Introduction to Statistics: Foundations of Data Analysis
Master essential statistical concepts for data analysis. Learn exploratory techniques, sampling principles, and significance tests.
Master essential statistical concepts for data analysis. Learn exploratory techniques, sampling principles, and significance tests.
Stanford's "Introduction to Statistics" course teaches fundamental statistical thinking concepts crucial for data analysis and communication. Students will learn to perform exploratory data analysis, understand key sampling principles, and select appropriate significance tests for various contexts. The course covers descriptive statistics, sampling and randomized controlled experiments, probability, sampling distributions, the Central Limit Theorem, regression, common tests of significance, resampling, and multiple comparisons. By the end, students will have the foundational skills to pursue more advanced topics in statistical thinking and machine learning.
4.6
(3,334 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Understand and apply descriptive statistics for data exploration
Master sampling techniques and design of randomized controlled experiments
Apply probability concepts to statistical problems
Understand sampling distributions and the Central Limit Theorem
Perform and interpret regression analysis
Construct and interpret confidence intervals
Skills you'll gain
This course includes:
4.48 Hours PreRecorded video
13 assignments
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FullTime access
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There are 12 modules in this course
This comprehensive course introduces students to the fundamental concepts of statistics, essential for data analysis and interpretation. The curriculum covers a wide range of topics, from basic descriptive statistics to more advanced concepts like regression, hypothesis testing, and analysis of variance (ANOVA). Students will learn how to visualize data, understand sampling techniques, apply probability concepts, and perform various statistical tests. The course emphasizes practical application, teaching students how to use statistical thinking to solve real-world problems and make data-driven decisions. By the end of the course, participants will have a solid foundation in statistical methods, preparing them for more advanced studies in data science and machine learning.
Introduction and Descriptive Statistics for Exploring Data
Module 1 · 2 Hours to complete
Producing Data and Sampling
Module 2 · 59 Minutes to complete
Probability
Module 3 · 1 Hours to complete
Normal Approximation and Binomial Distribution
Module 4 · 1 Hours to complete
Sampling Distributions and the Central Limit Theorem
Module 5 · 1 Hours to complete
Regression
Module 6 · 1 Hours to complete
Confidence Intervals
Module 7 · 1 Hours to complete
Tests of Significance
Module 8 · 1 Hours to complete
Resampling
Module 9 · 1 Hours to complete
Analysis of Categorical Data
Module 10 · 58 Minutes to complete
One-Way Analysis of Variance (ANOVA)
Module 11 · 1 Hours to complete
Multiple Comparisons
Module 12 · 1 Hours to complete
Fee Structure
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Instructor
Expert in Statistical Methodology and Inference
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4.6 course rating
3,334 ratings
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