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.
Overview
Syllabus
- Selecting a research question
- We would like to welcome you to Wesleyan University's Data Analysis and Interpretation Specialization. In this session, we will discuss the basics of data analysis. Your task will be to select a data set that you would like to work with and to review available code books that help you develop your own research question. You will also set up a Tumblr blog that will allow you to reflect on these experiences, submit assignments and share your work with others throughout the course. First, you may want to check out the welcome video
- Writing your first program - SAS or Python
- In this session, we will discuss how to write a basic program that allows you to load a data set and examine frequency distributions. Your task will be to write a program that helps you to explore the variables you have selected for your own research question. You may choose either Python or SAS. Both are made freely available, and we have created a helpful guide to support you in making the decision. Once you have selected your platform, just follow the instructions in the appropriate "GETTING STARTED...." file, and then check out this week's video lessons aimed at helping you write and run your first program. You need only view the lessons for one of the statistical platforms (SAS or Python).
- Managing Data
- In this session, we will help you to make and implement even more decisions with data. Statisticians often call this task 'data management', while computer scientists like the term 'data munging'. Whatever you call it, it is a vital and ongoing process when working with data. Your task will be to write a program that manages the variables you have selected for your own research question.
- Visualizing Data
- In this session we will discuss descriptive statistics and get you visualizing your newly data managed variables individually and as graphs showing the relationships between them.
- Supplemental Materials (All Weeks)
Taught by
Lisa Dierker and Jen Rose
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Reviews
2.4 rating, based on 5 Class Central reviews
4.4 rating at Coursera based on 935 ratings
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This is the first course in what was a new (at the time I took it) specialization covering data analysis using Python 3 or SAS. This first course mostly covered data manipulation, summarization, and visualization. The production value for this cou…
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They put a lot of effort in, that's for sure - it was just a bit much, for my taste. The videos are overloaded with visuals and sound, ever-changing backgrounds, animated writing... It's almost impossible to focus on the topic at hand.
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This class is ok if you are a beginner. They have decent videos that give good SAS and/or Python programming tips. It is ok to watch and learn, but expect ZERO support if you have any questions. They want you to create a research question, but then leave you hanging when anyone asks questions. The instructor(s) are non existent. It is your typical create & forget courses. Unfortunately, there are not that many courses that help learn/review SAS programming.
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Alex Ivanov
