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

Requirements
Basic Knowledge of Python

Description
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 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 CleaningImprove decision makingImprove the efficiencyIncrease productivityRemove the errors and inconsistencies from the datasetIdentifying missing valuesRemove duplicationWhy should you take this courseData 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 bning. 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, meg 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!

Overview
Section 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

Section 2: Playing with the Data

Lecture 9 Reading the data

Lecture 10 Structure of the data

Lecture 11 Meg/Splitting

Lecture 12 Integrity check

Lecture 13 Knowing the domain

Lecture 14 Range of variables

Lecture 15 Inquiring dependencies

Section 3: Variables and Correlations

Lecture 16 Type of variables

Lecture 17 More variable types

Lecture 18 Single variable plots

Lecture 19 Plotting interrelations

Lecture 20 Measuring correlations

Lecture 21 Need for transformation

Lecture 22 Discretizing features

Section 4: Missing Values and Outliers

Lecture 23 Absurd or Missing values

Lecture 24 Finding their distribution in the dataset

Lecture 25 Deciding what to do with them

Lecture 26 Looking for outliers

Section 5: Exercises

Lecture 27 Exercise-1

Lecture 28 Exercise-2

Lecture 29 Exercise-3

Lecture 30 Exercise-4

Students who want to learn the basics of Data Cleaning

HomePage:

https://www.udemy.com/course/data-science-and-ml-data-cleaning-techniques/

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