Python is a free, a general-purpose, and portable programming language. It is easy to use with its simple syntax and readability, which makes the code easy to understand and maintain (see the Berkeley blog). Python can be extended via libraries that can be used to tackle problems in machine learning, data analysis, and beyond. It has a vast ecosystem and a dynamic user’s community that make Python accessible to everyone.

The focus of this course series will be on Data Science. According to Wikipedia, Data Science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena with large volumes of data”. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. It aims to find unseen patterns, derive meaningful information, and make decisions based on the data.

Because of its features, Python is one of the preferred programming languages that data scientists can use to explore and analyze their datasets. The growth of Python in Data Science has gone hand in hand with that of its Pandas library, which opened the use of Python for data analysis to a broader audience by enabling it to deal with row-and-column datasets, import CSV files, and much more.

This course seriesintroduces the fundamental concepts of the Python programming language. In addition, it presents Python packages (NumPy, Matplotlib, and Pandas) used for data manipulation and visualization. The basics of Python combined with the Data Science topics, provide the necessary foundations for Exploratory Data Analysis and Machine Learning.

This course series is divided in two sessions:

• Introductions to Python: Meant for interns who are familiar with basic computer programming, but are new in Python.
• Python for Data Science: Meant for interns who have Python knowledge and plan to work on a project involving Data Science.

In the Introduction to Python, you will learn:

• Python basic syntax, variables, and types
• Conditional statements and loops
• Data structures: list, tuple, dictionary, set
• Functions and modules
• The datetime module
• I/O with text files
• Scripting and packaging

At the end of the Introduction to Python sessions, learners will be able to write a basic Python application using functions and modules.

In the Python for Data Science, the following will be covered:

• NumPy arrays
• Basic visualization with Matplotlib
• Data manipulation (reading data file, performing statistical analysis, visualization, handling time series data) with Pandas

Those with some knowledge of Python when they start the Python for Data Science session will be able to write their own Python scripts to read remote text files or tables, perform data wrangling, carry out basic data analytics and visualize data.

To take any of the sessions, you are expected to:

• Have a gmail account.
  • The gmail account will be use to give you access to the NASA Center for Climate Simulation (NCCS) Science Data Managed Cloud Environment (SMCE). The SMCE is a self-contained cloud platform that we will use for all our classes.
  • Google Colab will be used as a backup teaching platform.
• Have a Github account. Github is a web-based collaborative tool to discover, share and build software. You might need to share what you do with your mentor.

The SMCE can be accessed through the user portal and will be granted on June 7. You will use your gmail credential to automatically access the system. The system will ask you to provide your gmail password or give you automatic access if you are already logged in into gmail (through your browser).

To have an account on the SMCE, we recommend that you provide (once) your name and gmail userid by filling out the this online form.

All the classes will be provided using Jupyter notebooks. We will be explaining the features of Jupyter notebooks as we move along. In case you want to learn more, click on: .

Regardless of their level of proficiency in Python, all interns are expected to take the courses:

• Introduction Version Control with Git.
• Creating and Managing Git Repositories in GitHub.

Date	Room Number	Teams Invitation	Lecture Topic	Interactive Link
Friday, June 7	Bldg 34 W130	Teams Link	Introduction to Version Control with Git
Monday, June 10	Bldg 34 W130	Teams Link	Creating and Managing Git Repositories in GitHub

Date	Room Number	Teams Invitation	Lecture Topic	Interactive Link
Friday, June 14	Bldg 34 W305	Teams Link	Running Python
			Data Types
			Conditional Statements
			Loops
Monday, June 17	Bldg 34 W130	Teams Link	Advanced Data Types
			Functions
			Modules
Friday, June 21	Bldg 34 W305	Teams Link	datetime Module
			I/O on Text Files
Monday, June 24	Bldg 34 W130	Teams Link	Scripting and Packaging

To deepen your Python skills, you may consider doing the Beginner Python Exercises by Holy Python.

This section is more for interns who will have a Data Science related project.

Date	Room Number	Teams Invitation	Lecture Topic	Interactive Link
Wednesday, June 26	Bldg 34 W305	Teams Link	Introduction to NumPy
			Introduction to Data Visualization
			Matplotlib
Friday, June 28	Bldg 34 W305	Teams Link	Introduction to Pandas

For those who want to know more about the wealth of Python resources and discipline focused packages, explore the samples below.

