Statistics for Data Science Essentials

This course is part of AI and Machine Learning Essentials with Python Specialization

Instructors: Chris Callison-Burch

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4 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

16 assignments

Taught in English

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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

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.

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 videos1 reading4 assignments

8 videos Total 50 minutes

Introduction to Statistics for Data Science Essentials 3 minutes
Week 1 Introduction: Getting Started with Data Science 1 minute
Discrete Math Review I: Variables, Polynomials, Sets 18 minutes
Discrete Math Review II: Functions 4 minutes
Data Science in Simple Terms 8 minutes
Defining Data Science 5 minutes
Random Sampling I 5 minutes
Random Sampling II 6 minutes

1 reading Total 1 minute

Opt-in to Penn Engineering Online Communications 1 minute

4 assignments Total 240 minutes

Learning Check - Data Science in Simple Terms 20 minutes
Learning Check - Random Sampling 20 minutes
Week 1 Assignment - Discrete Math and Random Sampling 180 minutes
Practice Learning Check - Discrete Math Review 20 minutes

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

6 videos Total 39 minutes

Week 2 Introduction: Probability 1 minute
Basic Probability I 9 minutes
Basic Probability II 6 minutes
Basic Probability III 6 minutes
Additional Probability I 7 minutes
Additional Probability II 10 minutes

4 assignments Total 240 minutes

Learning Check - Basic Probability, Continued 20 minutes
Learning Check - Additional Probability 20 minutes
Week 2 Assignment - Probability 180 minutes
Practice Learning Check - Basic Probability 20 minutes

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

8 videos Total 53 minutes

Week 3 Introduction: Statistical Estimation 2 minutes
Analysis of the Sample Mean: The Expectation 10 minutes
Analysis of the Sample Mean: The Variance 10 minutes
Another Example: Which Candidate Will Win? 10 minutes
General Framework 4 minutes
Additional Probability III 7 minutes
The Central Limit Theorem I 6 minutes
The Central Limit Theorem II 5 minutes

4 assignments Total 240 minutes

Learning Check - General Framework of Statistical Estimation 20 minutes
Learning Check - The Central Limit Theorem 20 minutes
Week 3 Assignment - Statistical Estimation & The Central Limit Theorem 180 minutes
Practice Learning Check - Analysis of the Sample Mean 20 minutes

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 videos1 reading4 assignments

7 videos Total 57 minutes

Week 4 Introduction: Confidence Intervals & Point Estimation 1 minute
Confidence Intervals I 12 minutes
Confidence Intervals II 11 minutes
Point Estimation 5 minutes
Variance Estimation: Biased vs Unbiased, Part 1 11 minutes
Variance Estimation: Biased v. Unbiased, Part 2 12 minutes
Two Principles for Point Estimation 5 minutes

1 reading Total 1 minute

Opt-in to Penn Engineering Online Communications 1 minute

4 assignments Total 240 minutes

Learning Check - Point Estimation, Part I 20 minutes
Learning Check - Point Estimation, Continued 20 minutes
Week 4 Assignment - Confidence Intervals & Point Estimation 180 minutes
Practice Learning Check - Confidence Intervals 20 minutes

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Instructors

Chris Callison-Burch

7 Courses 11,454 learners

Offered by

University of Pennsylvania

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