Python, R, and SAS are examples of technologies that are frequently used in data science for machine learning and statistical analysis. NumPy, pandas, and scikit-learn are Python libraries that facilitate data modeling and manipulation. For the presenting of insights, visualization tools like as Matplotlib and Seaborn are essential. Conversely, Big Data technologies comprise NoSQL databases such as MongoDB for handling massive amounts of unstructured data, Apache Spark for real-time data processing, and Apache Hadoop for distributed storage and processing. In order to prepare students to tackle a variety of data-centric challenges, a Data Science machine learning course requires an understanding of these techniques and technologies.
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