General topics

• Programming Concepts
• Code Optimization
• Python Tools for Earth Science
• Data Visualization
• Web Scraping
• Data Science
• Machine Learning
  

Computing Tools

• SciPy - Provide utility functions for optimization, statistics, image processing, signal processing, etc.

Astronomy

• Astropy - Collection of software packages designed for use in astronomy.

Solar Physics

• Sunpy - Python library for Solar Physics data analysis and visualization.
• pysat - The Python Satellite Data Analysis Toolkit (pysat) simplifies the process of using new instruments, reduces data management overhead, and enables the creation of instrument independent analysis routines.
• HelioPy - Set of tools for heliospheric and planetary physics.
• PlasmaPy - Python package that provides common functionality required for plasma physics in a single, reliable codebase.
• SpacePy - Python package for data analysis, modeling and visualization in space sciences.

Sessions

Starting Point

All the classes will be provided using Jupyter notebooks. We will be explaining the features of Jupyter notebooks as we move along. In case you want to learn more, click on: .

Regardless of their level of proficiency in Python, all interns are expected to take the courses:

• Introduction Version Control with Git.
• Creating and Managing Git Repositories in GitHub.

Date	Teams Invitation Link	Lecture Topic	Interactive Link
Friday, June 23	LINK	Introduction to Version Control with Git
Monday, June 26	LINK	Creating and Managing Git Repositories in GitHub

Introduction to Python

Date	Teams Invitation Link	Lecture Topic	Interactive Link
Wednesday, June 28	LINK	Running Python
		Data Types
		Conditional Statements
		Loops
Friday, June 30	LINK	Advanced Data Types
		Functions
		Modules
Wednesday, July 5	LINK	datetime Module
		I/O on Text Files
Friday, July 7	LINK	Scripting and Packaging

To deepen your Python skills, you may consider doing the Beginner Python Exercises by Holy Python.

Data Science Tools

This section is more for interns who will have a Data Science related project.

Date	Teams Invitation Link	Lecture Topic	Interactive Link
Monday, July 10	LINK	Introduction to NumPy
		Introduction to Data Visualization
		Matplotlib
Wednesday, July 12	LINK	Introduction to Pandas

Sessions

Starting Point

All the classes will be provided using Jupyter notebooks. We will be explaining the features of Jupyter notebooks as we move along. In case you want to learn more, click on: .

Regardless of their level of proficiency in Python, all interns are expected to take the class on Introduction to Git.

Date	Teams Link	Lecture Topic	Interactive Link
Thursday, June 16	LINK	Introduction to Git

Introduction to Python

Date	Teams Link	Lecture Topic	Interactive Link
Wednesday, June 22	LINK	Running Python
		Data Types
		Conditional Statements
		Loops
Friday, June 24	LINK	Advanced Data Types
		Functions
		Modules
Monday, June 27	LINK	I/O on Text Files
		Scripting and Packaging

To deepen your Python skills, you may consider doing the Beginner Python Exercises by Holy Python.

Data Science Tools

This section is more for interns who will have a Data Science related project.

Date	Teams Link	Lecture Topic	Interactive Link
Wednesday, June 29	LINK	Introduction to NumPy
		Introduction to Data Visualization
		Matplotlib
Friday, July 1	LINK	Introduction to Pandas
Wednesday, July 6	LINK	Web Scraping

Topic	Interactive Link
Running Python
Data Types
Conditional Statements
Loops
Advanced Data Types
Functions
Modules
datetime Module
I/O on Text Files

To deepen your Python skills, you may consider doing the Beginner Python Exercises by Holy Python.

At the end of this course, we recommend that you take the Python assessment test. It takes less that 15 minutes without any help. We hope that you will score at least 80%.

Description

Git is a distributed version control system that is widely used for tracking changes in source code during software development. With a version control tool, you can make changes to your code with confidence (even reverting when needed), deploy different versions of your code, easily capture who made a change and when it happened, and communicate with your team on the modifications of the software. In this course, we will introduce fundamental version control concepts and demonstrate how Git, as a version control tool, can be employed to monitor alterations within your software application. You will gain proficiency in the essential procedures for creating and maintaining a Git repository.

• Prerequisites: Basic command-line skills.
• Date: Friday, September 8, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135237
• SATERN Registration Link: GSFC-600-IVCG

Description

Github is an online platform where Git users build software together. It also serves as a hosting provider and version control (with Git) platform that anyone can use to collaborate on open-source projects and share files. In this course, we will learn how to create a Git repository in Github and perform operations such as adding files, committing them, creating branches, forking, etc. We will also use a terminal to clone a repository on Github, do local changes and push them back to the remote repository on Github.

