Frequently Asked Questions

Flipped classroom learning

What is a flipped classroom?

The flipped classroom is a pedagogical approach in which the roles that class time and homework typically play in learning are reversed.

Instead of coming to class to listen to a lecture together and doing “homework” individually on your own time, this approach “flips” the model. You listen to lectures and acquire theoretical knowledge individually on your own time. Then you do work and solve problems together in the virtual classroom.

Watch this 2-minute YouTube video to learn more:

Learning management system (LMS) / self-paced content

What is a learning management system?

A learning management system, or LMS, is a system used to deploy and track online training. Data Society’s LMS is built using the LearnDash plugin for WordPress. It’s how we get the course materials to you and track your progress. You may also see it referred to as our online learning site, e-learning site, or online learning portal.

How do I reset my password?

Visit and follow the instructions to reset your password.

Can I share my username and password with colleagues or friends?

No. Students may not share their credentials with any other individuals.

How do I update my profile picture?

The inability to update the profile picture or avatar within a user’s profile is a known issue. There is no solution at this time.

What types of self-paced instruction does this course include?

Each lesson in this course includes a number of topics. Some topics are theoretical, while others are more practical. The theoretical topics are followed by quizzes so that you can check your understanding. The practical, coding-based topics are labeled with either (CW) for coding walkthrough or (E) for exercise, as described below:

  • (CW). Coding walkthroughs are instructor-led coding walkthroughs of previously discussed topics. You will be provided with the dataset used by the instructor as well as a breakdown of the steps followed.
  • (E). Exercises are your opportunity to practice coding independently. You will be asked to complete a series of steps in Python using a provided dataset. The results can be checked against a provided answer file.

Do I have to complete the topics in a specific order?

We recommend that you start with Lesson 1, Topic 1 and progress through the course in a linear fashion, as the topics build on one another.

However, we have not built in restrictions that require you to do so.

How much time will the self-paced content take me?

It depends. There are 3 hours, 29 minutes, 17 seconds of recorded video within this course’s three self-directed lessons, in addition to outside resources for you to explore, several knowledge checks to confirm your learning, and independent coding exercises. How long the lessons take you to complete will depend on factors such as your baseline knowledge of neural networks and how quickly you code.

What happens if I fail a quiz?

Quizzes are short knowledge checks to make sure that you have understood the major concepts in a given topic.

If you get a quiz question wrong, you will be invited to retake the quiz. You may retake the quiz as many times as you like.

Can I access course content on a phone or a tablet?

Much of the content for this course, including all of the videos, is fully optimized for mobile viewing.

However, topics identified as coding walkthroughs (CW) and exercises (E) require the use of a Python environment. We strongly encourage that you work through these topics using a desktop or laptop.

How long will I have access to the online course materials?

The course will remain accessible until at least June 1, 2021.

How long will the course forum be open?

The course forum will remain open until at least June 1, 2021.

However, Data Society’s experts will actively monitor the forums and respond to questions only until the date of the last instructor-led session on December 8, 2020.

When can I expect an answer to the question I asked on the forum?

Data Society will make its best effort to resolve questions about accessing the LMS and navigating course content as quickly as possible, within regular business hours.

Questions about course content, including troubleshooting in Python, should be answered within a 48-hour period.

If you see a question on the forum that you know the answer to, we welcome timely, accurate crowdsourced solutions.

Email for additional support.

Instructor-led session

Do I have to complete each of the self-paced topics and corresponding activities before the instructor-led session?

We have designed and sequenced this course to give you the best possible chance of success in meeting your learning objectives. We recommend that you complete every topic and activity prior to the instructor-led session, as substantial lecture-style review of the material is not planned.

How do I prepare for the instructor-led session?

The instructor-led session has two objectives:

(1) to answer your questions, and

(2) to give you the opportunity to apply the concepts you have learned to your own dataset with the benefit of an instructor’s and classmates’ insights.

Therefore, there’s a few things that you can do to prepare:

  • Make note of any outstanding questions that you have
  • Identify a dataset on which you’d like to practice and have it with you.
  • Have your computer accessible, with Python installed for in-class coding work. 
  • Download the Zoom client, if you are able. We recommend using the Zoom client to maximize the tool’s functionality, but understand if students must use the browser-based version. You can read more about Zoom and its functionality here.

What type of dataset is best for the instructor-led session?

You are free to choose any dataset that is of interest to you. However, there are a few ideal characteristics:

  • Is publicly available
  • Can be shared with your fellow classmates
  • Of a relevant subject matter (e.g., engineering, materials science, etc.)
  • Contains > 1,000 samples
  • Includes numeric data
  • Is multivariate (more than 5 variables)
  • Is well suited for either classification or regression (meaning you are looking for a binary target, or know what element you want to predict)

What if I don’t have a dataset to use during the instructor-led session?

We have identified three datasets for students who are unable to locate a dataset that meets the requirements. They are:

  • Steel Plates Faults: This multivariate dataset contains 1,941 instances of steel plate faults, classified into 7 different types.
  • Concrete Compressive Strength: This multivariate dataset contains 1,030 instances and is good for regression analysis. 
  • Superconductivity Data: This multivariate dataset contains 21,263 instances of data on superconductors and their relevant features.

Use this link to download all three.

You may also simulate a dataset, if this is a technique familiar to you. You can read more on the topic here.

How will the instructor-led session be conducted?

The session will be conducted using the Zoom platform.  You can read more about Zoom and its functionality here.

You should have received a calendar invite with a link to the Zoom meeting on Monday, November 30. If you did not receive this information, email

How do I join a Zoom meeting?

Watch this 1-minute video on the Zoom website to walkthrough the process for joining a Zoom meeting: Joining a Meeting

Do I have to install the Zoom client to attend the instructor-led session?

You are not required to download the Zoom client for the instructor-led session, and we understand that this is not permitted on your NASA-issued laptops. However, the use of whiteboard functionality and other tools only work if you have Zoom downloaded on your desktop so please understand that this functionality will not be available.


How do I install the software and packages necessary for this course?

Anaconda and package install instructions are provided here. The required pip_env.txt file is located here.

Can I use JupyterLab instead of Jupyter Notebook for this course?

The course materials (all videos, for example) are all done using Jupyter Notebook and the installation instructions are made to support that.

However, for the purposes of this course the code works for either without any functional differences. You can therefore choose to use either use Notebook or Lab for the course.

What packages are used in this course?


What issues have been reported when trying to follow the installation instructions?

  • Issue: “conda activate nasa-nn” only works in conda 4.8 and later. Solution: “conda update conda” will provide the needed update.
  • Issue: Participants do not have a “nasa-nn” folder on their Desktop when they reach the “cd Desktop/nasa-nn” step. Solution: Create one! This is where you will download the materials for each lesson.
  • Issue: tensorflow wasn’t installed by the pip_env.txt list. Solution: see “What issues have been reported while trying to complete the exercises?” below on this page.

What issues have been reported when trying to complete the exercises?

  • Issue: In the Lesson 1, Topic 5 notebook, tensorflow wasn’t installed by the pip_env.txt list.
    • Solution: The script calls for kiwisolver=1.3.0, and the install was trying to build it from source and failing, so subsequent packages weren’t installed.
      1. Delete the nasa-nn environment.
      2. Perform a conda update on conda, anaconda, python and –all on the base environment.
      3. Recreate the nasa-nn environment (see slide 8).
      4. Change pip_env.txt to install kiwisolver=1.3.1.
      5. Re-run the pip environment load (see slide 10).

Who should I contact if I have difficulty installing software or packages?

If you have any questions trying to install software or packages, contact Herb Schilling (