Day 1: Business Decision Making - Human Intelligence vs. Data-Driven Artificial Intelligence

• Understanding the limitations and biases in human decision-making.
• Real-world examples of intuition failures in business.
• How data-driven AI provides objective, reliable solutions, illustrated through real-world business cases.

Day 2: Understanding Data and Its Building Blocks

• The importance of data in decision-making and innovation.
• The building blocks of data.
• Techniques for managing, cleaning, and preparing data for analysis.
• Introduction to data work tools, including spreadsheets, SQL, and Python programming.

Day 3: Data Analysis, Visualization, and Business Intelligence

• Methods for analyzing data to extract meaningful information.
• Visualization techniques for effective communication of data insights.
• Introduction to Business Intelligence (BI) and its importance in decision-making.
• Case studies demonstrating the impact of visualization in real-world business scenarios.

Day 4: Introduction to Machine Learning

• Key machine learning concepts.
• Two different types of machine learning models: Unsupervised and Supervised
  learning.
• Unsupervised learning algorithms and practical applications.

Day 5: Supervised Learning - Regression and Classification - Part 1

• Regression and classification models, including:
• Linear regression
• Logistic regression
• Decision trees
• Support vector machines

Day 6: Supervised Learning - Regression and Classification - Part 2

• Regression and classification models, including:
  • Random forests
  • Gradient boosting trees
  • Neural networks
• Comparison of techniques in solving business problems.
• Common challenges in practical applications

Day 7: Real-World Business Case Studies Using Machine Learning - Part 1

• Customer Credit Assessment
• Check Fraud Detection

Day 8: Real-World Business Case Studies Using Machine Learning - Part 2

• Target Marketing
• Insurance Claims Prioritization

Day 9: Advanced Machine Learning Techniques

• Advanced techniques for improving model accuracy and effectiveness.
• Strategies for creating competition-winning machine learning solutions.

Day 10: Introduction to Natural Language Processing (NLP) and Exploring Generative AI

• Core concepts of NLP and key techniques.
• Practical applications of NLP and machine learning.
• Leveraging Generative AI for Data Science tasks.