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Statistics for Data Science Essentials

本课程是 AI and Machine Learning Essentials with Python 专项课程的一部分

位教师：Chris Callison-Burch

2,325 人已注册

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

12 条评论

中级等级

需要一些相关经验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

12 条评论

中级等级

需要一些相关经验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Comprehensively review probability and understand its role as a building block of data science.
Apply the central limit theorem, confidence intervals and the method of maximum likelihood to solving data science problems.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

16 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 AI and Machine Learning Essentials with Python 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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获得对主题或工具的基础理解
通过实践项目培养工作相关技能
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该课程共有4个模块

Review the basics of discrete math and probability before enhancing your probability skills and learning how to interpret data with tools such as the central limit theorem, confidence intervals and more. Complete short weekly mathematical assignments.

单元详情

In the first week of the course, we’ll introduce you to a broad definition of data science and go over some of its main building blocks. To prepare, we'll spend some time reviewing discrete math fundamentals. By the end of the week, we will solve our first data science task using random sampling.

涵盖的内容

8个视频1篇阅读材料4个作业

8个视频总计50分钟

Introduction to Statistics for Data Science Essentials3分钟
Week 1 Introduction: Getting Started with Data Science1分钟
Discrete Math Review I: Variables, Polynomials, Sets18分钟
Discrete Math Review II: Functions4分钟
Data Science in Simple Terms8分钟
Defining Data Science5分钟
Random Sampling I5分钟
Random Sampling II6分钟

1篇阅读材料总计1分钟

Opt-in to Penn Engineering Online Communications1分钟

4个作业总计240分钟

Learning Check - Data Science in Simple Terms20分钟
Learning Check - Random Sampling20分钟
Week 1 Assignment - Discrete Math and Random Sampling180分钟
Practice Learning Check - Discrete Math Review20分钟

The second week of our course is devoted to probability: since probability is the main language used by almost every data science concept, we will commit some time to deepening our understanding of it. By the end of the week, you will have far more tools in your probability toolkit, which will serve you throughout your AI and machine learning journey.

涵盖的内容

6个视频4个作业

6个视频总计39分钟

Week 2 Introduction: Probability1分钟
Basic Probability I9分钟
Basic Probability II6分钟
Basic Probability III6分钟
Additional Probability I7分钟
Additional Probability II10分钟

4个作业总计240分钟

Learning Check - Basic Probability, Continued20分钟
Learning Check - Additional Probability20分钟
Week 2 Assignment - Probability180分钟
Practice Learning Check - Basic Probability20分钟

In this week, we will build up our general framework of statistical estimation, taking from several of the concepts we have discussed and more that we will continue to add this week. We will start by going over the sample mean, and we will analyze how good this is as an estimator. We will then explore the Central Limit Theorem, one of the most effective and widely-used tools in statistics and data science. We will also continue some probability review.

涵盖的内容

8个视频4个作业

8个视频总计53分钟

Week 3 Introduction: Statistical Estimation2分钟
Analysis of the Sample Mean: The Expectation10分钟
Analysis of the Sample Mean: The Variance10分钟
Another Example: Which Candidate Will Win?10分钟
General Framework4分钟
Additional Probability III7分钟
The Central Limit Theorem I6分钟
The Central Limit Theorem II5分钟

4个作业总计240分钟

Learning Check - General Framework of Statistical Estimation20分钟
Learning Check - The Central Limit Theorem20分钟
Week 3 Assignment - Statistical Estimation & The Central Limit Theorem180分钟
Practice Learning Check - Analysis of the Sample Mean20分钟

Now that we have learned the important machinery of the Central Limit Theorem, we are ready to learn about confidence intervals this week. Confidence intervals are the main quantities to characterize error bars in almost any area of data science and machine learning. After going through confidence intervals and some examples, we will also explore a more general perspective on estimation: point estimation.