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Learn the fundamentals of statistical thinking and data analysis from Stanford professor Guenther Walther. Explore topics such as descriptive statistics, sampling, probability, regression, and tests of significance.
- Probability and Statistics
3.2 Data Visualisation ... 4.1 Introduction to Sampling ......
- Introduction to Data Analytics
Explain what Data Analytics is and the key steps in the Data...
- Probability and Statistics
A textbook for undergraduate students and self-learners in various disciplines, covering descriptive, inductive and explorative methods. It includes new chapters on logistic regression, sampling, bootstrapping and causal inference, with R code and solutions.
Learn about statistical data analysis from applied probability, sampling, estimation, hypothesis testing, linear regression, and more. This course is offered by the Sloan School of Management and the Institute for Data, Systems, and Society at MIT.
The success of the open-source statistical software “R” has made a significant impact on the teaching and research of statistics in the last decade. Analyzing data is now easier and more affordable than ever, but choosing the most appropriate statistical methods remains a challenge for many users. To understand and interpret
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts.
Explain what Data Analytics is and the key steps in the Data Analytics process. Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst. Describe the different types of data structures, file formats, and sources of data.
19 cze 2024 · This course is an intensive introduction to the fundamental concepts of probability, statistical inference, and statistical computing necessary for a working knowledge of applied statistics. The course moves at an accelerated pace and requires a substantial commitment in time and effort.