Welcome to Applied Plotting, Charting & Data Representation in Python!

Prerequisites

In order to be successful in this course you will need to know how to program in Python. This course is part of a specialization and the expectation is that you have completed the first course, Introduction to Data Science in Python .

Week by week

In week one you will be introduced to the principles of data visualization. This week’s assignment asks that you carefully read Alberto Cairo's work, Graphics Lies, Misleading Visuals . You will locate and identify a visual that displays misleading information. You will interpret the features of the visual in order to identify the mechanism(s) that is/are used by the "encoder" to mislead the "decoder." For each mechanism that you identify, you will explain how it was used to mislead.

In week two you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.

In week three you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.

In week four , then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.

Enrollment Options

Coursera has made the decision to make Specializations available by monthly subscription. This means you can choose to pay a monthly fee to access all of the courses in a specific Specialization. The Applied Data Science with Python specialization will be switching to this subscription model in April 2017 once the third course in the specialization launches.

Coursera’s switch to monthly subscriptions comes with another change -- for those learners who choose the “Audit Only” enrollment, you will no longer be able to submit assignments for grades nor see answers for those assignments. You will still have access to all the course materials but you will not be graded on your work, nor see answers to graded assignments.

For further information on the different enrollment options for Coursera courses, please visit the Enrollment Options Help page . If you have feedback about the enrollment options shared on the Enrollment Options page, you can share your thoughts with Coursera in this survey .

Grading and Assignments

The lectures will provide you with some guidance for completing assignments, but you will need to take initiative and look beyond assignment instructions in order to be successful. You'll need to know how to ask questions in the discussion forums of your peers, and seek out new information through web searches and Stack Overflow . Be sure to also check out the Additional Python Resources .

If you are not sure what kind of output is required, or think there is a need for more clarity, please head to the course discussion forums. Note that some assignments and in video quizzes may not be mobile friendly.

Some assignments allow you to download and view your fellow learner’s code and/or data. If you want to look at the learner's code, we recommend that you open it through the Jupyter notebook system on the Coursera platform as that will be more secure. Please ensure that all data you share is publicly available, since you will be sharing these data with other learners.

Peer Evaluation

All of the assignments for this course require that you create a visual that follows the principles that Professor Brooks outlines in his lectures. We use peer-grading to evaluate these assignments because it is essential that the aesthetic quality of the visuals are evaluated by a human, rather than a machine.

In order for this system to work, learners must carefully evaluate the work of their peers using a rubric that has been created by Professor Brooks and the course staff. Please read the rubric carefully and choose the options that most closely match the elements of the assignment you are grading.

Just a reminder to visit Coursera’s Code of Conduct and to abide by guidelines there. It is important when giving feedback to your peers to be polite and to be sensitive to the diversity of cultures and backgrounds of learners in your course.

Please also review Coursera’s help articles on peer reviewed assignments .

Working Offline

While the Coursera platform has an integrated Jupyter Notebook system, you can work offline on your own computer by installing Python 3.5+ and the Jupyter software packages. For more details, consult the Jupyter Notebook FAQ . Note that this course uses matplotlib 2.0.

If you would like to have copies of the lecture slides presented, please see the Supplementary Materials section of the Coursera platform.

Accessibility

We strive to develop fully accessible courses. Occasionally, some of our content does not fully meet our accessibility goals. Please use this form to inform us of any accessibility issues you are experiencing in this course.

Help!

If you're having problems, here are a couple of great places to go for help:

If the problem is with the Coursera platform such as verification on assignments, in video quiz problems, or the Jupyter Notebooks, please check out the Coursera Learner Support Forums

If the problem deals with understanding the assignment or how to use the Jupyter Notebooks, please read our Jupyter Notebook FAQ page in the course resources

If you have questions with the content of the course, or questions about programming in python or with the toolkits described, you can contact your peers and the course instructors in the discussion forums, or go to Stack Overflow

Having trouble accessing your previously submitted assignments? If your session has ended, you can access these again by selecting the "Switch Session" option. Details for how to select this can be found in this learner help center article. If you still have issues accessing your materials after switching sessions, please reach out to Coursera learner support via our online chat forums in the Learner Help Center .

In-Video Questions (IVQs)

In this course, in-video questions or IVQs may appear during lectures to help you learn as well as assess your understanding of the content. IVQs are optional and do not count towards your overall course grade.

Types of in-video questions

Many of the lectures contain in-video questions (IVQs). These questions are presented in a variety of formats. Some will ask you to write or think about a concept from the video. Others will ask for a short answer. Still others may ask you to choose from a multiple-choice list of answers. If an IVQ is a survey or a poll, you will see a summary of responses from other learners after you respond. You can look at the question again later to see new summary data as more of your peers answer.

Some IVQs also contain runnable code blocks. These IVQs allow you to practice the coding concepts during the lecture. In this course, these types of IVQs will usually be directly followed with the solution code.