Managers' Corner

This five-day training program provides a comprehensive approach to designing and implementing Data Science and AI projects in any company.

It addresses AI ethics, focusing on bias, transparency, and regulatory compliance, and explores business strategies that utilize Porter's principles to balance AI innovation with effective decision-making. Participants will learn about various Data Science applications, including Machine Learning, Deep Learning, Robotic Process Automation, and Generative AI.

The program emphasizes practical implementation alongside theoretical concepts, teaching attendees to leverage AI for predictive analytics, manage requests via WhatsApp, automate workflows, and create performance monitoring flowcharts. 

• Understand AI business ethics and strategies and apply Data Science, ML, and DL in AI project development.
• Apply statistical analysis and predictive modeling for AI-driven decision-making.
• Implement Machine Learning & Deep Learning solutions for business intelligence and automation.
• Pipelining NLPs to Machine Learning
• Explore Robotic Process Automation (RPA) to streamline repetitive business processes.
• Integrate ethical and regulatory frameworks into AI project development.
• Develop a complete AI project roadmap from strategy to deployment. 

Day 1: 

- Introduction to AI

- AI Ethics and Business Strategies

- Porter's rules of thumb

Day 2:

- CRISP-MD overview

- Transforming a business into efficient data sets

- Business automation with RPA

Day 3:

- Data Visualization, Statistical KPIs, and Data Analysis dashboards

Day 4:

- Predictive Machine Learning (Supervised) and Trends 

- Explorative Machine Learning (Unsupervised)

Day 5:

- Quality Control with Statistical Measurements and Deep Learning

- Generative AI for managerial automation

- Role of Generative AI Tools in the automation process

- Data Science or AI?

- AI ethics and governance considerations.

- Porter’s strategic rules for balancing AI adoption in competitive markets.

- CRISP-DM framework for AI project management.

- Translating a project into meaningful data

- Data Visualization anhttps://websites.godaddy.com/managers-cornerd statistical profiling tools.

- Predictive analytics with:

    * Machine Learning: Regressions, Decision Trees, SVMs, PCA, ...

    * Deep Learning: FFNN, CNN, RNN, LSTM, ...

    * NLP: Text classification, LLMs, ...

- Automating workflows, document processing, and customer service tasks with RPAs.

- Controlling production with Control Charts and CNN algorithms.

- AI-based inventory management, stock forecasting, and supply chain automation with Generative AI solutions.

- Role of LLMs (ChatGPT, DeepSeek) in the automation process.

The "Complete Data Science & AI Dictionary" workshop offers a comprehensive overview of foundational and advanced AI concepts. This immersive program blends theoretical insights with practical applications, providing a structured journey statistics, data visualization, data analysis, machine learning, deep learning, generative AI, NLPs, data management, big data technologies, IoT, and analytical tools.

Ideal for managers, analysts, and professionals, the workshop equips participants with the knowledge to navigate AI's evolving landscape and apply these technologies effectively across industries.

Designed to demystify AI solutions, it ensures participants gain a solid understanding of Data Science and AI components, enabling them to confidently engage in professional discussions without getting lost in technical jargon. 

• Understand key AI and data science terminologies across multiple domains.
• Differentiate between statistics, data analysis, and machine learning concepts.
• Recognize the core principles of deep learning and generative AI.
• Demystifying the concepts of learning, training models, overfitting, feature engineering.
• Identify common NLP terms and their applications.
• The Role of the AI Agents
• Familiarize with data management and big data.
• Comparing open-source tools with proprietary ones.
• Understanding AI project design.
• Prompting with ChatGPT

Day 1: 

- The complete roadmap towards AI

- Data design, visualization, and statistical KPIs

Day 2:

- Data Analysis with statistical tests

- Data Management & SQL

Day 3:

- Supervised Machine Learning vs. Trend Analysis

- IoT and Big Data technology essentials

Day 4:

- Unsupervised Machine Learning

- Python vs. Proprietary software

Day 5:

- Deep Learning and Generative Deep Learning

- Generative AI Tools

• Statistics & Data Analysis: Key concepts like mean, variance, statistical tests, correlation, regression, and hypothesis testing.
• Data Visualization: Histograms, scatter plots, dashboards, and storytelling.
• Supervised Machine Learning: Multiple regressions, discriminant analysis, decision trees, SVM, etc.
• Unsupervised Machine Learning: Principal Component Analysis, Clustering.
• Deep Learning & Generative AI: Neural networks, CNN, and GAN solutions.
• NLP and AI Agents: Tokenization, embeddings, LLMs, sentiment analysis, and AI Agents.
• Data Management: Governance, Quality, SQL
• Big Data technologies: Unstructured data, data lakes, NoSQL, Hadoop, Spark.
• IoT essentials in conveying data worldwide.
• Prompting with ChatGPT

This five-day workshop provides participants with essential knowledge and skills for automating business processes using Microsoft Power Automate and enhancing productivity with Microsoft Copilot.

