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Data science is a category that covers a wide range of topics related to the analysis and interpretation of data. This category may include keywords such as data mining, machine learning, artificial intelligence, statistical analysis, data visualization, big data, predictive modeling, and data engineering.
In this category, you can find articles, resources, and insights related to data science, including best practices, tools, and techniques used by data scientists. Whether you are a beginner or an experienced practitioner, this category can help you stay up-to-date with the latest trends and techniques in data science. You can also find tutorials, case studies, and examples of real-world applications of data science in various fields, such as healthcare, finance, marketing, and more.
In the world of artificial intelligence (AI), Generative AI is a fascinating field that has been gaining momentum in recent years. Generative AI is the branch of machine learning that deals with creating something new, such as images, music, or text, based on patterns it has learned from existing data. In this blog post, we … Read more
Data cleaning and preparation are essential steps in data analysis, and identifying and handling outliers is an important part of it. Outliers are extreme values that lie outside the normal range of data, which can significantly affect the results of data analysis and modeling. In this blog, we will discuss some Python Snippets for Outliers … Read more
In this interesting post, we will explore two terms we often hear from data scientists i.e. Extrapolation & Interpolation. When we get access to data points, we try to present all the information that can be useful. Let’s start exploring this bit from the AI universe. Interpolation vs Extrapolation The prefix inter- means between, and … Read more
Python is one of the most popular programming languages in the world of data science. It provides a variety of tools and packages for data cleaning and preparation. In this post, you’ll find crucial and commonly used Python Snippets for Data Cleaning and Preparation in the fields of AI, ML, and data science. Python Snippets … Read more
Missing data is a common issue in datasets, and can affect the accuracy of any analysis. It is important to handle missing data appropriately in order to avoid biased results. One way to handle missing data is through imputation, which involves filling in missing values with estimated values based on the available data. In this … Read more