Statistics for Data Science Essentials

University of Pennsylvania via Coursera

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Overview

The “Statistics for Data Science Essentials” course provides a foundational understanding of statistical concepts critical for data science. It is designed for beginners and professionals aiming to enhance their analytical skills to interpret and make data-driven decisions effectively. Participants will explore descriptive statistics, probability theory, and inferential statistics, including hypothesis testing, confidence intervals, and regression analysis. The course emphasizes practical applications, showing how statistical methods underpin data science workflows, from data exploration to predictive modeling. Learners will work with real...

Syllabus

Week 1: Getting Started with Statistics for Data Science

In the first week of the course, well 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.

Week 2: Probability

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.

Week 3: Statistical Estimation

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.

Week 4: Confidence Intervals & Point Estimation

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.