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Data Management and Visualization

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Data Management and Visualization

This course is part of Data Analysis and Interpretation Specialization

Instructor: Lisa Dierker

85,454 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

945 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

94%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

945 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

94%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

91% of learners achieved a positive career outcome

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This course is part of the Data Analysis and Interpretation Specialization

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There are 5 modules in this course

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.

Module details

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

What's included

5 videos5 readings1 peer review

5 videosTotal 31 minutes

Welcome Video6 minutes
Video Lesson - Steps in data analysis7 minutes
Video Lesson - What do we mean by data?5 minutes
Video Lesson - Datasets and codebooks7 minutes
Video Lesson - Developing a research question6 minutes

5 readingsTotal 50 minutes

Course codebooks10 minutes
Course Data Sets10 minutes
Getting Set Up for the Assignments10 minutes
Tumblr Instructions10 minutes
Troubleshooting Your Tumblr Assignment Blog Link10 minutes

1 peer reviewTotal 60 minutes

Getting Your Research Project Started60 minutes

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).

What's included

8 videos8 readings1 peer review

8 videosTotal 58 minutes

SAS Lesson 1 - Defining exploratory data analysis3 minutes
SAS Lesson 2 - SAS coding conventions9 minutes
SAS Lesson 3 - Running your program and examining frequency distribution10 minutes
SAS Lesson 4 - Refining your research question by selecting rows6 minutes
Python Lesson 1 - Defining Exploratory Data Analysis3 minutes
Python Lesson 2 - Python Coding Conventions10 minutes
Python Lesson 3 - Running your program and examining frequency distributions10 minutes
Python Lesson 4 - Refining your research question by selecting rows7 minutes

8 readingsTotal 80 minutes

Choosing SAS or Python10 minutes
Getting Started with SAS10 minutes
Getting Started with Python10 minutes
Codebook for Video Examples10 minutes
SAS Program for Video Examples10 minutes
Python Program for Video Examples10 minutes
Uploading Your Own Data to SAS10 minutes
Assignment Sample10 minutes

1 peer reviewTotal 60 minutes

Running Your First Program60 minutes

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.

What's included

8 videos4 readings1 peer review

8 videosTotal 45 minutes

SAS Lesson 1 - Setting aside missing data6 minutes
SAS Lesson 2 - Coding in valid data and recoding values3 minutes
SAS Lesson 3 - Creating secondary variables7 minutes
SAS Lesson 4 - Grouping variables within individual variables3 minutes
Python Lesson 1 - Setting aside missing data8 minutes
Python Lesson 2 - Coding valid data and recoding values6 minutes
Python Lesson 3 - Creating secondary variables8 minutes
Python Lesson 4 - Grouping values within individual variables4 minutes

4 readingsTotal 40 minutes

SAS Program for Video Examples10 minutes
Python Program for Video Examples10 minutes
Codebook for Video Examples10 minutes
Assignment Sample10 minutes

1 peer reviewTotal 60 minutes

Making Data Management Decisions60 minutes

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.

What's included

12 videos5 readings1 peer review

12 videosTotal 108 minutes

SAS Lesson 1 - Graphing individual variables6 minutes
SAS Lesson 2 - Describing distributions visually7 minutes
SAS Lesson 3 - Measures of center and spread12 minutes
SAS Lesson 4 - Designing the role each of your variables will play5 minutes
SAS Lesson 5 - Graphing decisions: categorical response variables13 minutes
SAS Lesson 6 - Graphing decisions: quantitative response variable10 minutes
Python Lesson 1 - Graphing individual variables7 minutes
Python Lesson 2 - Describing distributions visually7 minutes
Python Lesson 3 - Measures of Center and Spread12 minutes
Python Lesson 4 - Designing the role each of your variables will play5 minutes
Python Lesson 5 - Graphing Decisions: Categorical response variables14 minutes
Python Lesson 6 - Graphing decisions: quantitative response variable11 minutes

5 readingsTotal 50 minutes

Graphing Decisions Flowchart10 minutes
SAS Programs for Video Examples10 minutes
Python Programs for Video Examples10 minutes
Codebooks for Video Examples10 minutes
Assignment Sample10 minutes

1 peer reviewTotal 60 minutes

Creating graphs for your data60 minutes

What's included

3 readings

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Instructor

Instructor ratings

(78 ratings)

Lisa Dierker

Wesleyan University

5 Courses153,143 learners

Offered by

Wesleyan University

Learner reviews

5 stars
64.08%
4 stars
23.83%
3 stars
6.14%
2 stars
2.01%
1 star
3.91%

Showing 3 of 945

Reviewed on Jan 15, 2021

Great for a beginner.. the flexibility which is given by the course with our thoughts and imaginations was too good.. must-do for anyone to begin the Data Analysis career <3

Reviewed on Jun 1, 2020

I learnt a lot from this course in data management and visualization and enjoyed the assignment parts of the course. I also gained insights in making data management decisions!

Reviewed on Jun 30, 2018

This was a great course for beginners in Python. I found all the videos to be detailed and helpful in understanding the code and programming in Python

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