This Data Analytics Fundamentals course provides a strong theoretical foundation in data analysis and statistical reasoning. It is designed for learners who want to understand the core mathematical and probabilistic concepts behind modern data analytics and data science.
The course begins with basic data analytics concepts, including spatial sampling and different types of subsurface and structured data. It then introduces probability theory, explaining how uncertainty is measured and interpreted in data analysis.
Learners will explore different probability approaches such as frequentist probability and Bayesian probability, helping them understand how data-driven decisions are made under uncertainty. The course also covers joint, marginal, and conditional probability, which are essential concepts for advanced analytics and machine learning.
In addition, the course demonstrates Bayesian applications through practical examples, helping learners visualize how probability updates with new information. It also introduces univariate distributions and statistical summaries, which are key tools for understanding data behavior and patterns.
By the end of this course, learners will have a solid understanding of probability, statistical distributions, and foundational analytics concepts. This course is ideal for beginners, students, and anyone who wants to build strong analytical and mathematical thinking for data science and advanced analytics.
