Why Learning SQL and Data Sampling is Important for Data Scientists🤔 | Intellipaat #Shorts
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In this #shorts video, 'Senior Consultant Data Science' at Eli Lily and Company: Mr. Sahil Mattoo explains the challenge that every data scientist faces while working on large datasets. And how fundamental concepts like SQL and Sampling come to the rescue in these cases.
✅Why Learning SQL is Important for Data Scientists?
Learning SQL (Structured Query Language) is fundamental for data scientists because it is the standard language for querying and managing data in relational databases. Data scientists frequently need to retrieve and manipulate large datasets stored in databases, and SQL provides powerful, efficient ways to access this data.
✅Why Sampling is an Important Concept for Data Science Professionals?
Data sampling is critical for data scientists because it allows for the efficient handling and analysis of large datasets by selecting a representative subset of the data. Sampling makes data analysis more manageable, cost-effective, and timely, especially when working with massive datasets. By using proper sampling techniques, data scientists can ensure that the sample accurately reflects the population, leading to reliable and generalizable insights. Sampling is also essential for various machine learning applications, such as training and validating models, where it helps in reducing computational load and preventing overfitting.