Hands On Training

Dealing with Missing Data in Python

Name: Dealing with Missing Data in Python
Brand: Careervira
Price: 71 USD
Availability: InStock
Rating: 5.0 (3 reviews)

DataCamp

(3)

Course Report

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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.

Highlights

Pedagogy

Top 30 Percentile

Rating & Reviews

Top 30 Percentile

Parameters

Pedagogy

Acquire all major Python Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Python Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

Rating & Reviews

This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Supervised Learning with scikit-learn

Introduction to Data Visualization with Matplotlib

Data Manipulation with pandas

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...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%