Data Cleaning And Visualization In Python

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Data Cleaning And Visualization In Python
Published 2/2025
Created by Sairam Adithya
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 10 Lectures ( 2h 37m ) | Size: 2.22 GB​

Imputation techniques | Outlier analysis | Data transformation | Data visualization
What you'll learn
Understand the various issues that can be present in real time data
Understand imputation techniques and outlier analysis
Understand skewness and data transformation techniques to rectify them
Understand univariate, bivariate and multivariate feature visualization techniques
Implement the above mentioned concepts on real time dataset using python
Requirements
Minimal expertise of python is needed. Even not, no worries, the codes are very simple!!
Description
This course provides a comprehensive understanding of Exploratory Data Analysis (EDA), a crucial step in the machine learning lifecycle. EDA helps in diagnosing issues within datasets and applying appropriate techniques to improve data quality.The first phase of the course focuses on data cleaning, covering essential techniques such as handling missing values (imputation), data transformation, and outlier detection. Understanding these processes ensures the dataset is refined and structured for better model performance. Various imputation methods, including statistical, neighbor-based, and predictive filling, are discussed along with transformations like log, square root, and Box-Cox. Outlier detection techniques such as Z-score, IQR, and Mahalanobis distance are also explored.The second phase delves into data visualization, covering univariate, bivariate, and multivariate analysis. It provides an extensive discussion on various plots, including histograms, box plots, scatter plots, heatmaps, and more, ensuring clarity in data interpretation.The course concludes with real-world case studies, demonstrating how EDA helps derive meaningful insights. All implementations are carried out in Python, leveraging libraries such as pandas, numpy, seaborn, and matplotlib. By the end of this course, participants will have hands-on expertise in performing EDA effectively for any dataset and leverage these techniques to improvise the data for better results in machine learning analysis.
Who this course is for
Data science and AI aspirants who want to leverage their knowledge on machine learning through Exploratory Data Analysis
Engineering students of various backgrounds who want to apply artificial intelligence and machine learning in their domains
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