Summary Data generation and sources :the impact of IOT and social media

Understanding Machine Learning Concepts and Algorithms
Different types of machine learning: supervised, unsupervised, reinforcement learning
Applications of machine learning in real-world problems
Introduction to popular machine learning algorithms: linear regression, logistic regression, decision trees


Data Visualization Techniques and Tools
Understanding the basics of data visualization
Popular data visualization tools: Seaborn, Tableau, PowerBI
Techniques for effective data storytelling


The Data Lifecycle: From Business Requirement to Deployment
Understanding the different stages of the data lifecycle
From business requirements to data collection, processing, analysis, and deployment
Best practices for each stage of the data lifecycle


Fundamentals of Statistics and Probability in Data Science
Introduction to probability and inferential statistics
Understanding concepts such as point estimation, interval estimation, and hypothesis testing
Applications of statistical analysis in data science


Implementing Logistic Regression Using Python Libraries