to recorded content.

Fundamentals of Advanced Artificial Intelligence:
Review of key concepts in AI and machine learning.
Deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN) architectures.
Introduction to development frameworks such as TensorFlow, PyTorch, and Keras.

Deep Neural Networks and Complex Architectures:
Design and training of deep neural networks for complex classification and regression tasks.
Implementation of advanced architectures such as ResNet, Inception and GANs (Generative Adversarial Networks).
Regularization and optimization techniques to improve model performance.

Reinforcement Learning:
Basic concepts of Reinforcement Learning and its application in autonomous decision making.
Implementation of algorithms such as Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO).
Use cases in robotics, gaming, and recommender systems.

Generative Models and GANs:
In-depth study of the design and implementation of generative adversarial networks (GANs).
Advanced applications of GANs in the creation of images, text, and music.
Techniques for training stability and improvements in the quality of generations.

Advanced Natural Language Processing (NLP):
Advanced techniques in NLP: Transformers, BERT, GPT, and other language models.
Application of NLP in chatbots, sentiment analysis, and machine translation.
Development of language models and their integration in conversational AI applications.

Computer Vision:
Implementation of advanced computer vision techniques for object recognition, segmentation, and face detection.
Use of CNNs in image classification, anomaly detection, and video analysis tasks.
Industrial applications in security, automotive and manufacturing.

Explainable AI systems and AI ethics:
Developing AI models that are interpretable and transparent.
Techniques for making AI systems understandable to humans, including LIME and SHAP.
Ethical considerations in advanced AI development: bias, privacy, and liability.

Optimization and Scalability of AI Systems:
Strategies for optimizing AI models for large data volumes and high availability.
Implementation of AI in production environments: DevOps for AI, and use of cloud computing.
Scalability and performance: techniques to improve computational efficiency.

Final Specialization Project:
Participants will develop a complete advanced AI project, applying all the knowledge acquired.
The project will include the definition of a complex problem, model design, implementation and performance evaluation.
Presentation of the project to a panel of AI experts, followed by detailed analysis and recommendations for improvement.

Additional Benefits:

Advanced Artificial Intelligence Certification:
Upon completion of this course, you will receive a prestigious certification that endorses your skills in designing and implementing advanced AI solutions, positioning you as a leader in the technology industry.

Access to Exclusive Resources and Cutting-Edge Software:
During the course, you will have access to software licenses, exclusive datasets, and educational resources that will allow you to practice and hone your skills.

Expert Mentoring and Global Collaboration Opportunities:
Connect with AI experts from around the world, participate in one-on-one mentoring sessions, and collaborate on high-impact international projects.

Course Duration: 12 months

Modality: 100% online, with live classes, virtual labs, and access to recorded content.

Fundamentals of Data Analytics:
Introduction to data analytics: key concepts and their relevance in the modern business environment.
Data analytics life cycle: collection, processing, analysis, interpretation and communication of results.
Data management principles: data quality, cleaning and preparation for analysis.

Data Analysis Tools and Techniques:
Advanced use of tools such as Excel, SQL and Python for the analysis of large volumes of data.
Introduction to R and SAS for statistical analysis and data modeling.
Data mining techniques: clustering, regression, and time series analysis.

Data visualization:
Principles of data visualization: how to tell a story with data using graphs, tables, and dashboards.
Use of visualization tools such as Tableau and Power BI to create interactive reports and executive presentations.
Best practices in communicating insights to non-technical audiences.

Predictive Modeling and Machine Learning:
Introduction to machine learning and its application in business data analytics.
Development and validation of predictive models: linear regression, decision trees and classification models.
Implementation of machine learning algorithms for forecasting and customer segmentation.

Advanced Data Analysis:
Advanced analysis techniques: cohort analysis, basket analysis and recommendation models.
Sentiment analysis and natural language processing (NLP) for the interpretation of unstructured data.
Use of big data in decision making: case studies in sectors such as finance, retail and healthcare.

Data Analytics Project Management:
Design and implementation of data analytics projects in a business environment.
Management of data analytics teams: roles and responsibilities, agile methodologies.
Integration of data analytics in corporate strategy and its impact on competitiveness.

Ethics and Privacy in Data Analytics:
Ethical considerations in data collection and use: privacy, consent and security.
Compliance with regulations such as GDPR and CCPA in handling personal data.
Strategies to protect data integrity and avoid bias in analysis.

Final Certification Project:
Participants will apply everything they have learned to develop a real data analytics project.
The project will include data collection and preparation, advanced analysis, creation of visualizations and presentation of insights to a panel of experts.
Evaluation of the impact of the analysis performed and strategic recommendations based on the results.

