Welcome to the Ball State University course “Statistical Methods for Data Science.” This course is about Statistical Methods for data scientists. To make good sense of data, you will need the right tools and analytics methods. We are going to take a systematic approach to learn about the right tools and methods you can use. Note that as data scientists it is important for us to be able to connect data and learn how the world around us works. To accomplish this challenging task, we will learn how we can connect data through probability theory and statistical models and take actionable decisions, confirm a hypothesis, or make predictions.
DSCI 602: Statistical Methods for Data Science (2024)
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
Instructor
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Recommended if you're interested in Probability and Statistics
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Johns Hopkins University
Stanford University
University of Colorado Boulder
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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