Interactive Course

Dealing with Missing Data in Python

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

  • 4 hours
  • 14 Videos
  • 46 Exercises
  • 1,281 Participants
  • 3,800 XP

Loved by learners at thousands of top companies:

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Course Description

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 course Dealing with Missing Data in Python, 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 analyze, impute and evaluate the effects of imputing the data.

  1. Does Missingness Have A Pattern?

    Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.

  2. Advanced Imputation Techniques

    Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!

  1. 1

    The Problem With Missing Data

    Free

    Get familiar with missing data and how it impacts your analysis! Learn about different null value operations in your dataset, how to find missing data and summarizing missingness in your data.

  2. Does Missingness Have A Pattern?

    Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.

  3. Imputation Techniques

    Embark on the world of data imputation! In this chapter, you will apply basic imputation techniques to fill in missing data and visualize your imputations to be able to evaluate your imputations' performance.

  4. Advanced Imputation Techniques

    Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!

What do other learners have to say?

Devon

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Devon Edwards Joseph

Lloyd's Banking Group

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Harvard Business School

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Ronald Bowers

Decision Science Analytics @ USAA

Suraj Donthi
Suraj Donthi

Deep Learning & Computer Vision Consultant

Suraj is a Deep Learning practitioner with experience in applying deep learning algorithms to solve real-world problems involving the Computer Vision tasks of Object Detection, Tracking, and Segmentation. His experience includes building solutions in various domains including Computer Vision for Self Driving Cars, People tracking & analytics for retail and public spaces, and Biomedical Image Analysis. His clients include companies from Canada, USA, UK, India and growing. He's also working to develop biomedical analysis solutions with the Optical Imaging Lab @ NIMHANS which is a top Research Institute for brain research.

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