Additional Benefits:

International Certification in Data Analytics:
Upon completion of the course, you will obtain a globally recognized certification that endorses your competencies in data analytics, making you a highly demanded professional in the market.

Access to Tools and Specialized Software:
During the course, you will have access to data analytics software licenses such as Tableau, Power BI, and machine learning tools for practical use.

Personalized Mentoring and Networking Opportunities:
Connect with experts in the field of data analytics, receive personalized career guidance, and participate in exclusive networking events.

Course Duration: 9 months

Modality: 100% online, with live classes, hands-on labs, and access to recorded content.

Introduction to Sustainable Finance:
Definition and scope of sustainable finance in the global context.
Evolution and trends in the sustainable investment market.
Regulatory framework and international standards that promote sustainable finance, such as the Principles for Responsible Investment (PRI) and the United Nations Global Compact.

Environmental, Social and Governance (ESG) criteria:
Analysis and evaluation of ESG criteria in financial decision making.
Tools and metrics for measuring environmental, social and governance impact.
Successful cases of ESG integration in investment portfolios.

Socially Responsible Investments (SRI):
Sustainable investment strategies: negative screening, positive screening, and active engagement.
SRI investment funds: types, characteristics and financial performance.
Return and risk analysis in sustainable investments versus traditional investments.

Green Bonds and Climate Finance:
Introduction to green bonds, social bonds and other sustainable debt instruments.
Green bond issuance and certification process.
Analysis of case studies on financing renewable energy, energy efficiency, and urban sustainability projects.

Risk Management in Sustainable Finance:
Identification and assessment of sustainability-related risks, including climate risk.
ESG risk mitigation strategies in portfolio and project management.
Risk analysis models that incorporate sustainability criteria.

Corporate Strategies and Sustainable Finance:
Integration of sustainability into corporate strategy and the impact on long-term value creation.
Evaluation of sustainable investment projects: tools and methodologies.
Corporate governance and the importance of transparency and accountability in sustainable finance.

Financial Innovation for Sustainability:
Innovations in sustainable financial products, such as sustainability-linked lending and impact funds.
Role of technology and fintech in promoting sustainable finance.
Opportunities and challenges in creating new sustainable financial markets.

Social Impact and Performance Measurement:
Methodologies for assessing the social and environmental impact of investments.
Use of key performance indicators (KPIs) to measure the success of sustainable strategies.
Sustainability reporting and compliance with international standards.

Final Project and Implementation Plan:
Participants will develop a strategic sustainable finance plan for an organization, focusing on ESG integration, risk management and profitability optimization.
Presentation of the project to a panel of sustainable finance experts, followed by detailed feedback.
Preparation of a final report reflecting findings and recommendations for implementation of the plan.

Additional Benefits:

Certification in Sustainable Finance:
Upon completion of the course, you will receive an internationally recognized certification that endorses your competencies in sustainable finance and your ability to lead projects in this area.

Access to Specialized Tools and Resources:
You will gain access to ESG analysis platforms, sustainability databases, and exclusive learning resources to continue honing your skills.

Mentoring and Global Networking:
You will have the opportunity to interact with global leaders in sustainable finance, participate in discussion forums, and attend networking events to expand your professional network.

Course Duration: 8 months

Modality: 100% online, with live sessions, analysis of real case studies and access to recorded content.

Fundamentals of Financial Risk Management:
Introduction to financial risk management and its importance in business strategy.
Basic principles of market risk, credit risk, liquidity risk and operational risk.
International regulations and standards in risk management, such as Basel III and COSO.

Risk assessment and quantification:
Risk assessment methods: qualitative and quantitative analysis.
Risk quantification models: Value at Risk (VaR), Expected Shortfall, and stress models.
Tools and software for financial risk assessment.

Market Risk and Hedging Models:
Market risk analysis: types of risks, including foreign exchange, interest rate and commodity risks.
Hedging strategies using financial derivatives: forwards, futures, options and swaps.
Portfolio risk management and investment optimization under uncertainty conditions.

Credit Risk and Credit Portfolio Management:
Credit risk analysis: creditworthiness assessment and credit rating.
Credit risk models: Merton, CreditMetrics, and CreditRisk+.
Credit risk mitigation strategies: guarantees, securitization and use of CDS (Credit Default Swaps).

Operational Risk and Business Continuity Management:
Identification and assessment of operational risk: process failures, human error, and fraud.
Implementation of internal controls and operational risk management policies.
Business continuity planning and crisis management.

