Enroll Course: https://www.coursera.org/learn/statistical-inference-for-estimation-in-data-science
In the ever-evolving field of data science, a solid understanding of statistical inference is paramount. If you’re looking to deepen your knowledge in this crucial area, the “Statistical Inference for Estimation in Data Science” course on Coursera, offered by CU Boulder as part of their Master of Science in Data Science program, is an exceptional choice.
This course dives deep into the foundational concepts of statistical inference, starting with the essentials of sampling distributions and confidence intervals. It’s designed to equip you with the skills to define and construct robust estimators, a core competency for any data scientist. The curriculum meticulously covers the method of moments estimation and maximum likelihood estimation (MLE), providing both theoretical grounding and practical application.
The syllabus is thoughtfully structured, beginning with an “Introduction” to set the stage. The “Point Estimation” module introduces desirable properties of estimators and reviews fundamental concepts like expectation, variance, and covariance, before delving into the intuitive “method of moments.” The “Maximum Likelihood Estimation” module then builds upon this, explaining likelihood functions and the construction of MLEs for various scenarios, including the valuable invariance property.
Further modules explore the “Large Sample Properties of Maximum Likelihood Estimators,” introducing concepts like asymptotic unbiasedness and normality, and the important “Cramér–Rao lower bound” as a benchmark for estimator variance. The course then transitions to “Confidence Intervals Involving the Normal Distribution,” clearly defining and explaining the interpretation of confidence intervals, and demonstrating their construction for population means with both known and unknown variances, using both large and small samples.
Finally, “Beyond Normality: Confidence Intervals Unleashed!” broadens the scope, showing how to develop confidence intervals for parameters in non-normal distributions and for other quantities of interest. This includes detailed coverage of two-sample confidence intervals, and intervals for population variances and proportions.
This course isn’t just about theory; it’s about building the practical skills needed to make informed decisions from data. Whether you’re looking to enhance your academic credentials or simply bolster your data science toolkit, “Statistical Inference for Estimation in Data Science” is a highly recommended and valuable investment in your professional development.
Enroll Course: https://www.coursera.org/learn/statistical-inference-for-estimation-in-data-science