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Additionally, both AI and ML require large amounts of data to train and refine their models, and they often use similar tools and techniques, such as neural networks and deeplearning. Inspired by the human brain, neural networks are crucial for deeplearning, a subset of ML that deals with large, complex datasets.
Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. The process or lifecycle of machine learning and deeplearning tends to follow a similar pattern in most companies. Moreover, tools like PowerBI and Tableau can produce remarkable results.
Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. Big Data Technologies: Hadoop, Spark, etc.
I conducted thorough data validation, collaborated with stakeholders to identify the root cause, and implemented corrective measures to ensure data integrity. I would perform exploratorydataanalysis to understand the distribution of customer transactions and identify potential segments.
Azure Synapse Analytics Previously known as Azure SQL Data Warehouse , Azure Synapse Analytics offers a limitless analytics service that combines big data and data warehousing. This service enables Data Scientists to query data on their terms using serverless or provisioned resources at scale.
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