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what are the top data science techniques one should know

Data science is analyzing the raw data collected from different sources and making that data into useful information needed for the organization. It is vital to analyze a large amount of raw data into something meaningful and extract the valuable information necessary for the different purposes of the organization, such as marketing, sales, and decision-making. Join the Data Science Course in Chennai to learn about data science.


Python 07-12-22, 4:27 p.m. divyaram
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Top Data Science ways:
Retrogression. Assume you are a deals director trying to read the coming month's deals.
Bracket.
Linear retrogression.
Jackknife retrogression.
Anomaly discovery.
Personalization.
Lift analysis.
Decision tree. Visit: Data Science Course in Pune
02-03-23, 1:30 p.m. saniya838

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Here are some of the top data science techniques that one should know:
  1. Regression Analysis: This technique is used to establish the relationship between a dependent variable and one or more independent variables. It is commonly used for predictive modelling and forecasting.
  2. Classification:This technique is used to categorize data into different classes or groups. It is often used in image and speech recognition and in spam detection.
  3. Clustering: This technique is used to group data into clusters based on similarities between data points. It is often used in customer segmentation and anomaly detection.
  4. Association Rules:This technique is used to discover relationships between variables in a large dataset. It is often used in market basket analysis, to identify products that are frequently purchased together.
  5. Time Series Analysis:This technique is used to analyze and predict trends in time-dependent data. It is often used in finance, economics, and weather forecasting.
  6. Natural Language Processing:This technique is used to analyze and understand human language. It is often used in sentiment analysis, chatbots, and language translation.
  7. Deep Learning: This technique is used for complex pattern recognition and prediction, using artificial neural networks. It is often used in image and speech recognition, and in natural language processing.
  8. Dimensionality Reduction:This technique is used to reduce the number of variables in a dataset while retaining the most important information. It is often used in data visualization and feature selection.
02-05-23, 1:33 p.m. Muskan

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