Dealing with Missing Data in Python

Name: Dealing with Missing Data in Python
Brand: Careervira
Price: 74 USD
Availability: InStock
Rating: 1 (1 reviews)

via

DataCamp

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Visit Course Report

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Data Manipulation with pandas

Introduction to Data Visualization with Matplotlib

Supervised Learning with scikit-learn

What You Will Learn

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

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

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 Content

Expand all sections

Module 1: The Problem With Missing Data

Module 2: Does Missingness Have A Pattern?

Module 3: Imputation Techniques

Module 4: Advanced Imputation Techniques

Course Instructors

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...