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Welcome to the Ball State University course “Statistical Methods for Data Science.” 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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该课程共有5个模块
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.
涵盖的内容
12个视频7篇阅读材料1个作业1次同伴评审1个非评分实验室
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.
涵盖的内容
10个视频6篇阅读材料2个作业1个非评分实验室
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.
涵盖的内容
10个视频6篇阅读材料2个作业
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.
涵盖的内容
11个视频8篇阅读材料1个作业1个编程作业1次同伴评审1个非评分实验室
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.
涵盖的内容
14个视频5篇阅读材料1个作业1个编程作业1次同伴评审2个非评分实验室
攻读学位
课程 是 Ball State University提供的以下学位课程的一部分。如果您被录取并注册，您已完成的课程可计入您的学位学习，您的学习进度也可随之转移。
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