Grasp the Artwork of Information Exploration and Visualization with Python Libraries
What you’ll be taught
Information Cleansing and Preprocessing – Dealing with lacking values, outliers, and information inconsistencies utilizing Pandas and NumPy.
Information Visualization – Creating insightful visualizations utilizing Matplotlib, Seaborn, and Plotly to know information distributions and relationships.
Characteristic Engineering – Extracting significant options and remodeling uncooked information for higher evaluation and mannequin efficiency.
Statistical Evaluation – Understanding descriptive statistics, correlation, and speculation testing to attract significant insights.
Palms-on EDA with Actual-World Datasets – Making use of EDA methods to real-world datasets from domains like finance, healthcare, and setting
English
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