Data Filtering

Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description
Once data is gathered for data science it is often in an unstructured or raw format. Data must be filtered for content and validity. In this course, you'll explore examples of practical tools and techniques for data filtering.

Target Audience
Individuals with some programming and math experience working toward implementing data science in their everyday work

Prerequisites
None

Expected Duration (hours)
1.0

Lesson Objectives

Data Filtering

start the course

identify common filtering techniques and tools

extract date elements from common date formats

parse content types in HTTP headers

use csvcut to filter CSV data

use sed to replace values in a text data stream

drop duplicate records from data

extract headers from a jpeg image

use pdfgrep to extract data from searchable pdf files

detect invalid or impossible data combinations

parse robots.txt from a web site to decide what should and shouldn't be crawled nor indexed

drop records from a CSV file based on date range

Course Number:
df_dses_a03_it_enus

Expertise Level
Beginner