{"id":13283,"date":"2026-08-10T10:57:11","date_gmt":"2026-08-10T10:57:11","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-data-cleaning-and-visualization-in-python-2025-2\/"},"modified":"2026-08-10T10:57:11","modified_gmt":"2026-08-10T10:57:11","slug":"udemy-data-cleaning-and-visualization-in-python-2025-2","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-data-cleaning-and-visualization-in-python-2025-2\/","title":{"rendered":"Udemy \u2013 Data Cleaning and Visualization in Python 2025-2"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Data Cleaning and Visualization in Python. This course provides a comprehensive understanding of exploratory data analysis, or EDA, which is a critical step in the machine learning cycle. Its main goal is to identify problems in a dataset and apply appropriate techniques to improve data quality. The first part of the course focuses on data cleaning and covers essential techniques such as missing value management, data transformation, and outlier detection. This part teaches various methods for filling in missing values, including statistical, neighbor-based, and predictive methods, along with transformations such as logarithmic, square root, and Box-Cox. It also covers outlier detection techniques such as Z-score, interquartile range, and Mahalanobis distance. The second part is dedicated to data visualization and covers univariate, bivariate, and multivariate analyses. This section discusses the types of graphs including histograms, box plots, scatter plots, and heat maps to make data interpretation clear. The course concludes with real-world case studies that demonstrate how exploratory data analysis helps extract meaningful insights. All implementations are done in Python using libraries such as Pandas, NumPy, Seaborn, and Matplotlib. By the end of this course, participants will have the practical skills to effectively perform exploratory data analysis on any dataset and use these techniques to improve data and achieve better results in machine learning analyses.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n