In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
Inferential Statistical Analysis with Python
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Inferential Statistical Analysis with Python
This course is part of Statistics with Python Specialization
Instructors: Brenda Gunderson
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4.6
(905 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 21 hours
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What you'll learn
Determine assumptions needed to calculate confidence intervals for their respective population parameters.
Create confidence intervals in Python and interpret the results.
Review how inferential procedures are applied and interpreted step by step when analyzing real data.
Run hypothesis tests in Python and interpret the results.
Skills you'll gain
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Assessments
7 assignments
Taught in English
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This course is part of the Statistics with Python Specialization
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There are 4 modules in this course
In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made. On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.
What's included
6 videos7 readings1 assignment1 discussion prompt3 ungraded labs
In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
What's included
10 videos5 readings3 assignments6 ungraded labs
In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.
What's included
10 videos2 readings2 assignments1 peer review1 discussion prompt6 ungraded labs
In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.
What's included
6 videos5 readings1 assignment
Instructors
Instructor ratings
4.7 (143 ratings)Offered by
Recommended if you're interested in Data Analysis
Akamai Technologies, Inc.
Tufts University
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