Welcome to the Ball State University course 鈥淪tatistical Methods for Data Science.鈥� As the title suggests, this course provides fundamental concepts and methods for data-generating mechanisms such as probability models and inferential methods such as estimation and hypothesis testing. scientists. You will need the right tools and analytics methods to make good sense of data and to make data-driven decisions. We are going to take a systematic approach to build a strong foundation on probability and probability models, large sample theory as a bridge between probability theory and inference, and basic inferential processes. Please 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.
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There are 5 modules in this course
Welcome!听In part 1 of this module you will complete a recommended听reading听about the听course and听post听on a discussion board entry to introduce yourself to your classmates.听In听part听2听of听this module,听we will review probability theory and its applications听to real-world problem-solving.听听Probability听is a measure of听the chance听of听occurrence of a future event.听For example, what is the probability that you听will听see two heads when you toss two coins?听It is 录, right? Why do听you care about learning probability?听Here is a quote by the ancient Greek philosopher Democritus听鈥淓verything existing in the universe is the fruit of chance鈥�.听Thus, it is important for us to have basic probability knowledge. In听data听science,听听probability听helps听us听understand how听data听is听generated听and plays听a major role听in inference and prediction.In this module, we will review three definitions of probability, probability laws, conditional probability, and Bayes' rule.听Knowledge of conditional probability is essential in most practical problems. Bayes' rule provides a mechanism for determining conditional probabilities when prior probabilities are given.听
What's included
12 videos7 readings1 assignment1 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 assignments1 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
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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.鹿
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you鈥檝e earned a Course Certificate, even if you complete the course within the two-week refund period. .
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