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Overview
Context
Tired of working with messy data? Did you know that most of a data scientist's time is spent in finding, cleaning and reorganizing data? Well turns out you can clean your data in a smart way!
In this Dealing with Missing Data in Python course offered by Data Camp, you'll do just that! 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 evaluate the effects of imputing the data.
Programme Structure
Chapters include:
- The Problem With Missing Data
- Imputation Techniques
- Does Missingness Have A Pattern?
- Advanced Imputation Techniques
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Data Science & Big Data View 469 other Short Courses in Data Science & Big Data in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites
- Supervised Learning with scikit-learn
- Introduction to Data Visualization with Matplotlib
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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