Completion

CERTIFICATE

Data Analysis Tools

Name: Data Analysis Tools
Rating: 4.5 (414 reviews)

Master statistical testing methods including ANOVA, Chi-Square, and Pearson correlation using Python or SAS.

Course Cost

Free course

Intermediate

Skill Level

9 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis and Interpretation Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.5

46,240 Enrolled

Jen Rose

Lisa Dierker

English

4.5

46,240 Enrolled

English

Jen Rose

Lisa Dierker

What you'll learn

Develop and test statistical hypotheses effectively

Apply ANOVA for analyzing relationships between variables

Conduct Chi-Square tests for categorical data analysis

Perform Pearson correlation analysis

Explore statistical interactions and moderation effects

Skills you'll gain

Statistical Analysis

ANOVA

Chi-Square Testing

Pearson Correlation

Hypothesis Testing

Data Analysis

Statistical Software

Python Programming

SAS Programming

Statistical Inference

This course includes:

3.2 Hours PreRecorded video

1 peer review per module

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Share your certificate with prospective employers and your professional network on LinkedIn.

CREATED BY

PROVIDED BY

Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

There are 4 modules in this course

This comprehensive course teaches fundamental statistical testing methods for data analysis. Students learn to develop and test hypotheses using various statistical techniques including ANOVA, Chi-Square tests, and Pearson correlation analysis. The curriculum covers probability in inference, hypothesis testing procedures, and statistical interactions. Through hands-on practice with either SAS or Python, learners gain practical experience in selecting and applying appropriate statistical tests for different types of data and research questions.

Hypothesis Testing and ANOVA

Module 1 · 4 Hours to complete

This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course.

Skills you'll gain

Statistical Analysis

Hypothesis Testing

ANOVA

Probability Testing

This course includes:

14 Videos

11 Reading Materials

Chi Square Test of Independence

Module 2 · 2 Hours to complete

This session shows you how to test hypotheses in the context of a Chi-Square Test of Independence (when you have two categorical variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a Chi-Square Test of Independence.

Skills you'll gain

Statistical Analysis

Chi-Square Test

Categorical Analysis

Hypothesis Testing

This course includes:

7 Videos

3 Reading Materials

Pearson Correlation

Module 3 · 1 Hours to complete

This session shows you how to test hypotheses in the context of a Pearson Correlation (when you have two quantitative variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a correlation coefficient.

Skills you'll gain

Statistical Analysis

Correlation Analysis

Pearson Correlation

Quantitative Analysis

This course includes:

4 Videos

2 Reading Materials

Exploring Statistical Interactions

Module 4 · 2 Hours to complete

In this session, we will discuss the basic concept of statistical interaction (also known as moderation). In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often characterized statistically as an interaction.

Skills you'll gain

Statistical Analysis

Moderation Analysis

Statistical Interaction

Variable Relationships

This course includes:

9 Videos

2 Reading Materials

Fee Structure

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

4.8

Reviews

92,317

Students

Courses

Jen Rose

Jennifer Rose serves as Director of the Center for Pedagogical Innovation and Professor of the Practice at Wesleyan University's Quantitative Analysis Center. Along with colleague Lisa Dierker, she co-developed the "Passion-Driven Statistics" curriculum, which has transformed statistics education through project-based learning approaches. Her innovative teaching methods have earned multiple National Science Foundation grants to disseminate their educational model nationwide. As Director of the Institutional Review Board and Professor of the Practice in the Center for Pedagogical Innovation, she has significantly influenced how statistics is taught to undergraduate students. Her course "Intro to Statistical Consulting" provides students with real-world experience by connecting them with nonprofits for data analysis projects. Her work has extended the reach of this innovative teaching approach to thousands of students globally through online platforms and institutional partnerships.

4.8

Reviews

92,317

Students

Courses

Lisa Dierker

Lisa Dierker, Professor of Psychology at Wesleyan University, has transformed statistical education through her innovative "Passion Driven Statistics" curriculum. Her expertise spans chronic disease epidemiology and advanced statistical methods, leading to significant National Science Foundation funding for developing accessible, project-based teaching approaches. Her collaborative work across disciplines including public health, medicine, engineering, and neuroscience has produced groundbreaking research while making statistics more engaging for diverse student populations. Through her leadership in curriculum development and cross-disciplinary research, she has significantly influenced how quantitative methods are taught at the undergraduate level, while maintaining active research in epidemiology and public health.

Intermediate

Skill Level

9 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.

Data Analysis Tools

What you'll learn

Skills you'll gain

This course includes:

Get a Completion Certificate

Get a Completion Certificate

Top companies offer this course to their employees

There are 4 modules in this course

Hypothesis Testing and ANOVA

Skills you'll gain

This course includes:

Chi Square Test of Independence

Skills you'll gain

This course includes:

Pearson Correlation

Skills you'll gain

This course includes:

Exploring Statistical Interactions

Skills you'll gain

This course includes:

Fee Structure

Reviews

Faculties

Frequently asked Questions

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Specialization?

What is the refund policy?

Is financial aid available?