Each participant is expected to have a Github account. We recommend that each participant uses their own computer (with Git installed) and sets up their Github personal access tokens and SSH keys. We will provide access to a cloud-based platform as an alternative.

• Prerequisites: Familiarity with basic Git commands
• Date: Friday, September 15, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135358
• SATERN Registration Link: GSFC-CMGRG

Description

NumPy (Numerical Python) is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. It is meant to provide an array object that is at least an order of magnitude faster than traditional Python lists. Using NumPy, mathematical and logical operations on arrays can be efficiently performed. This course introduces the structure of Numpy arrays, show various ways to create them to facilitate numerical calculations. We will also present how to perform array slicing, in-place arithmetic, etc. and how to use built-in mathematical functions for faster computations.

• Prerequisites: Familiarity with Python
• Date: Tuesday, September 26, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135321
• SATERN Registration Link: GSFC-600-ITNUMPY

Description

Pandas is an open-source, Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is built on top of Numpy and is one of the most important Python tools for data analyses. In this course, we will introduce Pandas Series and DataFrames and show how to manipulate them. We will also show how to use Pandas to manipulate and visualize real datasets.

• Prerequisites: Familiarity with Python, Numpy
• Date: Tuesday, October 3, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135335
• SATERN Registration Link: GSFC-600-ITP

Description

Shapely is a Python package used for the manipulation and analysis of planar geometric objects (such as points, lines, polygons, etc.). It is rooted in the conventions of the geographic information system (GIS) world and is used in packages such as Cartopy, GeoPandas and MovingPandas. With Shapely, we can create different geometric objects (points, curves and surfaces), visualize them, and perform operations (length, distance, centroid, merging, etc.) with them. In this class, we will first define the concept of spatial data and the learn how to create various geometric objects, manipulate, and visualize them. We will also use the well-known text (WTK) and the GeoJSON formulations to represent the geometry objects.

• Prerequisites: Familiarity with Python, Matplotlib
• Date: Tuesday, October 17, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135328
• SATERN Registration Link: GSFC-CGOS

Description

Cartopy is a Python package to manipulate and visualize geospatial data. It relies on PROJ, NumPy and Shapely libraries, and includes an interface built on top of Matplotlib to create high quality maps. Cartopy supports several map projections, and can transform points, lines, vectors, polygons, and images between those projections. In this course, we will learn how to use different map projections and add features (rivers, lakes, boundaries, etc.) to plot geolocated data. We will also plot geometry objects and use shapefiles to create maps.

• Prerequisites: Familiarity with Numpy, Matplotlib and Shapely
• Date: Tuesday, October 24, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135354
• SATERN Registration Link: GSFC-PGDC

Description

GeoPandas is a Python open-source library that extends the features of Pandas with the ability to read, analyze, and visualize geospatial data. It combines the capabilities of Pandas and Shapely, providing geospatial operations in Pandas and a high-level interface to multiple geometries to Shapely. In this course, we will introduce the basic data structure of GeoPandas (GeoSeries and GeoDataFrame), show simple examples on how to create such a data structures. We will then learn how to read various files (containing geospatial information) and create different types of maps.

• Prerequisites: Familiarity with Numpy, Pandas and Shapely
• Date: Tuesday, October 31, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135322
• SATERN Registration Link: GSFC-600-GEOP

Description

MovingPandas is a Python library for movement data exploration and analysis using the spatial and temporal representations of the data. It handles the movement of data based on Pandas, GeoPandas and HoloViz GeoViews. It provides a trajectory data class and corresponding methods for tracking the movements of planar objects. With MovingPandas, we can produce static and interactive maps. In this class, we will first use an example that tracks the movement of a point object. The example will allow us to understand the concept of trajectory and its associated functions. We will then present additional examples on how we can manipulate the movement data (from the International Space Station, satellites, ships) and visualize it using static and interactive visualizations in Python.

• Prerequisites: Familiarity with Pandas, GeoPandas and Datetime module
• Date: Tuesday, November 7, 2023 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive and invitation prior to the class)
• SATERN Course ID: 135330
• SATERN Registration Link: GSFC-600-IMP

We provide a set of classes focusing on Data Science and Machine Learning (ML) with Python. The courses will start with the foundational tools (such as Numpy and Pandas) and then end with the basic machine learning methods and Python modules (Scikit_learn and TensorFlow) for Machine Learning.

