Data science is an interdisciplinary area in which insights and information are extracted from organized and unstructured data using diverse approaches, tools, and methodologies. It integrates statistics, mathematics, computer science, domain expertise, and communication skills to analyze and understand data in order to solve complicated problems and make sound decisions.
Data science's major purpose is to employ data-driven methodologies to acquire a better knowledge of data processes, trends, and patterns. This knowledge can be applied in a variety of disciplines, including business, healthcare, finance, marketing, social sciences, and others.
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Key components of data science include:
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Data Collection: Gathering and acquiring data from various sources, which may include databases, APIs, web scraping, sensors, surveys, and more.
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Data Cleaning: Preprocessing and transforming raw data to remove errors, inconsistencies, and missing values to ensure the data is suitable for analysis.
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Data Exploration: Exploring and visualizing the data to identify patterns, trends, and relationships between variables.
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Data Analysis: Applying statistical and machine learning techniques to extract insights, build predictive models, and make data-driven decisions.
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Model Building: Creating algorithms and models that can learn from data and make predictions or classifications.
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Model Evaluation: Assessing the performance of models using various metrics and techniques to ensure their accuracy and generalization.
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Data Visualization: Presenting the results of data analysis in a visually understandable format to communicate findings effectively.
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Communication: Effectively communicating complex technical findings to non-technical stakeholders to aid in decision-making.
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Mentor - Vyankatesh Panchal
Intormation and research are sources from internet.
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