Liquidity Risk and Management Strategies:
Liquidity risk analysis: causes and effects on financial stability.
Liquidity risk management strategies: cash flow management and liquidity reserves.
Liquidity risk regulation: Basel III requirements and key ratios such as LCR and NSFR.

Integrated Risk Management (ERM):
Development of an Enterprise Risk Management (ERM) framework.
Strategies for integrating risk management into strategic planning.
Case studies on the successful implementation of ERM in global organizations.

Risk in Emerging and Global Markets:
Specific challenges in risk management in emerging markets.
Assessment of political, economic and governance risks in international contexts.
Global risk mitigation strategies for multinational and exporting companies.

Final Project and Risk Management Simulation:
Participants will develop a final project consisting of risk identification and mitigation in a real or simulated case study.
Simulation of a financial crisis and development of an action plan to mitigate the impact on the organization.
Presentation of the project to a panel of experts and evaluation of the proposed solutions.

Additional Benefits:

Certification in Financial Risk Management:
Upon completion of the course, you will receive a certification that demonstrates your mastery of financial risk management, endorsed by prestigious financial institutions.

Access to Specialized Tools and Resources:
You’ll get access to risk management software licenses, real case studies, and exclusive reference material to strengthen your knowledge.

Mentoring and Networking:
During the course, you will have the opportunity to interact with leaders in the financial industry, participate in webinars and networking events, and receive guidance for your career in risk management.

Course Duration: 6 months

Modality: 100% online, with live sessions, practical simulations and access to recorded content.

Introduction to Process Automation and RPA:
Definition and scope of RPA in the business context.
Benefits and challenges of process automation.
Comparison between RPA and other automation technologies, such as BPM (Business Process Management) and AI.

RPA fundamentals:
Basic principles of RPA and how bots work.
Architecture and components of an RPA platform.
Common use cases of RPA in different industries, such as finance, human resources, and customer service.

Identification of Processes for Automation:
Criteria for selecting suitable processes for RPA.
Return on Investment (ROI) analysis of automation projects.
Documentation and mapping of business processes for automation.

Development of Bots with RPA Tools:
Introduction to leading RPA tools such as UiPath, Automation Anywhere and Blue Prism.
Design and development of bots: from data capture to automation of complex tasks.
Implementation of RPA in cloud and hybrid environments.

Integration of RPA with Existing Systems:
Connectivity of bots with ERP systems, CRM and other enterprise software.
Use of APIs and scripts to extend RPA capabilities.
Automation of processes involving multiple systems and platforms.

RPA Project Management:
Agile methodologies for RPA implementation.
Change management and RPA adoption strategies in the organization.
Monitoring, maintenance and scalability of bots in production.

Security and Governance in RPA:
Security considerations in the implementation of bots.
Regulatory compliance and risk management in RPA projects.
Governance and best practices in managing automation environments.

Impact of RPA on the Workforce:
Changes in job roles and responsibilities due to automation.
Strategies for retraining and relocation of employees affected by RPA.
Future of Work: how RPA and other emerging technologies are transforming organizations.

Final Project:
Development of a real automation project, where participants will identify a process in their organization, design an RPA bot and implement it in a simulated or real environment.
Presentation and evaluation of the project by a panel of RPA experts.

Additional Benefits:

Certification in Process Automation with RPA:
Upon completion of the course, you will receive a recognized certification that validates your ability to design and implement RPA solutions in enterprise environments.

Access to Specialized Tools and Resources:
You’ll get access to RPA software licenses, process documentation templates, and additional learning resources to hone your skills.

Mentoring and Networking:
During the course, you will have the opportunity to interact with automation experts, participate in webinars and networking events, and receive guidance for your RPA projects.

Course Duration: 6 months

Modality: 100% online, with live sessions, hands-on labs and access to recorded content.

Introduction to Data Science in the Business Context:
Definition and scope of data science applied to business.
Importance of data culture in decision making.
Differences between business intelligence, data analysis and data science.

Fundamentals of Statistics and Data Analysis:
Basic principles of descriptive and inferential statistics applied to business data.
Data exploration: cleaning, normalization and preparation of datasets for analysis.
Introduction to R and Python for business data analysis.

Predictive Models and Machine Learning:
Supervised machine learning techniques: linear regression, decision trees, and Random Forest.
Unsupervised algorithms: clustering, principal component analysis (PCA) and k-means.
Implementation of predictive models for sales forecasting, customer segmentation and process optimization.

