Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions.

The Data Analysis and Interpretation Specialization takes you from data novice to data analyst in just four project-based courses. You’ll learn to apply basic data science tools and techniques, including data visualization, regression modelling, and machine learning. Throughout the Specialization, you will analyze research questions of your choice and summarize your insights. In the final Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. These instructors are here to create a warm and welcoming place at the table for everyone. Everyone can do this, and we are building a community to show the way.

4 Courses
Complete 4 courses in the suggested order.
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Capstone Project
Tackle the Capstone Project, designed to apply skills you’ve learned.
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Certificate
Earn a Certificate to share your success with the world.

Course 1: Data Management and Visualization

Commitment 4 weeks of study, 4-5 hours/week

Have you wanted to describe your data in more meaningful ways? Interested in making visualizations from your own data sets? After completing this course, you will be able to manage, describe, summarize and visualize data. You will choose a research question based on available data and engage in the early decisions involved in quantitative research. Based on a question of your choosing, you will describe variables and their relationships through frequency tables, calculate statistics of center and spread, and create graphical representations. By the end of this course, you will be able to: – use a data codebook to decipher a data set – identify questions or problems that can be tackled by a particular data set – determine the data management steps that are needed to prepare data for analysis – write code to execute a variety of data management and data visualization techniques

To read more about the details of this course, click here!

Course 2: Data Analysis ToolsData Analysis and Interpretation Specialization

Do you want to answer questions with data? Interested in discovering simple methods for answering these questions? Hypothesis testing is the tool for you! After completing this course, you will be able to:

  • identify the right statistical test for the questions you are asking
  • apply and carry out hypothesis tests
  • generalize the results from samples to larger populations
  • use Analysis of Variance, Chi-Square, Test of Independence and Pearson correlation
  • present your findings using statistical language.

 

To read more about the details of this course, click here!

Course 3: Regression Modeling in Practice


Commitment 4 weeks, 4 – 5 hours per week

What kinds of statistical tools can you use to test your research question in more depth? In this course, you will go beyond basic data analysis tools to develop multiple linear regression and logistic regression models to address your research question more thoroughly. You will examine multiple predictors of your outcome and identify confounding variables. In this course you will be introduced to additional Python libraries for regression modeling. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate residual variability. Finally, through blogging, you will present the story of your regression model using statistical language.

To read more about the details of this course, click here!

Course 4: Machine Learning for Data Analysis

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

To read more about the details of this course, click here!

Course 5: Data Analysis and Interpretation Capstone

The Capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the Specialization to address an important issue in society. You will use real world data to complete a project with our industry and academic partners. A major component of the Capstone project is for you to be able to choose the information from your analyses that best conveys results and implications, and to tell a compelling story with this information. You will report your findings in a professional quality report that can be shown to colleagues and potential employers to demonstrate the skills you learned by completing the Specialization.

To read more about the details of this course, click here!

At the end of the of the Data Analysis and Interpretation Specialization course you will be able to access and manage data using either the Python or SAS programming language, explore patterns and associations among variables, and use machine learning methods to develop predictive algorithms. Additionally, you will have a portfolio of hands-on project work that demonstrates your ability to apply all of these methods to real-world situations.–

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Data Analysis and Interpretation Specialization

Wesleyan University is dedicated to providing an education in the liberal arts that is characterized by boldness, rigor, and practical idealism.


At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit.

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