Project

Astrophysics Meets Data Analysis

Challenging

45 completions

~ 24 hours

3.8

Sooner or later, all great data analysts may face a challenge in a field they don't know anything about. The goal is to delve into the sphere to resolve the tasks. Step into the shoes of a data analyst and explore astrophysics! Discover the properties of compact groups of galaxies, manage data tables using pandas, and test various statistical hypotheses with scipy. You will also work with a coordinate system using astropy, a Python package for astronomers.

Provided by

JetBrains Academy

About

Most galaxies in the Universe are found as highly-dense groups or clusters. The densest and the most populous are galaxy clusters — they may contain 50+ relatively bright galaxies in the area of a few megaparsecs. Smaller aggregations are called groups and compact groups (CGs). Compact groups are of particular interest for studying galaxy merging and the properties of interacting galaxies. This project presents an opportunity to be an astrophysicist. Discover interesting properties of compact groups with the statistical methods of Python. Don't worry if you're completely new to the field of astrophysics — we'll explain all the important concepts and provide you with the tips to help you navigate. But be ready to learn a lot of new information!

Graduate project

This project covers the core topics of the Data Analyst course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Read the dataset with CGs properties, display a box-plot, and find the mean redshift for different groups of CGs.

Examine the influence of low surface brightness features on the mean surface brightness of the intra-group light.

Load datasets of isolated galaxies and galaxies in compact groups. Perform the Kolmogorov-Smirnov homogeneity test to discover the differences between them.

Calculate the mean Sérsic index and the mean numerical galaxy type for each group. To find a relation between the intra-group light and the galaxy morphology, calculate the Pearson correlation coefficients.

Find the projected median separation between galaxies in groups using the equatorial coordinates and redshifts. Calculate the Pearson correlation coefficients between the separation and the mean surface brightness of the intra-group light. Find a relation between the intra-group light and the density of galaxies.

Reviews

Joydeep Chatterjee

8 months ago

2.0

Required meticulousness in defining the proper columns and parameters of pandas DataFrames.

Darya Kuzmenko

11 months ago

4.3

I have learned how to process the data I have very little understanding of. I have practiced pandas, stats and itertools. It was fun to test hypotheses too.

Aneurin Sutton

1 year ago

4.3

I learned the power of several of the objects and methods in the itertools, stats, and astropy packages to analyze and look for relations in cosmological data.

3.8

Learners who completed this project within the Data Analyst course rated it as follows:

Usefulness

4.1

Fun

3.9

Clarity

3.5