Data Science Foundations: Statistical Inference
Master core statistical methods and probability theory to analyze data, build models, and support decision-making in data science applications.
Master core statistical methods and probability theory to analyze data, build models, and support decision-making in data science applications.
This specialization provides a comprehensive foundation in probability theory and statistical inference for data science. Students learn fundamental statistical concepts, estimation techniques, and hypothesis testing methods while developing practical skills in R programming through hands-on exercises and data analysis projects.
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বাংলা, پښتو, اردو, 2 more
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What you'll learn
Master probability theory fundamentals for data science
Perform statistical inference and estimation
Construct and interpret confidence intervals
Develop hypothesis testing skills
Analyze data using R programming
Apply statistical methods to real datasets
Skills you'll gain
This course includes:
104 Hours PreRecorded video
Exercises in Jupyter Notebooks and benchmark quizzes
Access on Mobile, Desktop, Tablet
FullTime access
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There are 3 courses in this program
Probability Theory: Foundation for Data Science
Course 1 · 40 Hours to complete · 7 modules
Statistical Inference for Estimation in Data Science
Course 2 · 28 Hours to complete · 6 modules
Statistical Inference and Hypothesis Testing in Data Science
Course 3 · 36 Hours to complete · 6 modules
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