What is Data Science?

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Key Components of Data Science
Data Collection:

Gathering data from various sources such as databases, APIs, sensors, web scraping, and more. This includes both structured data (like databases) and unstructured data (like text, images, and videos).

Data Cleaning and Preparation:

Processing and transforming raw data into a suitable format for analysis. This involves handling missing values, removing duplicates, and correcting errors.
Data Exploration and Visualization:

Analyzing data to discover patterns, correlations, and trends. Visualization tools such as charts, graphs, and dashboards are used to present data insights effectively.

Statistical Analysis:

Applying statistical techniques to understand data distributions, relationships, and significance. This helps in making inferences and testing hypotheses.

Machine Learning:

Building predictive models using machine learning algorithms. This involves training models on historical data to make predictions or classifications on new data.

Data Engineering:

Developing and maintaining infrastructure for data storage, processing, and retrieval. This includes working with big data technologies like Hadoop and Spark.

Domain Expertise:

Applying domain-specific knowledge to interpret data in a meaningful context. This ensures that insights are relevant and actionable.

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