Data Visualization for Decision Making:
Creation of enterprise dashboards using tools such as Tableau and Power BI.
Advanced visualization techniques to communicate key insights to stakeholders.
Storytelling with data: how to build effective narratives based on data analysis.

Real-Time Data Analysis:
Introduction to big data analytics and real-time processing technologies.
Implementation of data pipelines for real-time analytics with Apache Spark and Kafka.
Applications of real-time analytics in marketing, finance and operations.

Data Science for Marketing and Sales:
Advanced customer segmentation and behavioral analysis using clustering and classification.
Optimization of marketing campaigns with attribution techniques and propensity models.
Churn prediction and customer retention strategies.

Data Science in Finance and Operations:
Models for financial risk prediction and credit scoring.
Supply chain optimization using prescriptive analytics.
Application of time series models for demand forecasting and inventory planning.

Ethical and Privacy Issues in Data Science:
Ethical considerations in the use of business and personal data.
Compliance with regulations such as GDPR and CCPA in data handling.
Biases in predictive models and how to mitigate them in business decisions.

Final Project:
Development of a comprehensive project where participants will apply the data science techniques learned to solve a real business problem, from conceptualization to implementation.
Presentation of the final project to a panel of experts and evaluation of its potential impact in a business context.

Additional Benefits:

Certification in Data Science Applied to Business:
Upon completion of the course, you will receive a certification that validates your ability to apply data science to strategic business decision making.

Access to Advanced Tools and Resources:
You’ll get access to data analytics software licenses, real-world case study datasets, and business dashboard templates.

Mentoring and Networking:
During the course, you will have the opportunity to interact with data science leaders and industry experts, as well as participate in networking events.

Course Duration: 5 months

Modality: 100% online, with live sessions, practical exercises and access to recorded content.

Fundamentals of Natural Language Processing:
Introduction to NLP and its importance in artificial intelligence.
Basic principles of computational linguistics and language representation.
Statistical and rule-based models for text processing.

Textual Data Preprocessing:
Text cleaning and normalization techniques: tokenization, stemming, lemmatization.
Stop words removal and missing data handling.
Text representation: Bag of Words (Bag of Words), TF-IDF, and Word Embeddings (Word2Vec, GloVe).

Classical NLP models:
Markov and n-gram models for text generation.
Text classification with supervised algorithms such as Naive Bayes, SVM and Random Forest.
Sentiment analysis, spam detection and topic classification.

Deep Learning for NLP:
Recurrent neural networks (RNN) and LSTM for text sequences.
Transformers and BERT: Pretrained models and their adaptation to specific tasks.
GPT (Generative Pre-trained Transformer) applications in text generation and chatbots.

Speech Processing and Speech Recognition:
Introduction to speech signal processing.
Techniques for automatic speech recognition (ASR) and speech synthesis (TTS).
Application of NLP in virtual assistants and dialog systems.

Advanced NLP applications:
Machine Translation (Machine Translation) and its implementation with sequence-to-sequence models.
Text Summarization and summary generation.
Named Entity Recognition (NER) and analysis of relationships between entities.

Ethical Challenges and Considerations in NLP:
Biases in NLP models and how to mitigate them.
Privacy and security in handling textual and speech data.
Ethical considerations in the implementation of NLP technologies.

Final Project:
Development of a comprehensive project in NLP, which may include from the creation of an intelligent chatbot to a sentiment analysis system in social networks.
Presentation and evaluation of the project with feedback from NLP experts.

Additional Benefits:

Certification in Natural Language Processing (NLP):
Upon completion of the course, you will receive a recognized certification that validates your proficiency in advanced NLP techniques, prized in technology industries.

Access to Specialized Tools and Resources:
You’ll gain access to NLP libraries and platforms, exclusive datasets and development environments optimized for language processing.

Mentoring and Networking:
During the course, you will be able to interact with NLP practitioners and experts, participate in webinars and networking events, and receive mentoring for your projects.

Course Duration: 4 months

Modality: 100% online, with live sessions, practical exercises and access to recorded content.

Introduction to Blockchain and Cryptocurrencies:
History and evolution of blockchain and cryptocurrencies.
Key differences between blockchain and traditional database technologies.
Overview of Bitcoin, Ethereum, and other major cryptocurrencies.

Technical fundamentals of blockchain:
Structure of a blockchain: blocks, hashing, and mining.
Decentralized consensus: Proof of Work (PoW), Proof of Stake (PoS), and other algorithms.
Concept of smart contracts and their implementation in platforms such as Ethereum.

