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They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesistesting and deep learning to the team. They build production-ready systems using best-practice containerisation technologies, ETL tools and APIs.
Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Statistics : Fundamental statistical concepts and methods, including hypothesistesting, probability, and descriptive statistics.
They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. These models may include regression, classification, clustering, and more. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc. ETL Tools: Apache NiFi, Talend, etc. Read more to know.
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. Understanding ETL (Extract, Transform, Load) processes is vital for students. Students should learn about data wrangling and the importance of data quality.
Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. Data Warehousing and ETL Processes What is a data warehouse, and why is it important? Explain the Extract, Transform, Load (ETL) process. What approach would you take?
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