Data Analysis and Interpretation Specialization

Learn Data Science Fundamentals

Earn a Certificate

  • Specialization via Coursera and Wesleyan University
  • $395 for 6-7 months
  • 3 courses + capstone project
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Data Analysis and Interpretation
★★★★★ (1 Review)
Drive real world impact with a four-course introduction to data science.
Credential Type
6-7 months

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 modeling, 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.

Incentives & Benefits

When you complete this Specialization, you’ll have a portfolio of hands-on data analysis and interpretation that demonstrates your ability to solve real-world problems by applying a variety of analytical methods.

What You'll Learn

  • Access and manage data using either the Python or SAS programming language
  • Explore patterns and associations among variables
  • Use machine learning methods to develop predictive algorithms

Recommended Background

    ★★☆☆☆ (5) 4 weeks 26th Jun, 2017
    Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.
    ★★★☆☆ (4) 4 weeks 26th Jun, 2017
    In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
    ★★★★☆ (3) 4 weeks 30th Jun, 2017
    This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. 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 the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
    ★★★★☆ (4) 4 weeks 26th Jun, 2017
    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.

    1 Review.

    completed this credential in Nov 2016.

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    1 review

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