Description

Web scraping is the process of retrieving data from the web. In this course, we use the Python packages Requests (to access a server and get the content of a web page) and Beautiful Soup (to parse the content of HTML documents from servers) to perform web scraping by accessing and manipulating text-based data from web pages. We will explain how to access a web page, read, and collect data, and work with the textual information available there. We will also introduce the JavaScript Object Notation (JSON) and learn how to get JSON objects from the web.

• Prerequisites: Python programming and basic HTML syntax
• Date: Tuesday, January 30, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136273
• SATERN Registration Link: GSFC-WS-P

Description

Python has been created an object-oriented language. Because of this, creating and using classes and objects are downright easy. This course presents the principles of object-oriented programming (OOP) using Python. We use simple examples to learn the benefits of OOP and see how the main OOP concepts (Encapsulation, Data Abstraction, Polymorphism, and Inheritance) can be used in Python applications.

• Prerequisites: Familiarity with Python programming
• Date: Tuesday, February 6, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136267
• SATERN Registration Link: GSFC-600-OOPP

Description

A decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it. Decorators allow you to define reusable building blocks that can change or extend the behavior of other functions. And they let you do that without permanently modifying the wrapped function itself. The function’s behavior changes only when it’s decorated. In this course, we will use a step-by-step approach to create decorators (functions and classes) and provide practical examples, explanations, and even some tips and tricks to help you become proficient in using decorators in your Python projects.

• Prerequisites: Familiarity with Python programming and OOP
• Date: Tuesday, February 13, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136276
• SATERN Registration Link: GSFC-600-PYDE

Description

This course is meant for Python programmers who want to learn techniques needed to speed up their Python applications. We will first list the Python features that make the language slow. We then introduce profiling tools that can be used to identify bottlenecks in Python applications. We present programming tips (that are partly within the realm of Python) to consider for accelerating loops, perform arithmetic operations, list creations, quickly manipulate NumPy arrays, reduce memory footprint, etc.

• Prerequisites: Familiarity with Python programming
• Date: Tuesday, February 27, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136275
• SATERN Registration Link: GSFC-OPA

Description

Numba is a just-in-time compiler for Python that works best on code that uses NumPy arrays and functions, and loops. The most common way to use Numba is through its collection of decorators that can be applied to users’ defined functions to instruct Numba to compile them. In this course, we present the main features of Numba and show of it can be used to accelerate Python applications that heavily rely on NumPy arrays. In the last part of the course, we will attempt to accelerate an image classification Machine Learning application using Numba.

• Prerequisites: Familiarity with Python programming, NumPy and decorators
• Date: Tuesday, March 5, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136277
• SATERN Registration Link: GSFC-600-APACN

Description

Dask is a flexible parallel computing library that provides multi-core and distributed parallel execution on larger-than-memory datasets. It distributes data across multiple cores and provides ways to scale Pandas, Scikit-Learn and NumPy workflows natively. We will introduce the basic concepts of Dask and learn how it can be used to accelerate calculations with NumPy and Pandas objects through Dask Arrays and DataFrames. We will also show how to use the dynamic task scheduling tool to distribute tasks across multiple cores.

• Prerequisites: Familiarity with Python programming, NumPy and Pandas
• Date: Tuesday, March 12, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136278
• SATERN Registration Link: GSFC-600-SPAMCPD

Description

Xarray is an open-source library providing high-level, easy-to-use data structures and analysis tools for working with multidimensional labeled datasets and arrays in Python. Xarray combines the convenience of labeled data structures inspired by Pandas with the multi-dimensional arrays of NumPy and parallel out-of-core computation from Dask to provide an intuitive, powerful and scalable platform for scientific analysis.
Xarray introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays. It is particularly tailored to working with netCDF files. In this course, we will cover the main concepts of Xarray and will learn how to read multidimensional data from scientific data formatted files, manipulate the data, and perform visualizations. We will also present how use Xarray to read large data files with Dask.

• Prerequisites: Familiarity with Python programming, NumPy and Pandas
• Date: Tuesday, March 19, 2024 (13:00 to 16:00 US EST)
• Where: Virtual through Teams (you will receive an invitation prior to the class)
• SATERN Course ID: 136268
• SATERN Registration Link: GSFC-600-MGX

SPRING 2023

Manipulating Satellite Data

In this course series, we use Python tools to read NASA and NOAA related satellite data files, process the data and perform basic visualizations. We limit the scope of our analysis to datasets with netCDF, HDF4 and HDF5 file formats.

