Complete Data Science & AI Skills

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 statistic, 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.

Workshop Overview

Learning Outcomes

• 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.

Detailed Course Schedule

• 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.

What will it be about?