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Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.
Mastery of statistical concepts equips professionals to make informed decisions and draw accurate conclusions from empirical observations. Proficiency in programming languages Fluency in programming languages such as Python, R, and SQL is indispensable for Data Scientists.
Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decisiontrees, and support vector machines. You should be skilled in using a variety of tools including SQL and Python libraries like Pandas. It includes regression, classification, clustering, decisiontrees, and more.
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Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques.
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