Introduction :
Exploratory Data Analysis or EDA is a crucial step in data preprocessing, ensuring that your data is accurate, complete, and ready for analysis. In this comprehensive video, we dive deep into the essentials of data cleaning, breaking down common techniques, best practices, and challenges you may face during the preprocessing stage. Whether you’re a beginner or looking to refine your skills, this tutorial will guide you through each step, from identifying dirty data to cleaning it for better accuracy and analysis. By mastering these data cleaning techniques, you’ll streamline your data science workflow, avoid common pitfalls, and make your datasets more reliable for future analysis. Don’t miss out on this critical aspect of data preprocessing that can make or break your data-driven projects!
Important points :
We discussed :
What is Data Science ?
What are the prerequisites of Data Science ?
Data Science Life Cycle
Why we do Exploratory Data Analysis (EDA) ?
Important syntaxes used in EDA Process
Practice of EDA using python
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#datapreprocessing
#datascience
#dataanalytics
#machinelearningcourse
#datapreparation
#cleandata
#dataworkflow
#Datacleaning
#datamanagement
#datacleaning
#datacleaning
#datapreprocessing
#OutlierDetection
#FeatureSelection
#machinelearning
#numpy
#pandas
#matplotlib
#seaborn
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