Course Provider
What will you learn in this course?
At the end of this course, you should be able to:
- Understand the concept of probability and its relation with mathematical sets
- Be able to perform basic operations with sets and probabilities and use them to answer interesting questions
- Contrast population and sample, parameter and its estimate
- Understand and contrast the concepts of basic summary statistics, e.g., mean, variance, standard deviation, etc. for population versus samples and learn to estimate them from data
- Understand the concept of effect size and uncertainty
- Have a comprehensive idea about different kinds of probability distributions both discrete and continuous
- Understand the concept of hypothesis, type I and II errors, level of significance, and power
- Understand and be able to test hypotheses for mean and proportion and draw useful inferences from the process
- Define and recognize the relation between covariance and Pearson’s correlation coefficient
- Recognize the importance of dimensionality reduction, and comprehend principal component analysis. Along the way, you will learn about different matrix factorization techniques, eigenvalues, eigenvectors, their properties and interpretation
Probability and Statistics
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Skill Type
Emerging Tech
- Domain
Artificial Intelligence
- Course Category
Deepskilling Course
- Course Covered under GoI Incentive
No
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- Course Price
FREE
- Course Duration
30 Hours
- Course Price
Why should you take this course?
At the end of this course, you should be able to:
- Understand the concept of probability and its relation with mathematical sets
- Be able to perform basic operations with sets and probabilities and use them to answer interesting questions
- Contrast population and sample, parameter and its estimate
- Understand and contrast the concepts of basic summary statistics, e.g., mean, variance, standard deviation, etc. for population versus samples and learn to estimate them from data
- Understand the concept of effect size and uncertainty
- Have a comprehensive idea about different kinds of probability distributions both discrete and continuous
- Understand the concept of hypothesis, type I and II errors, level of significance, and power
- Understand and be able to test hypotheses for mean and proportion and draw useful inferences from the process
- Define and recognize the relation between covariance and Pearson’s correlation coefficient
- Recognize the importance of dimensionality reduction, and comprehend principal component analysis. Along the way, you will learn about different matrix factorization techniques, eigenvalues, eigenvectors, their properties and interpretation
Who should take this course?
- BE/ BTech students-any stream
- Non-engineering students-STEM background
- Working Professionals
Curriculum
- The idea of probability will be introduced starting from the concept of mathematical sets.
- Students will learn how to perform basic probability operations and get an idea about conditional probability and Bayes’ theorem.
- This will then be used to develop the idea about the different probability distributions, both discrete as well as continuous, using appropriate examples.
- In between the concepts of different summary statistics such as mean, and variance will be introduced.
- Students will also learn how to explore the pattern among multiple variables in the form of covariance, correlation, and principal component analysis.
Tools you will learn in the course
- Introduction to sets
- Basic set operations, probability
- Sum and multiplication 'rules' of probability
- Probability distributions
- Discrete and continuous probability distribution
- Hypothesis testing
- T and chi-squared tests
- Covariance
- Correlation
- Principal component analysis