In the satellite data files we will be working with, the latitude/longitude information of the covered areas, might not be readily available. We need to understand the metadata of each file and write code segments to extract appropriate information and data from the file. The goal of this course series is to provide the necessary Python skills to confidently access satellite datasets, and to present sample workflows on how to read and manipulate data from the files. We will be able to read observations from files (using pyhdf, h5py, Xarray), extract latitude/longitude information, and use them to create Xarray DataSets. The Xarray objects will then be utilized to facilitate the manilipulations of observations. In particular, we will visualize the data on maps to create static and interactive plots.

Useful Pointers

• Examples of accessing NASA HDF/HDF-EOS files using Python
• Satellite Imagery Analysis in Python Part I: GOES-16 Data, netCDF Files, and The Basemap Toolkit
• STAR Atmospheric Composition Product Training
• Visualizing Global Land Temperatures in Python with scrapy, xarray, and cartopy
• How to work with satellite data in Python

Background

We provide a set of classes focusing on data visualization with Python. Data visualization is the graphical representation (line plot, pie chart, histogram, contour plot, map, etc.) of information and data. Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. The tools offer us the ability to see at a glance what matters. They are essential to analyze massive amounts of information and make data-driven decisions. In this series of presentations, we will introduce a few Python packages to visualize geospatial data. We will learn how to do simple 2D static plots as well as interactive time series maps.

We are particularly interested in:

• Matplotlib: One of the most popular Python packages for data visualization. It is mainly used for 2D static plots.
• GeoPandas: Enables the use and manipulation of geospatial data. It provides support for geographic data to Pandas objects.
• Cartopy/Xarray: Cartopy is designed for geospatial data processing in order to produce maps and other geospatial data analyses. Xarray handles labeled multi-dimensional arrays and performs advanced analytics and visualization. The two packages can be combined to manipulate and visualize geospatial data.
• HoloViews: Conveys the message that data is trying to tell rather than focusing on how to do visualizations. Produces interactive plots.
• GeoViews: Facilitates the exploration and visualization of any data that includes geographic locations.

We will also provide two tutorials on ASTG’s eViz/iViz packages built from the above tools to streamline visualization of Earth System model outputs such as GEOS, modelE, LIS and WRF.

Requirements

To take any of the courses, you are expected to be familiar with Python and have knowledge of NumPy and Pandas. You can refer to the Jupyter notebooks, Introduction to NumPy and Introduction to Pandas, to learn about those topics.

You are also expected to have a Google Mail (gmail) account that will be needed to access either Google Colab or the NASA Center for Climate Simulation (NCCS) Science Managed Cloud Environment (SMCE). In case the SMCE is used, we will ask you to provide (once) your gmail userid.

List of Courses

List of Tutorials

Accessing the SMCE Platform

To access the SMCE, go to the user portal and log in. It is assumed that you have already provided your gmail userid. The system will ask you to provide your gmail password or give you automatic access if you are already logged in into gmail (through your browser). We recommend that 2-4 days before the beginning of each class, you provide your gmail userid by filling out this online form.

Installing the Anaconda Python Distribution

You are not required to have Python installed on your computer. If you want to have a Python distribution on your local platform, we recommend using he Anaconda Python distribution. To install it, follow the instructions in the Anconda installation Guide. If you have a Mac, it is preferable to use the command line installation option.

Note that you are not required to install anything on your local platform to take a course here. The SMCE platform is self contained and has all we need. However, we believe that it is important to have a local Python distribution that you can use for code development.

Accessing the Materials

The materials are in a Git repository that can be cloned using:

     git clone https://git.smce.nasa.gov/astg/training/py_materials.git

When you issue the above command, you will get a folder named py_materials.

Teaching Team

• Vanessa Valenti (vanessa.l.valenti@nasa.gov)
• Bruce Van Aartsen (bruce.vanaartsen@nasa.gov)
• Carlos Cruz (carlos.a.cruz@nasa.gov)
• Megan Damon (megan.r.damon@nasa.gov)
• Aaron Skolnik (aaron.skolnik@nasa.gov)
• Jules Kouatchou (jules.kouatchou@nasa.gov)

Answer: The main entry point for the Bootcamp (as far as the Python classes are concerned) is the webpage:

https://astg.pages.smce.nasa.gov/website/python/#nasa%20intern%20bootcamps

The webpage will constantly be updated to inform you on everything related to the classes:

• Topics to be covered.
• Schedule
• Teams invitation links
• Access to the materials.
• Link to the Python self-assessment test.
• Link to the cloud teaching platform.
• Link to the evaluation form.
• Etc.

