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Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, MachineLearning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.
Both data science and machinelearning are used by data engineers and in almost every industry. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages. Python is the most common programming language used in machinelearning.
Understanding the differences between SQL and NoSQL databases is crucial for students. Students should learn how to leverage MachineLearning algorithms to extract insights from large datasets. Students should learn how to train and evaluate models using large datasets.
Decision Trees: A supervisedlearning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of MachineLearning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.
Explore MachineLearning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervisedlearning such as linear regression , logistic regression, decision trees, and supportvectormachines.
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