Data Analysis
Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves a variety of techniques and methods, including statistical analysis, machine learning, data mining, and visualization. Data analysis is used in various fields, such as business, healthcare, finance, and social sciences, to identify patterns, trends, and relationships in data, and to make predictions and recommendations based on the findings. The process of data analysis typically involves several stages, including data collection, data cleaning and preprocessing, exploratory data analysis, hypothesis testing, model building and evaluation, and communication of results.
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- Cluster: In data science, a cluster refers to a group of data points that share similar characteristics or features. Clustering is a technique used in unsupervised machine learning to group similar data points together based on their similarities an ... Read More >>
- ARIMA: ARIMA (AutoRegressive Integrated Moving Average) is a time series forecasting model that takes into account the autocorrelation and trend in the data. It is a combination of three components: autoregression (AR), differencing (I), and movin ... Read More >>
- Indices: In Data Science, indices refer to the values that are used to identify and locate specific data points within a dataset. These values can be numeric or categorical and are often used as a reference point for data analysis and manipulation. ... Read More >>
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