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Exploratory Data Analysis in Data Science
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 13m | Size: 994.97 MB
Learn EDA from basic to advanced data insights
What you'll learn
Learn techniques of data analysis like univariate, bivariate, multivariate analysis with data spread, data distortion, correlation using python
Learn to plot and analyze data distribution measurable patterns like Normal Distribution, Standard Normal, Poisson, Binomial distributions
Learn to reflect data analysis results as visualizations chart like Scatter chart, HeatMap, Histogram, Column Chart, BoxPlot etc
Case study based examples to learn data ingestion, cleanup, statistical measures, data dependency patterns, and predictive outcome of analysis for ML modeling
Enables a person to confidently apply learning concepts in business use-cases.
Requirements
Basic knowledge of python programming
Development environment like Google Colab, Jupyter notebook, Excel etc
Description
The idea of this course is that before you start building ML models, first understand what the dataset actually contains.
Exploratory Data Analysis is meant to uncover structure, patterns, relationships, anomalies, and assumptions in the data.
EDA steps are
-Know your data => Clean it => Explore each variable => Find relationships => Detect anomalies/ Outliers => Extract meaningful insights => Useful in feature engineering and machine learning
-Learn EDA with Python libraries - Pandas, NumPy, SciPy, Matplotlib and Seaborn
Learn about statistical measures like Measures of central tendency, Measures of spread and Measures of distortion.
Learn approach to plot data distributions for continuous and discrete form of data it includes Normal Distribution, Std Normal Distribution, Uniform Distribution, Poisson and Binomial distribution.
Learn to handle categorical form of data, to identify outliers or extremes in a given dataset, identify the strength of relationships between variable and decide the important variables like dependent and independent variables.
Learn how to use Hypothesis testing in data analysis.
Learn to interpret data analysis insights using data visualizations like Histogram, Bell Curve, Column chart, Box Plot, Heatmap, Line Chart and more.
Learn to do data analysis using sample data and excel spreadsheets.
Use sample data, code scripts, pdf summary of course to practice EDA.
Who this course is for
Python developers who are curious to learn Data Exploration Techniques of Data Science
Data professionals who are keen to bridge knowledge gap between data engineering and machine learning modeling
Professionals who are keen to learn data visualization and interpretation techniques
Homepage
Code:
https://www.udemy.com/course/exploratory-data-analysis-in-data-scienceDOWNLOAD LINKS
