Data Wrangling & EDA in Data Science | AIML End-to-End Session 37

Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?

Welcome to Session 37 of our End-to-End AIML series! In this session, we cover the powerful combination of Data Wrangling and Exploratory Data Analysis (EDA) in data science. These processes are essential for preparing, cleaning, and exploring your data to uncover insights before building AI/ML models.

What You'll Learn:

Data Wrangling Recap: Review key data wrangling techniques such as handling missing data, cleaning inconsistencies, and data transformation using Pandas and NumPy.
Introduction to EDA: Understand the role of Exploratory Data Analysis in identifying patterns, relationships, and anomalies in your data.
Visualizing Data: Learn how to create meaningful visualizations using Matplotlib and Seaborn to explore your dataset and uncover insights.
Statistical Summaries: Use descriptive statistics to summarize data and identify trends using techniques such as mean, median, standard deviation, and more.
Handling Outliers: Discover methods to detect and manage outliers during the EDA process to avoid skewed results.
Real-World Applications: Follow along with practical examples of data wrangling and EDA on real-world datasets, preparing them for machine learning.
This session is perfect for anyone looking to master the foundational steps of data science, including data cleaning and exploration, before moving into model building.

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