Mastering Exploratory Data Analysis (EDA) with Python

Name: Mastering Exploratory Data Analysis (EDA) with Python
Rating: 4.8 (8 reviews)

Unlocking Insights, Unleashing Potential: Dive Deep into Data Exploration with Python

Created byEduconnhub, Cu Dinh Tien, Quan Pham, Dien Pham

Last updated 8/2024

English

English [Auto],

What you'll learn

Comprehensive Overview of Exploratory Data Analysis
Data Validation Techniques
Data Summarization Techniques
Handling Missing Data
Categorical Data Analysis
Numeric Data Analysis
Temporal Data Analysis
Understanding Correlation
Exploring Factor Relationships and Distributions
Hypothesis Generation

Course content

3 sections • 14 lectures • 1h 59m total length

Introduction10:49
Master exploratory data analysis (EDA) by validating data quality, handling missing values, exploring correlations, and visualizing relationships to prepare data for machine learning.
Python Code & DataSets0:02

Mastering Anaconda_ A Step-by-Step Guide to Installation and Configuration3:48
Install and configure Anaconda by downloading from the Anaconda site and using the installer wizard with Python 3.8, then install Python 3.8 from python.org and verify with the python command.
Exploring the Power of Python_ A Comprehensive Overview and Practical Guide9:19
Learn Python's simplicity, readability, and versatility for web development and scientific computing, with practical guidance on immediate mode, script mode, and IDE workflows.

Comprehensive Overview of Exploratory Data Analysis10:49
Master exploratory data analysis (EDA) techniques from data validation, missing values, and data types to correlations and visualizations, formulate hypotheses, identify patterns and anomalies, and prepare data for machine learning.
Mastering Data Validation Techniques with Python11:02
Master data validation techniques with python to inspect data types using info and types, adjust with astype, validate categorical data with isin, and analyze numerical ranges for exploratory data analysis.
Mastering Data Summarization Techniques with Python12:32
Learn data summarization with Python, using groupby, aggregations, and visualizations to compare genres by mean publishing year and bar plots with confidence intervals.
Strategies for Handling Missing Data11:35
Learn how missing data distorts distributions and salaries in data analysis, and apply practical Python strategies such as removing incomplete observations and imputing with median or subgroup estimates.
Exploring Categorical Data Analysis in Python8:56
Explore categorical data analysis in Python using pandas to filter non-numeric columns, count job title frequencies, and identify trends with str.contains and numpy select, then visualize results with seaborn.
Exploring Numeric Data in Python7:51
Preprocess salary data in the data professionals dataset, converting rupees to usd, then analyze by experience and company size using groupby to compute mean, median, and standard deviation.
Navigating Outliers: Strategies for Effective Data Analysis8:29
Identify outliers using mean, median, standard deviation and IQR, then visualize with boxplots and set thresholds to decide whether to drop or retain them for analysis.
Exploring Patterns Over Time: Analyzing Divorce Filings Data7:26
Analyze patterns over time in divorce filings data from Mexico using pandas DateTime, parse_dates, and line plots to reveal trends by month and year.
Exploring Relationships: Understanding Correlation in Data Analysis5:50
Explore correlation in data analysis with Python, using pandas to compute correlation coefficients, heat maps, scatter plots, and pair plots for rainfall and plant growth.
Exploring Factor Relationships and Distributions11:06
Explore factor relationships and distributions across categorical variables, including education levels, by analyzing marriage duration and age using histograms, kernel density estimates, cumulative plots, and scatter visualizations.

Requirements

Basic Python Programming Skills
Familiarity with Data Analysis Concepts
Basic Statistics Knowledge
Access to Python Environment

Description

*This course contains the use of artificial intelligence.*

Embark on a transformative journey into the heart of data exploration with our comprehensive course, "Mastering Exploratory Data Analysis with Python." This course is meticulously designed to equip you with the essential skills and techniques needed to unlock insights and unleash the full potential of your data. With Python as our primary tool, you'll delve into the intricate world of exploratory data analysis (EDA), where every dataset tells a unique story waiting to be uncovered.

Through 12 in-depth modules, you'll gain a profound understanding of EDA from every angle. From mastering data validation and summarization techniques to navigating outliers and handling missing data with confidence, you'll build a solid foundation for effective data analysis. You'll explore the nuances of categorical and numeric data analysis, learning how to uncover patterns, relationships, and distributions that lie beneath the surface.

But we don't stop there. You'll also dive into the fascinating realm of time series analysis, where you'll dissect divorce filings data to uncover temporal patterns and trends. Armed with this knowledge, you'll harness the power of correlation analysis and delve into the intricacies of factor relationships, paving the way for deeper insights into your data.

Whether you're a seasoned data professional seeking to sharpen your skills or an aspiring data scientist eager to embark on your journey, this course is for you. Join us, and let's embark on a transformative exploration of data together

Who this course is for:

Data Analysts
Data Scientists
Business Analysts
Researchers
Students and Aspiring Data Professionals

Mastering Exploratory Data Analysis (EDA) with Python

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 11min

Environment Installations2 lectures • 13min

Mastering Exploratory Data Analysis with Python Programing10 lectures • 1hr 36min

Requirements

Description

Who this course is for: