Business Analytics for Management Decision
This course will be exclusively quantitative and an application to business/management-related problems. It is connected with problem sets and real life cases to know the relevance of a particular problem and the decision-making thereof.
Introduction to Biostatistics
In this course, we will discuss different statistical tools required to (i) analyze our observations, (ii) design new experiments, and (iii) integrate a large number of observations in a single unified model.
Essential Statistics for Data Science - Learn Statistics for Data Science
Comprehensive overview of essential statistics for data science, covering central tendency, hypothesis testing, ANOVA, and more. Ideal for aspiring data scientists seeking foundational knowledge.
Statistics
Comprehensive video series covering fundamental statistical concepts, from data analysis to hypothesis testing, with practical examples and in-depth explanations.
Statistics for Data Science
Comprehensive exploration of statistical concepts for data science, covering descriptive and inferential statistics, probability distributions, hypothesis testing, and various analytical methods through practical examples.
Statistics
Comprehensive exploration of statistical concepts, from basics to advanced topics, covering descriptive and inferential statistics, probability, distributions, hypothesis testing, and various statistical tests.
Biostatistics and Research Methodology
Comprehensive exploration of biostatistics and research methodology, covering statistical concepts, sampling techniques, hypothesis testing, and research design for pharmacy students.
Statistics Fundamentals
Comprehensive overview of statistical concepts, from basic distributions to advanced topics like maximum likelihood, providing a solid foundation for data analysis and interpretation.
Mathematics - Probability and Statistics
Comprehensive exploration of probability and statistics, covering set theory, probability laws, distributions, sampling, descriptive statistics, estimation, and hypothesis testing.
Probability and Statistics
Comprehensive exploration of probability theory, statistical distributions, sampling, estimation, and hypothesis testing, providing a solid foundation for data analysis and statistical inference.
Data Analytics with Python
Learn Python-based data analytics, covering statistics, probability, hypothesis testing, regression, ANOVA, clustering, and decision trees. Gain practical skills for data-driven decision making and statistical analysis.
Introduction to Probability and Statistics
This course is aimed at being a pre-term or a preparatory course for probability and statistics. It is very basic and introduces the students to the understanding of data and measuring associations. It also introduces the concepts of probability and dist…
Linear Regression Analysis and Forecasting
The accuracy of forecasting depends upon the goodness of obtained model. What are the steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course.
Introduction to Econometrics
Comprehensive exploration of econometric analysis, covering linear regression, hypothesis testing, STATA applications, and advanced models like logit, probit, and tobit.
Statistics 200 Video Lectures
Comprehensive video lectures covering fundamental statistical concepts, methods, and applications for beginners through advanced learners.
Statistics Lectures - OER
Comprehensive introduction to statistical concepts and methods, covering fundamental principles and practical applications in data analysis and interpretation.
Applied Statistics and Econometrics
Gain practical skills in statistical analysis and econometric modeling, enhancing your ability to interpret data and make informed economic decisions.
Engineering Probability Lectures, Fall 2018
Comprehensive lecture series on engineering probability, covering fundamental concepts, discrete and continuous random variables, estimation techniques, and hypothesis testing.
Uncontextualized Significance Considered Dangerous in Evaluation - Session M1.5
Explore the dangers of uncontextualized significance in evaluation, presented by Nicola Ferro and Mark Sanderson at SIGIR 2024.
Data Thinning to Avoid Double Dipping in Statistical Analysis - Lecture 1
Explore data thinning techniques to prevent double dipping in statistical analysis, with applications in single-cell RNA sequencing and unsupervised learning problems.
Study Design Part 2 - Statistical Considerations in Preparing Your Research Protocol - Lecture 3
Explore statistical considerations for study design, including sample size, power analysis, and data analysis planning for clinical and translational research.