Pandas for Data Science

Duke University via Coursera

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Overview

How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas a common library in Python used for data science can ease their workflow. We recommend you should take this course after...

Syllabus

Intro to Pandas For Data Science + Strings and I/O

This module, you will learn how to read data from files into your python program, and write that corresponding data to a file. Well be working primarily with string-type data in this unit and will give special attention to the way that python handles strings. Additionally well go over some basic debugging in python using exception traces, and youll leverage these to create your own python program that is capable of reading and writing to a file.

Module 2: Tabular Data with Pandas

This module, youll learn how to begin to utilize Pandas, one of the most commonly used libraries in Data Science with python. Pandas is predominantly used for working with tabular data. By the end of this module youll be able to identify the hallmarks and quirks of working with tabular data, describe the benefits and limitations of using Pandas, and be able to perform some basic data manipulation techniques in Pandas.

Module 3: Loading and Cleaning Data

This Module, you will learn how to perform basic file operations in Pandas, as well as how to clean up large datasets. Youll learn to read and write from common tabular file formats, and Pandas-specific intricacies for working with that data. Additionally, youll learn best practices for cleaning your data.

Module 4: Data Manipulation

This module you will learn how to combine datasets from different sources. Pandas has different methods of combining data depending on your preferred outcome, and youll be able to differentiate between when to use each kind. Additionally, well go over computationally efficient ways of querying your data, which, while similar to selecting data via subsetting in its outcomes, has a distinct set of advantages.