Data Cleaning Techniques in Data Science & Machine Learning

Data Cleaning Techniques in Data Science & Machine Learning, available at $29.99, has an average rating of 3.67, with 30 lectures, based on 9 reviews, and has 170 subscribers.

You will learn about Professional ways for handling the data Learn Standard visualization techniques like Histograms, Scatterplots etc How to locate discrepancies, and deal with issues This course is ideal for individuals who are Students who want to learn the basics of Data Cleaning It is particularly useful for Students who want to learn the basics of Data Cleaning.

Enroll now: Data Cleaning Techniques in Data Science & Machine Learning

Summary

Title: Data Cleaning Techniques in Data Science & Machine Learning

Price: $29.99

Average Rating: 3.67

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Professional ways for handling the data
Learn Standard visualization techniques like Histograms, Scatterplots etc
How to locate discrepancies, and deal with issues

Who Should Attend

Students who want to learn the basics of Data Cleaning

Target Audiences

Students who want to learn the basics of Data Cleaning

One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.

A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.

Why should you learn Data Cleaning?

Improve decision making
Improve the efficiency
Increase productivity
Remove the errors and inconsistencies from the dataset
Identifying missing values
Remove duplication

Why should you take this course?

Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning.

This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions where you’ll be introduced to the dataset for ensuring complete learning of Data Cleaning.

Enroll in this course now to learn about data cleaning concepts and techniques in detail!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Identifying the task

Lecture 2: Model building

Lecture 3: Some common solutions

Lecture 4: Training and test data

Lecture 5: Cross validation

Lecture 6: Feature selection

Lecture 7: Accuracy measures

Lecture 8: Overfitting

Chapter 2: Playing with the Data

Lecture 1: Reading the data

Lecture 2: Structure of the data

Lecture 3: Merging/Splitting

Lecture 4: Integrity check

Lecture 5: Knowing the domain

Lecture 6: Range of variables

Lecture 7: Inquiring dependencies

Chapter 3: Variables and Correlations

Lecture 1: Type of variables

Lecture 2: More variable types

Lecture 3: Single variable plots

Lecture 4: Plotting interrelations

Lecture 5: Measuring correlations

Lecture 6: Need for transformation

Lecture 7: Discretizing features

Chapter 4: Missing Values and Outliers

Lecture 1: Absurd or Missing values

Lecture 2: Finding their distribution in the dataset

Lecture 3: Deciding what to do with them

Lecture 4: Looking for outliers

Chapter 5: Exercises

Lecture 1: Exercise-1

Lecture 2: Exercise-2

Lecture 3: Exercise-3

Lecture 4: Exercise-4

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 2 votes
4 stars: 3 votes
5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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