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 using Porter's principles to balance AI innovation with competitive 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.
• Explore Robotic Process Automation (RPA) to streamline repetitive business processes.
• Leverage Generative AI for stock optimization, demand forecasting, and process automation.
• 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.

- Data Visualization, 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.

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 ad AI Agents: Tokenization, embeddings, LLMs, sentiment analysis, and AI Agents.
• Data Management & Big Data: Structured vs. unstructured data, data lakes, NoSQL, Hadoop, Spark.
• IoT essentials in conveying data worldwide.