How cryptocurrencies work:
How cryptocurrencies are created, stored and transacted.
Cryptocurrency security: private keys, digital wallets, and protection measures.
Cryptocurrency market analysis: volatility, market capitalization and trends.

Practical applications of Blockchain:
Use cases in different industries: finance, logistics, healthcare, and more.
Exploration of decentralized applications (DApps) and decentralized finance (DeFi).
Impact of blockchain on digital identity, supply chain, and intellectual property.

Regulatory and Legal Aspects:
Current regulations on cryptocurrencies and blockchain globally.
Legal considerations on the adoption of cryptocurrencies and the creation of ICOs (Initial Coin Offerings).
Regulatory compliance and legal challenges in the implementation of blockchain in companies.

Challenges and Opportunities in Blockchain:
Scalability and efficiency in blockchain networks.
Decentralization vs. centralization: debates and hybrid models.
The future of cryptocurrencies and blockchain: emerging trends and possible scenarios.

Cryptocurrency investments:
Investment strategies in cryptocurrencies: trading, holding, and staking.
Evaluation of risks and opportunities in the cryptocurrency market.
Tools and platforms to manage cryptocurrency investments.

Final Project:
Development of a practical project where participants will design and implement a simple blockchain application or create a cryptocurrency investment portfolio based on market analysis.
Presentation of the project and discussion of its feasibility and potential impact.

Additional Benefits:

Certification in Blockchain and Cryptocurrency Fundamentals:
Upon completion of the course, you will receive a recognized certification that validates your knowledge and skills in blockchain and cryptocurrencies.

Access to Specialized Tools and Resources:
You’ll get access to blockchain development platforms, cryptocurrency trading simulators, and additional educational resources.

Mentoring and Networking:
You will have the opportunity to interact with blockchain and cryptocurrency experts, and participate in networking events with other professionals interested in this technology.

Course Duration: 4 months

Modality: 100% online, with live sessions and access to recorded content.

Introduction to Tableau and its Ecosystem:
Overview of Tableau and its applications in data visualization.
Setting up the working environment and understanding the Tableau interface.
Exploring the different versions of Tableau: Tableau Desktop, Tableau Online and Tableau Server.

Connecting and Preparing Data:
Connecting to various data sources, including Excel files, SQL, cloud databases, and APIs.
Data preparation and cleansing techniques using Tableau Prep.
Data management in Tableau: joining, blending, and filtering data to create reliable data sources.

Fundamentals of Data Visualization:
Principles of designing effective visualizations, including choice of charts, colors, and layouts.
Creating basic charts in Tableau: bar charts, line charts, scatter charts, heat maps, and geographic maps.
Customizing visualizations: titles, labels, legends, and annotations.

Interactive Dashboards and Storytelling with Data:
Design and development of interactive dashboards that allow users to explore data and discover key insights.
Integration of multiple visualizations into a single dashboard, linking charts through actions and filters.
Creating stories with Tableau Story to present data in a narrative and sequential way.

Advanced Analysis with Tableau:
Use of advanced Tableau features, such as table calculations, level of detail (LOD) and parameter calculations.
Application of advanced analytics, such as customer segmentation, cohort analysis, and trend analysis.
Techniques for predictive analytics and data forecasting in Tableau.

Integration of Tableau with other tools and platforms:
Integrating Tableau with R and Python to perform statistical analysis and machine learning.
Connecting Tableau with ETL tools and big data platforms to handle large volumes of data.
Publishing and sharing visualizations and dashboards on Tableau Server and Tableau Online.

Optimization and Best Practices:
Techniques for optimizing dashboard performance, including improving load times and reducing complexity.
Best practices for designing visualizations that maximize clarity and communication effectiveness.
Managing permissions and data security in Tableau.

Final Project:
Development of a comprehensive project where participants will apply all the knowledge acquired to solve a real or simulated business problem by creating an interactive dashboard and data story.
Presentation of the project to a panel of experts to receive detailed feedback.

Additional Benefits:

Certification in Data Visualization with Tableau:
Upon completion of the course, you will receive a certification that validates your proficiency in using Tableau for data visualization, highly valued in the industry.

Access to Exclusive Resources and Templates:
You’ll get access to dashboard templates, example use cases, and additional educational resources to enhance your skills.

Mentoring and Networking:
During the course, you will have the opportunity to interact with expert data visualization mentors and participate in networking events with other industry professionals.

Course duration: 3 months

Modality: 100% online, with live sessions and access to recorded content.