DANL 210 - Data Preparation and Management

2020-2021 Catalog Year

Credit(s): 3
Lecture: 3
Non-Lecture: 0
This course aims to provide overview of how one can manipulate, process, clean, and crunch datasets with hands-on and practical case studies that show you how to solve a broad set of data analysis problems effectively. We will use a computing development environment, Jupyter Notebook, which is a shell and notebook for exploratory computing. This course will cover topics such as (1) loading, cleaning, transforming, merging, and reshaping data, (2) creating informative visualizations with matplotlib, (3) dataset slicing, dicing, and summarizing, and (4) analyzing and manipulating regular and irregular time series data. We will cover these topics to solve real-world data analysis problems with thorough, detailed examples. Computing is done in Python (the de facto programming language in data analytics) using Pandas (the practical, modern data science tools in Python) in addition to Numpy and Matplotlib.

Prerequisite(s): (ECON 205 or GEOG 278 or MATH 242 or MATH 262 or PLSC 251 or PSYC 250 or SOCL 211) and DANL 100
Offered: Every Spring Semester