Putting the Pieces Together

Astro497, Week 15, Monday

Logistics

• Presentations

• SRTEs

• My course-specific survey (in Google)

• Undergrad Town Hall: Wednesday, November 30th at 5 PM in Davey 541

Questions fom Last week

Looking back

Course Goals

• Understand how astronomers detect and characterize extrasolar planetary systems,

• Learn about the current state and future of exoplanet science,

• Increase their data acumen[data_acumen], and

• Appreciate how building data science skills can benefit astronomy & astrophysics research.

Course Objectives

• Ingest and manipulate data from astronomical surveys.

• Quantitatively describe the effects of exoplanets on astronomical observations.

• Build, apply, assess and update astrophysically motivated models for astronomical observations.

• Create visualizations for exploratory and explanatory data analyses of observations from exoplanet surveys.

• Synthesize the above into a dashboard to support the efficient analysis of exoplanet observations while following principles of reproducible research.

What Data Science skills have we developed?

Data Science:

• Data Acumen

• Databases, queries & storage

• Ingesting data & Data wrangling

• Exploratory data analysis

• Model building & assessment

• Explanatory data analysis

• Data visualization

• Reproducible research

• Scientific workflows

• Technical collaboration (if teamed up for project)

• Scientific communications

What Data Science skills have we skipped
(or only skimmed the surface of)?

• Probability & Statistics

• Machine Learning/Artificial Intelligence (AI)

  • Non-parametric regression

  • Classification

  • Clustering

  • Density estimation

  • Anomaly detection

  • Image analysis

• Computing

  • Data structures

  • Algorithms

  • Databases

  • Parallel computing

• Applications

  • Hardware

  • Big Data frameworks

  • ML/AI tools

  • Software engineering

  • Deployment & operations

Looking forward

Foundational Classes to learn more about Data Sciences

Mathematics

• Probability

  • Elementary Probability (STAT 318)

  • Probability Theory (STAT/MATH 414)

  • Introduction to Probability and Stochastic Processes for Engineering (STAT/MATH 418)

  • Astrostatistics (ASTRO 415)

• Linear Algebra (MATH 220)

Programming

• Intro to Programming (e.g., CMPSC 121, 122)

• Data management/databases (DS 220, but one DS or CMPSC preqreq beyond CMPSC 122)

• Programming Models for Big Data (DS/CMPSC 410, but several CMPSC prereqs)

• Information Retrieval and Organization (e.g., IST 441, but several IST prereqs)

Machine Learning/AI

• Machine Learning (DS 310; prereqs: (CMPSC 121 or CMPSC 131) and (STAT/MATH 318 or STAT/MATH 414 or STAT/MATH 418))

• AI (e.g., DS/CMPSC 442, but several CMPSC prereqs)

Applied classes that connect to Data Sciences

• Astrostatistics (ASTRO 415, Spring 2023)

• Computational Astrophysics (ASTRO/PHYS 410, Spring 2023)

• Astronomical Techniques? (ASTRO 451, Fall 2022)

• Data Science Through Statistical Reasoning and Computation (STAT 380; but prereq STAT 184)

• Visual Analytics for Data Sciences (DS 330; but prereq DS 220)

• Research projects (e.g., ASTRO 496, summer project or thesis)

Project-based learning

Pros:

• Help to motivate why need to learn things

• Emphasize practical problems

Cons:

• Forces you to work through implementation details

• Risk learning specific tools, rather than underlying mathematics/algorithms

• Specific tools used are very likely certainly become obsolete soon

Looking back at Exoplanets

• Detection methods:

• Characterization of individual planets/systems

• Characterization of populations

• Future prospects

Classes to learn more about exoplanets

• Planets and Planetary System Formation (ASTRO 420W)

• Stellar Structure and Evolution (ASTRO 414)

• Research projects (e.g., ASTRO 496, summer project or thesis)

Plan for rest of week

Setup/Helper Code