Calling all Data Aficionados!
The "Advanced Data Science" course provides a comprehensive overview of key statistical and mathematical concepts crucial for data analysis.
It begins with probability theory, which quantifies the likelihood of events and forms the basis for understanding randomness and variability. The course then covers the normal distribution, a fundamental statistical pattern characterized by its bell-shaped curve, essential for analyzing continuous data. Students will learn about various sampling techniques, such as random, stratified, and convenience sampling, to efficiently collect and analyze data from larger populations. The curriculum includes hypothesis testing, a method for evaluating assumptions and making informed decisions based on sample data. Additionally, the course explores correlation to assess relationships between variables, distinguishing between positive, negative, and no correlation scenarios. Linear regression is taught for modeling the relationship between a dependent variable and one or more independent variables, while logistic regression focuses on predicting binary outcomes and interpreting probabilities. These topics equip students with the skills to draw meaningful conclusions, make predictions, and understand complex data relationships, applicable across diverse fields like science, economics, and social sciences.
Upon completing the "Advanced Data Science" course, students will be equipped with a robust understanding of statistical methods and analytical techniques, enabling them to tackle complex data challenges and make data-driven decisions in various professional and academic settings.