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.

Fundamentals of Data Analysis:
Introduction to the basic concepts of data analysis, including types of data, descriptive statistics, and data collection methods.
Understanding the importance of data analysis in decision making and its impact on organizations.

Data Collection and Preparation:
Techniques for collecting data from various sources, including surveys, databases, and online sources.
Methods for data cleaning and preparation, including error removal, normalization, and data transformation for analysis.

Data Exploration and Visualization:
Tools and techniques for exploring data sets and discovering patterns and trends.
Creating effective visualizations, such as graphs, charts, and tables, to communicate insights in a clear and understandable way.

Basic Statistical Analysis:
Application of fundamental statistical techniques, including measures of central tendency, dispersion, and correlation analysis.
Use of basic statistical tests to infer and generalize results from data samples.

Introduction to Data Analysis Tools:
Familiarization with tools and software used in data analysis, such as Microsoft Excel, Google Sheets and statistical analysis tools such as R and Python.
Use of advanced functions and tools to perform complex analysis and handle large volumes of data.

Predictive Modeling and Advanced Analytics:
Introduction to basic predictive models, including linear regression and time series analysis.
Application of modeling techniques to make predictions and trend analysis based on historical data.

Interpretation of Results and Decision Making:
Development of skills to interpret data analysis results and draw meaningful conclusions.
Application of data insights in strategic and operational decision making within an organization.

Ethics and Privacy in Data Analysis:
Ethical considerations in the collection, analysis, and use of data, including privacy protection and compliance with legal regulations.
Best practices to ensure responsible and ethical use of data in analysis and decision making.

Practical Project:
Participants will conduct a hands-on project involving the collection, preparation, analysis and visualization of a real or simulated data set.
The project will be reviewed and evaluated, with detailed feedback from experts in data analysis.

Additional Benefits:

Data Analytics Certification:
Upon completion of the course, you will receive an industry-recognized certification that validates your ability to perform effective data analysis.

Access to Exclusive Tools and Resources:
You’ll get access to data analytics software, advanced tutorials and project templates to facilitate your learning and practical application.

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

Course duration: 4 months

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

Introduction to Big Data:
Definition and characteristics of Big Data, including the 5 V’s: Volume, Velocity, Variety, Veracity and Value.
Understanding the importance of Big Data in the business context and how it is changing the way organizations operate.

Big Data Infrastructure and Technologies:
Exploration of the main technologies and tools used in Big Data management, such as Hadoop, Spark, and NoSQL databases.
Design and management of Big Data architectures, including storage, processing and analysis of large volumes of data.

Data Collection and Storage:
Techniques for collecting data from a variety of sources, including structured, unstructured, and semi-structured data.
Methods for efficient data storage using technologies such as HDFS (Hadoop Distributed File System) and cloud solutions.

Big Data Processing and Analysis:
Introduction to batch and real-time data processing techniques.
Application of advanced analytical techniques, such as real-time data analysis, data mining and machine learning.

Data Visualization and Communication:
Creation of effective visualizations to communicate insights derived from Big Data, using tools such as Tableau, Power BI and D3.js.
Development of interactive dashboards and reports that facilitate the understanding of complex data and data-driven decision making.

Applications of Big Data in Business:
Case studies on how different industries use Big Data to improve operations, marketing, sales and customer service.
Strategies for implementing Big Data solutions in key areas such as customer segmentation, supply chain optimization and demand forecasting.

Data Quality and Security Management:
Techniques to ensure data quality, including data cleansing, validation and enrichment.
Strategies to protect data and comply with privacy and security regulations, such as GDPR and CCPA.

Digital Transformation and Big Data:
Analysis of how Big Data drives digital transformation in enterprises and contributes to innovation and the creation of new business models.
Development of a roadmap to integrate Big Data into business strategy and foster a data-driven culture.

Ethics and Legal Considerations in Big Data:
Exploration of the ethical and legal challenges associated with the collection and use of large volumes of data.
Best practices for handling data ethically and complying with legal regulations.

Final Project:
Participants will conduct a hands-on project using a Big Data platform to solve a real or simulated business problem.
The project will be presented to a panel of experts, with detailed feedback on the approach and proposed solutions.

Additional Benefits:

Certification in Big Data and its Application in Business:
Upon completion of the course, you will receive an industry-recognized certification that validates your ability to use Big Data in the business context.

Access to Exclusive Tools and Resources:
You’ll get access to Big Data analytics platforms, advanced visualization tools and detailed case studies to enrich your learning.

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

Course duration: 6 months

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

Introduction to Machine Learning:
Definition of Machine Learning and its role in artificial intelligence.
Differences between supervised, unsupervised and reinforcement learning.
Applications and use cases of Machine Learning in different sectors.

