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And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.
They introduce two primary data structures, Series and Data Frames, which facilitate handling structured data seamlessly. With Pandas, you can easily clean, transform, and analyse data. Engaging in these events fosters community, providing support and motivation as you advance your Python journey for Data Science.
Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.
Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes.
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