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But heres the question I keep asking myself: do we really need this immense power for most of our analytics? Think about it: LLMs like GPT-3 are incredibly complex deeplearning models trained on massive datasets. In analytics, LLMs can create natural language query interfaces, allowing us to ask questions in plain English.
To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI).
Artificial Intelligence Analytics. AI can be applies to all 3 major types of analytics: DescriptiveAnalytics: The entire journey of the descriptive and diagnostic analytics process includes data extraction, data aggregation and data mining; 3 applications where AI is widely used to reduce costs, and eliminate complex actions.
An interview with Pranshuk Kathed, machine and deeplearning enthusiast. Once we have right data, we do some descriptiveanalytics which tells us column’s mean, median, mode, standard deviation, variance, bias, some skewness – how the data is spread. I thought I'd bust some of the hype too, but - the hype is true.
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