Question: What do I expect to learn from this Bootcamp?

Answer: The primary goal of the Bootcamp is to provide the foundations of the Python programming language. We hope that after attending all the sessions and participating in all the class activities, you will be able to understand all Python data types, how to write conditional statements and loops, how to write a function and create a module, and how to write a simple Python application. On top of that, you will also learn how to use version control to write software applications, you will be exposed to cloud computing, and you will learn basic tools to edit a file and how to use Jupyter notebook.

Question: Do I have to know any programming language before attending the Bootcamp?

Answer: No! However, if you know any (other than Python), you will be able to quickly absorb Python concepts.

Question: Is there any requirement to take this Bootcamp?

Answer: We basically expect each participant to have a Gmail account (only needed to have access to the cloud platform) and a github.com account (needed to apply version control concepts for maintaining a software application).

Question: Am I required to take the Python self-assessment test?

Answer: The test is only meant to evaluate your Python knowledge and to determine which session you need to take. If you have never been exposed to the Python programming language, you do not need to take the test before the Bootcamp starts (however, it will be a good idea to take it at the end). If you have programmed with Python before, the test will determine if you have the basic foundations. If it is the case, you may just want to consider the Data Science track of the Bootcamp.

When you complete the test, you should receive an e-mail notification with your score and how you answer each question.

Question: What to do if during the test I encounter a cell with DO NOT EDIT this field or your time will not be recorded.?

Answer: We are sorry for the issue. You can fill out the box with anything you want. Whatever you write will not affect your score.

Question: Which days of the week the classes will take place?

Answer: The classes will typically occur on Mondays, Wednesdays, and Fridays. Note that you have at least a day between two consecutive classes to allow you to go over the materials of the previous class and get ready for the next. You can only master the concepts of any programming language if you continually practice and apply the concepts you have learned.

Question: How long is each class?

Answer: Each class lasts three hours, starts at 12:00 pm and ends at 3:00 pm. We will have plenty of breaks to allow to you to rest and digest the materials.

Question: Am I required to attend each class?

Answer: We assume that you were selected for the internship to learn new concepts and make your own contribution to the project you are involved in. Attending this Bootcamp is part of the learning process. We then expect you to attend all the classes you signed up for. We also understand that some of the classes may interfere with other internship activities. Please communicate with your mentor who will provide appropriate guidance.

Question: Are you going to collect attendance in each class?

Answer: Yes, we will do that and provide the information to the Program Coordinator.

Question: Will there be any face-to-face class?

Answer: All the classes will be hybrid. All the classes will be provided from a conference room at NASA Goddard and will broadcast through Teams. We expect that all the interns who have offices at NASA Goddard will physically attend the classes (see the Bootcamp webpage for the locations). Remote interns will attend virtually.

Question: How can I have access to the materials?

Answer: The Bootcamp webpage has (or will have) instructions on how to obtain the class materials.

Question: Am I allow to use the materials after the Bootcamp?

Answer: Absolutely, you can use them (for your internship project and beyond) as you wish. However, they are not meant for commercial use.

Question: Can I share the materials with somebody else?

Answer: Yes, you can do it.

Question: Are the classes going to be recorded?

Answer: Yes, each class will be recorded. Each recording will be available in the Teams chat room of the corresponding class. In addition, the Program Coordinator will make all the recordings available at a specific website (check with the Program Coordinator).

Question: Are we going to be evaluated at the of the Bootcamp?

Answer: No, there is not evaluation test. We believe that if you participate in class activities, you will have the ability to understand the structure of a Python code and write your own Python application.

Question: Is there any way to provide feedback on the classes were provided?

Answer: Yes, we will appreciate if all the participants fill out the evaluation survey which link is available at the Bootcamp webpage. Whatever constructive feedback you provide will help us improve the Bootcamp and make us better instructors.

Question: I am involved in an internship that relies on Python and I need additional help. Can I contact the instructors for guidance?

Answer: Absolutely! The instructors are available to assist you. However, note that if you need a lot of help or your project is beyond their expertise, they will refer you to other resources.