Data Munging | Data Science | SNS Institutions

Описание: Data munging, also known as data wrangling, is the process of transforming raw, unstructured, or messy data into a clean, structured, and usable format for analysis. In real-world applications, data collected from various sources such as databases, sensors, logs, APIs, or spreadsheets is often incomplete, inconsistent, and filled with errors. Data munging plays a crucial role in preparing this data so that meaningful insights can be extracted effectively.

In this video, we explore the complete concept of data munging step by step. You will learn how to handle missing values, remove duplicate records, correct data inconsistencies, standardize formats, and filter irrelevant information. The video also covers essential techniques such as data parsing, normalization, transformation, feature creation, and data integration from multiple sources. These steps ensure that the dataset is accurate, reliable, and ready for further analysis or machine learning tasks.

We also discuss popular tools and technologies used for data munging, including Python (Pandas, NumPy), R, SQL, and spreadsheet-based tools. Practical examples are included to help you understand how data munging is applied in real-world scenarios like business analytics, IoT data processing, big data analytics, and artificial intelligence applications. Special attention is given to industry use cases where data quality directly impacts decision-making.#snsinstitutions
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