What is Data Science? Data Science Process?
Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data using various scientific methods, algorithms, and tools. It combines elements of statistics, mathematics, computer science, and domain expertise to analyze complex datasets and extract meaningful patterns, trends, and insights.

The data science process typically involves several key steps:

Problem Definition: The first step in the data science process is to clearly define the problem or question that needs to be addressed. This involves understanding the business context, identifying objectives, and defining measurable goals.
Data Acquisition: Once the problem is defined, the next step is to gather relevant data from various sources. This may involve collecting data from databases, APIs, files, or external sources. Data acquisition also includes data cleaning and preprocessing to ensure that the data is accurate, complete, and formatted correctly for analysis.
Exploratory Data Analysis (EDA): In this step, data scientists explore and visualize the dataset to gain a better understanding of its structure, relationships, and patterns. EDA involves summarizing key statistics, visualizing distributions, and identifying potential correlations or anomalies in the data.
Feature Engineering: Feature engineering is the process of selecting, transforming, and creating new features from the raw data to improve the performance of machine learning models. This may involve scaling, encoding categorical variables, handling missing values, and creating new features based on domain knowledge.
Model Selection and Training: Once the data is prepared, data scientists select appropriate machine learning algorithms and models based on the nature of the problem and the characteristics of the data. Models are trained on a subset of the data using techniques such as regression, classification, clustering, or deep learning.
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