Review of the first 6 Sessions in LSST DSFP
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Review of the first 6 Sessions in LSST DSFP by Dongming Jin


A few links




See my article and the convertedtalk about adopting this workflow.

Appendix: Linked Curriculum

Machine Learning

S1D4: intro + hand-on

S2D1: supervised learning

S2D1: unsupervised learning

S2D1: clustering

S2D2: ML overview

S2D2: model interpret

S2D4: neural networks

S2D4: hand-on: apply + develop
S2D4: deep learning

S2D5: ML workflow + hand-on


S1D3: intro + hand-on

  • luminance: high spatial frequency data
  • saturation: low spatial frequency data
  • color-blind-proof: viridis

S2D1: human bias

S1D5: communication

S2D5: astronomical images

S5D2: HST images

S6D2: ParaView

S6D3: Bokeh

S6D4: Glue

S6D5: science communication

S6D6: data ethics

Image Processing

S1D2: intro + hand-on1 + hand-on2

S5D1: photons to science

S5D1: overview of imaging

S5D1: detector noise
S5D1: PSF problem

S5D1: image reduction + hand-on

S5D2: photometry-star + hand-on

S5D2: photometry-galaxy + hand-on

S5D3: astrometry + hand-on

S5D4: stacking & differencing

S5D4: image registration
S5D4: image re-projection
S5D4: image staking
S5D4: image differencing

S5D4: spectroscopy + hand-on

S5D5: multi-wavelength + hand-on


S1D4: star-galaxy

S3D4: streaming data pipeline

S3D5: DataLab

S5D4: image visualization

S6D4: interactive visualization

Software Engineering

S1D1: git

S3D1: code repositories + hand-on

S3D1: unit test & continuous integration

S3D2: object oriented programming + hand-on

S3D2: documentation + hand-on

S3D4: profiling + hand-on

S3D4: parallel programming + hand-on

S3D4: code styling

Data Management

S1D3: intro + SQL hand-on

S3D1: sqlite in python

S3D2: databases + hand-on

S3D3: parallel database + hand-on

S3D4: streaming data

S3D5: scaling

S3D5: computing at scale

Time-series Analysis

S1D5: intro + hand-on

S6D1: Lomb Scargle periodogram

S6D1: GPs + MCMC

S6D1: transient fitter-MOSFiT

S6D2: state space model + hand-on

S6D2: feature engineering


S1D2: intro + hand-on

S1D2: advanced inference

S4D1: intro to Bayesian

S4D1: Nuisance
S4D1: Credibility
S4D1: Likelihood and MLE

S4D2: Gaussian process regression + hand-on

S4D3: advanced GP

S4D3: GP implementation
S4D3: GP in action

S4D2: MCMC guide

S4D2: PyMC
S4D2: Sampling

S4D2: generative models

S4D4: hierarchical Bayesian models

S4D4: practical HBM with PyJAGS
S4D4: break-point HBM

S4D4: selection effects

S4D5: model selection

S4D5: Bayes factor

S5D2: intro to R


S1D1: intro

S1D1: goal