University of Pennsylvania
Statistics for Data Science Essentials
University of Pennsylvania

Statistics for Data Science Essentials

Chris Callison-Burch
Hamed Hassani

Instructors: Chris Callison-Burch

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level
Some related experience required
19 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Recently updated!

April 2024

Assessments

16 assignments

Course

Gain insight into a topic and learn the fundamentals

Intermediate level
Some related experience required
19 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the AI and Machine Learning Essentials with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

8 videos4 assignments

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.

What's included

6 videos4 assignments

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.

What's included

8 videos4 assignments

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.

What's included

7 videos4 assignments

Instructors

Chris Callison-Burch
6 Courses635 learners

Offered by

Recommended if you're interested in Probability and Statistics

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