Mathematical and Statistical Fundamentals:
Introduction to the basic mathematical concepts needed for Machine Learning, including linear algebra, calculus and probability.
Understanding of the fundamental statistical principles underlying Machine Learning algorithms.

Data Preprocessing:
Techniques for data cleaning, transformation and normalization.
Handling of missing, categorical and unbalanced data.
Use of tools and libraries for preprocessing, such as Pandas and Scikit-Learn.

Supervised Learning Algorithms:
Introduction to supervised learning algorithms, including linear regression, logistic regression, decision trees, and support vector machines (SVM).
Implementation and evaluation of models using performance metrics such as accuracy, recall and F1-score.

Unsupervised Learning Algorithms:
Exploration of unsupervised learning techniques, including clustering with K-means and principal component analysis (PCA).
Application of methods for dimensionality reduction and pattern identification in unlabeled data.

Model Evaluation and Validation:
Techniques for evaluating model performance, including cross-validation, partitioning of data into training and test sets, and hyperparameter fitting.
Analysis of overfitting and underfitting, and strategies to improve model generalization.

Introduction to Neural Networks:
Basic concepts of artificial neural networks and their operation.
Implementation of simple neural networks and use of frameworks such as TensorFlow and Keras to create basic deep learning models.

Practical applications of Machine Learning:
Development of practical projects using Machine Learning to solve real-world problems, such as sales predictions, sentiment analysis, and personalized recommendations.
Integration of Machine Learning models in business applications and systems.

Ethics and Considerations in Machine Learning:
Reflection on the ethical implications of Machine Learning, including data bias and privacy.
Best practices to ensure fairness and transparency in the development and application of Machine Learning models.

Final Project:
Participants will develop a hands-on project involving the application of Machine Learning techniques to a real or simulated problem.
The project will be reviewed and evaluated, with detailed feedback from Machine Learning experts.

Additional Benefits:

Certification in Machine Learning for Beginners:
Upon completion of the course, you will receive an industry-recognized certification that validates your proficiency in Machine Learning fundamentals.

Access to Exclusive Tools and Resources:
You’ll gain access to Machine Learning platforms, software libraries, and additional educational resources to facilitate your learning and practical application.

Mentoring and Networking:
During the course, you will have the opportunity to interact with expert Machine Learning 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.

Introduction to Python and its Applications in Data Science:
Installation and configuration of the Python development environment.
Introduction to key libraries for data science: NumPy, pandas, Matplotlib and Seaborn.
Python applications in data science, including exploratory analytics and predictive modeling.

Fundamentals of Python for Data Science:
Python data structures: lists, dictionaries, sets, and tuples.
Flow control and error handling: conditionals, loops and exceptions.
Functions and modules: creating and using functions, importing modules and packages.

Data manipulation and cleaning with Pandas:
Use of pandas for data loading, cleaning and manipulation.
Advanced data manipulation techniques, including grouping, pivoting and merging of datasets.
Handling missing data, duplicates and transformations.

Data visualization with Matplotlib and Seaborn:
Creating static and dynamic charts using Matplotlib and Seaborn.
Customization of visualizations: styles, labels, legends and annotations.
Development of interactive dashboards with Plotly and Dash.

Exploratory Data Analysis (EDA):
Exploratory analysis techniques to identify patterns, trends and anomalies in data.
Application of descriptive statistics and correlation analysis.
Use of visualization tools to discover insights and prepare data for modeling.

Introduction to Machine Learning with Scikit-Learn:
Basic principles of machine learning and its implementation in Python using Scikit-Learn.
Creation and evaluation of supervised and unsupervised learning models.
Cross-validation techniques, hyperparameter tuning and feature selection.

Development of Predictive Models:
Implementation of predictive models for regression and classification problems.
Evaluation of model performance using metrics such as accuracy, recall, F1-score and ROC-AUC.
Model regularization and optimization techniques to improve generalization.

Textual Data Processing and NLP:
Introduction to natural language processing (NLP) and its application in Python.
Text analysis techniques, including tokenization, lemmatization and sentiment analysis.
Use of NLP libraries such as NLTK and SpaCy for textual data processing.

Integration and Automation:
Python integration with SQL and NoSQL databases for data management and analysis.
Script development and automation of data science workflows.
Creation of applications and APIs using Flask to expose machine learning models.

Final Project:
Development of a comprehensive project covering data collection, manipulation, analysis and modeling using Python.
Presentation and evaluation of the project with feedback from data science experts.

Additional Benefits:

Certification in Python for Data Science:
Upon completion of the course, you will receive an industry-recognized certification that validates your proficiency in using Python for Data Science.

Access to Exclusive Tools and Resources:
You’ll gain access to development environments, datasets and advanced analysis tools to facilitate your learning and practical application.

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

Course duration: 4 months

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