The "Complete RPA, Power Automate & Copilot Dictionary" workshop offers a practical introduction to Robotic Process Automation (RPA) and No-Code/Low-Code tools. It combines theory with hands-on applications, covering process automation and workflow optimization.

Designed for business leaders, IT professionals, analysts, and process managers, the workshop empowers participants to automate tasks, eliminate repetitive work, and drive efficiency using Microsoft’s automation tools. It ensures a clear, non-technical understanding of automation implementation across industries.

• Understand key RPA and automation terms.
• Differentiate RPA, AI-assisted automation, and traditional scripting.
• Recognize the capabilities of Microsoft Power Automate,      Power Automate Desktop, and Copilot.
• Learn to design and improve business workflows.
• Explore No-Code/Low-Code methods for building bots.
• Understand how Copilot AI enhances automation through natural language.
• Familiarize with governance, security, and RPA best practices.
• Compare standalone automation platforms with integrated Microsoft solutions.
• Design end-to-end real-world automation projects. 

Day 1:

• Introduction to the Automation Era
• RPA vs. Traditional Automation vs. AI-Powered Automation
• Overview of the Microsoft Power Platform

Day 2:

• Designing Process Flows using Microsoft Power      Automate Cloud
• Understanding Connectors, Triggers, and Actions

Day 3:

• Introduction to Power Automate Desktop for Robotic Desktop Automation
• Screen scraping, file system automation, and UI  automation
• Hands-on labs: Automate your first desktop task

Day 4:

• Copilot in Power Platform: Empowering No-Code      Automation with AI
• How to generate workflows using natural language
• Training Copilot to refine automation suggestions

Day 5:

• Governance, Security, and Best Practices in RPA
• Planning and managing automation at scale
• Final Project: Build a Business Automation Solution using Power Automate. 

• Automation Basics: Key concepts like processes,      workflows, triggers, and actions.
• RPA Foundations: Attended vs. unattended bots,      rule-based automation.
• Power Automate Cloud: Setting up flows in Microsoft      365, Outlook, SharePoint, and external apps.
• Power Automate Desktop: Advanced UI and robotic desktop automation (RDA).
• Copilot in Automation: Creating workflows using natural language.
• Data Handling: Managing structured and unstructured data in workflows.
• Security and Compliance: Secure practices, audit trails, and data governance.
• Use Cases: HR onboarding, invoice processing, sales automation, approval workflows.  
• Prompting with ChatGPT

With the rapid advancements in technology, the foundation of AI-driven decision-making, particularly through "Supervised" Machine Learning (ML) models and mainly "Neural Networks", has become increasingly accessible to practitioners. Thanks to improved automation tools, mastering these key predictive models is now more achievable.

This workshop provides an in-depth look at "supervised" ML and its one-step improvement, Neural Networks, which play a vital role in enhancing predictive accuracy across various industries. Participants will engage with multiple PYTHON comparative solutions by simply learning how to prompt with ChatGPT. By the end, attendees will have the skills to utilize these powerful algorithms effectively, positioning themselves as proficient practitioners.

• Explore the rise of AI with IoT and technology capacities.
• Understand the difference between Statistical Data Analysis, Machine Learning, and Deep Learning models.
• Differentiate between Regression and Classification models.
• Avoid the high-performance illusion of predictive models.
• Validate and evaluate ML and DL models.
• Selecting between ML and DL models.
• Optimize models with hyperparameter fine-tuning.
• Compare ML and DL models using accuracy measures.
• Design dashboards for comparative models.
• Prompting with ChatGPT

Day 1:

- The complete ML and DL encyclopedia 

Day 2: 

- Linear and Logistic Regressions 

- Discriminant analysis

Day 3:

-  Decision Trees / Naive Bayes / Support Vector Machines

Day 4: 

- Feed Forward Neural Networks

- Sequential Predictive Nets

Day 5: 

- Case study workshop

- Compare Machine Learning and Deep Learning approaches.

- Understand data requirements and scalability differences.

- Explore ML models like decision trees and SVMs.

- Learn how DL models like CNNs and RNNs extract complex patterns.

- Apply the right validation techniques: cross-validation vs. train/test splits.

- Discover regularization methods like dropout and early stopping.

- Master analytics: feature engineering vs. automatic representation learning.

- Choose the best approach for your data and business needs.