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At the heart of this discipline lie four key building blocks that form the foundation for effective data science: statistics, Python programming, models, and domain knowledge. Some of the most popular Python libraries for data science include: NumPy is a library for numerical computation. Matplotlib is a library for plotting data.
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Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Examples include linear regression, logistic regression, and supportvectormachines.
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Another example can be the algorithm of a supportvectormachine. Hence, we have various classification algorithms in machine learning like logistic regression, supportvectormachine, decision trees, Naive Bayes classifier, etc. What are SupportVectors in SVM (SupportVectorMachine)?
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