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Dealing with Missing Data in Python

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$12

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Are you fed up with dealing with messy data? Did you know that data scientists spend the majority of their time organizing, cleaning and finding data? You can clean up your data intelligently, it turns out! You can do just that with this course, "Dealing With Missing Data in Python". Learn how to correct missing values in both numerical and categorical data as well as time-series data. You'll learn how to spot patterns in missing data. While working with data related to diabetes and air quality, you will learn how to analyse and impute data and then evaluate its results.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Supervised Learning with scikit-learn

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Introduction to Data Visualization with Matplotlib

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Data Manipulation with pandas

What You Will Learn

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Learn how to identify, analyze, remove and impute missing data in Python

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You'll learn to address missing values for numerical, and categorical data as well as time-series data

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You'll learn to see the patterns the missing data exhibits! While working with air quality and diabetes data, you'll also learn to analyze, impute and evaluate the effects of imputing the data

Course Instructors

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Suraj Donthi

Deep Learning & Computer Vision Consultant

Suraj is a Deep Learning practitioner with experience in applying deep learning and machine algorithms to solve complex problems in the domains of automotive, retail, surveillance, biomedical image p...

Course Reviews

Average Rating Based on 3 reviews

5.0

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Course Cover

$12

$6

49% OFF

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