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How Predictive Analytics In Data Science Differs From Statistics
It’s a well evident fact that the present age of analytics is being dominated by technologies like Data Science, Artificial Intelligence & Machine Learning, Both Data Science & Machine Learning are two distinct technologies which are often used interchangeably. There’s no denying that a slight overlap exists between these two technologies, but on a broader scale, both these technologies differ in terms of roles & responsibilities, process implementations, or results.
Statistics & Predictive Analytics are among the crucial processes in analytics in Data Science. If you are a Data Science career enthusiast, then having intense knowledge of statistics & predictive analytics is crucial to interpret with Big Data accurately & to achieve the desired goals & objectives.
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Now, let’s understand how Predictive Analytics in Data Science differs from Statistics
Predictive Analytics is all about making accurate predictions by analyzing the present & historical data. By subjecting the data to Predictive Analytics, we can By subjecting the data to Predictive Analytics, analysts can accurately predict the occurrence of events, results, identify the behavioral changes, and trends.
It obtains accurate results about the target variable based on the provided input variables.
Statistics in Data Science helps the Data Scientists or to obtain clear insights about the data based on numerical facts. The use of statistics can also be seen in different Machine Leaning processes involving Data Science. There are two different types of Statistical Models in data Science namely Descriptive Statistics which uses mean or standard deviation to summarize the data & Inferential Statistics whose outputs are based on observation errors & sample variation
Statistics will be playing a crucial role in the data modeling process in Predictive Analytics.
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