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Data Science is an interdisciplinary field that utilizes scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. Python has become a leading language for data science due to its simplicity, versatility, and extensive libraries.
Python has emerged as a dominant language in the field of data science due to its powerful libraries, ease of use, and strong community support. Its versatility allows data scientists to efficiently analyze data, build models, and derive meaningful insights, making it an invaluable tool in today’s data-driven world.
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Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, programming, and domain expertise to analyze data and inform decision-making.
Some of the main libraries used in Data Science with Python include:
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series (1D) and DataFrame (2D) that make it easy to handle and analyze large datasets, perform data cleaning, and conduct exploratory data analysis.
The train_test_split() function is used to split a dataset into two parts: a training set and a testing set. This is important for evaluating the performance of machine learning models, allowing you to train the model on one subset of data and test it on another to assess its generalization ability.
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers instead of the underlying pattern. This results in poor performance on unseen data. Techniques like crossvalidation, pruning, and regularization can help mitigate overfitting.
Common methods for handling missing data include:
Feature scaling refers to the process of normalizing or standardizing the range of independent variables (features) in a dataset. It is important because many machine learning algorithms (like gradient descent) perform better when features are on a similar scale, improving convergence speed and model performance.
The groupby() function in Pandas is used to split the data into groups based on certain criteria. It allows for aggregate operations to be performed on each group, enabling analysis of summary statistics (like mean, sum, count) for different subsets of the data.
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