Elevate your data analysis skills with our comprehensive Probability and Statistics course, tailored for professionals seeking real-world applications. Ideal for aspiring data analysts, engineers, scientists, and anyone looking to enhance their decision-making abilities, this course is your gateway to mastering essential statistical concepts. Dive deep into data sets, Chebyshev’s inequality, descriptive statistics, probability axioms, and Bayes’ formula. Gain expertise in random variables, mathematical expectations, various distributions, confidence intervals, hypothesis testing, and regression analysis.
Probability and Statistics
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Beginner level
Recommended experience
3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Evaluate and interpret complex data sets with probabilistic models, applying Bayes’ theorem and Chebyshev’s inequality to solve real-world problems.
Design hypothesis tests, including t-tests, z-tests, and chi-square tests, to validate data-driven hypotheses in various professional contexts.
Construct and optimise predictive models using multiple and nonlinear regression techniques to forecast outcomes and improve decision-making.
Synthesise probability and statistical knowledge to develop innovative solutions for complex analytical challenges.
Skills you'll gain
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Recently updated!
August 2025
Assessments
111 assignments
Taught in English
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There are 9 modules in this course
In this module, you will be introduced to statistics and descriptive statistics. You will learn about various visualizations to understand the data. You will understand various measures of central tendency and measures of variability to analyze the given data for more insights.
What's included
9 videos4 readings7 assignments1 plugin
In this module, you will be introduced to the basics of set theory and probability. You will learn about the axioms of probability and conditional probability. You will understand the difference between dependent and independent events. You will also explore one of the important concepts in data science (machine learning), i.e., Bayes’ formula.
What's included
13 videos3 readings14 assignments1 plugin
In this module, you will learn how to generalize the events and their outcomes by a variable, that is, a random variable. You will explore types of random variables. You will gain an understanding of a mathematical expectation. You will further learn about the procedure to find the mean and variance using mathematical expectation. This module also covers the probability distribution function.
What's included
14 videos3 readings14 assignments1 plugin
In this module, you will learn about various discrete probability distributions. You will be able to understand Binomial and probability distributions with their corresponding probability distribution functions. You will also learn about the mean and variance of Binomial and Poisson distributions.
What's included
13 videos2 readings14 assignments
In this module, you will learn continuous probability distributions in general and normal/Gaussian distribution in particular. You will gain an understanding of the mean and variance of normal distribution. You will also explore the standard normal distribution with the help of normal distribution tables. Furthermore, you will be introduced to other continuous distributions like uniform distribution and Gamma distribution.
What's included
14 videos3 readings14 assignments
In this module, you will learn the importance of sampling and various sampling techniques. You will be introduced to sampling distribution, which plays an important role in understanding data. You will learn about the central limit theorem that will help you understand the use of normal distribution in many situations. Then, you will be introduced to the next step in sampling, that is, estimation. You will also gain an understanding of the t- and chi-square distribution.
What's included
15 videos2 readings16 assignments1 plugin
In this module, you will learn to identify and validate hypotheses using various statistical techniques, including sampling. You'll cover forming hypotheses, type I and type II errors, and their impact on test significance and power. The module also explores hypothesis testing with proportions, handling both large and small samples, and validating multiple proportions using the chi-square test.
What's included
27 videos5 readings20 assignments
In this module, you will learn how to understand the relation between two variables in the given data and the types of correlation that exists between two variables. You will be able to find coefficient correlation to establish this. The module answers why it is important to use the given data for future prediction for which regression is helpful. This module will also help you understand simple linear regression with the help of normal equations and their matrix form.
What's included
12 videos2 readings8 assignments
In this module, you will learn how to predict when nonlinearity exists in the data. With the learnings from simple linear regression, you will understand the regression for prediction when nonlinearity exists in the data. Furthermore, in nonlinear regression, you will focus on polynomial regression.
What's included
9 videos2 readings4 assignments1 plugin
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Instructor
Birla Institute of Technology & Science, Pilani
10 Courses32,180 learners
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