Ball State University
DSCI 602: Statistical Methods for Data Science (2024)
Ball State University

DSCI 602: Statistical Methods for Data Science (2024)

Vinayak Tanksale
Munni Begum

Instructors: Vinayak Tanksale

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Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
41 hours to complete
3 weeks at 13 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
41 hours to complete
3 weeks at 13 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree

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Recently updated!

November 2024

Assessments

4 quizzes, 4 assignments

Taught in English

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There are 5 modules in this course

Welcome! In part 1 of this module you will complete a recommended reading about the course and post on a discussion board entry to introduce yourself to your classmates. In part 2 of this module, we will review probability theory and its applications to real-world problem-solving.  Probability is a measure of the chance of occurrence of a future event. For example, what is the probability that you will see two heads when you toss two coins? It is ¼, right? Why do you care about learning probability? Here is a quote by the ancient Greek philosopher Democritus “Everything existing in the universe is the fruit of chance”. Thus, it is important for us to have basic probability knowledge. In data science,  probability helps us understand how data is generated and plays a major role in inference and prediction.In this module, we will review three definitions of probability, probability laws, conditional probability, and Bayes' rule. Knowledge of conditional probability is essential in most practical problems. Bayes' rule provides a mechanism for determining conditional probabilities when prior probabilities are given. 

What's included

12 videos7 readings2 quizzes1 peer review1 ungraded lab

In this module, we will talk about random variables which are basically a mapping or correspondence between the sample space of a random experiment and the real number system.

What's included

10 videos6 readings2 quizzes1 ungraded lab

In this module, we will learn about discrete probability distributions based on what is known as Bernoulli Trials. You will learn about Bernoulli, Binomial, Geometric, and Negative Binomial Distributions. These distributions are widely used in numerous applications including health and biomedical sciences, social sciences, environmental sciences, finance and business, and education among others.

What's included

10 videos6 readings2 assignments

This module covers continuous probability distributions. In the real world, not all random variables are discrete. For example, daily rainfall amount, the lifetime of an equipment, biological measures such as the body mass index or BMI and Cholesterol levels, and various test scores take values in intervals and are called continuous random variables.

What's included

11 videos8 readings1 assignment1 programming assignment1 peer review1 ungraded lab

In this module, we will revisit Normal distribution and its attractive properties. You will see how the law of large numbers can be used to approximate the distributions of sum or average of sample data.

What's included

14 videos5 readings1 assignment1 programming assignment1 peer review2 ungraded labs

Instructors

Vinayak Tanksale
Ball State University
3 Courses93 learners
Munni Begum
Ball State University
3 Courses82 learners

Offered by

Recommended if you're interested in Probability and Statistics

Build toward a degree

This course is part of the following degree program(s) offered by Ball State University. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

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