Explore the essentials of Exploratory Data Analysis (EDA) and data cleaning in Python. This workshop covers techniques for understanding data structure, identifying patterns, and handling missing or inconsistent data. Learn to use libraries like Pandas and Matplotlib to transform raw data into meaningful insights, ensuring high-quality datasets for analysis.
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In this comprehensive workshop, we dive into the critical steps of Exploratory Data Analysis (EDA) and data cleaning using Python. You will learn how to examine datasets to uncover hidden patterns, trends, and relationships, which are crucial for guiding decision-making and building robust models. We’ll cover how to clean and prepare raw data, addressing common challenges like missing values, outliers, and inconsistent data formats.
Using powerful Python libraries such as Pandas, NumPy, and Matplotlib, participants will gain hands-on experience in reshaping data, performing statistical summaries, visualizing distributions, and more. Whether you're new to data analysis or looking to refine your skills, this workshop provides practical techniques for transforming messy, unstructured data into clean, insightful datasets that can drive actionable results. Join us to enhance your data wrangling skills and lay the foundation for deeper analytical tasks like machine learning and predictive modeling.
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