DATA SCIENCE Data science is a multidisciplinary field that combines statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves various processes, including data collection, cleaning, analysis, and visualization. Key components of data science include: Data Collection: Gathering data from various sources, such as databases, APIs, or web scraping. Data Cleaning: Preparing the data for analysis by removing errors, handling missing values, and ensuring consistency. Exploratory Data Analysis (EDA): Analyzing the data to discover patterns, trends, and relationships through visualization and summary statistics. Modeling: Applying statistical and machine learning techniques to make predictions or classify data. Evaluation: Assessing the performance of models using metrics like accuracy, precision, and recall. https://www.sevenmentor.com/data-science-course-in-pune.php

DATA SCIENCE Data science is a multidisciplinary field that combines statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves various processes, including data collection, cleaning, analysis, and visualization. Key components of data science include: Data Collection: Gathering data from various sources, such as databases, APIs, or web scraping. Data Cleaning: Preparing the data for analysis by removing errors, handling missing values, and ensuring consistency. Exploratory Data Analysis (EDA): Analyzing the data to discover patterns, trends, and relationships through visualization and summary statistics. Modeling: Applying statistical and machine learning techniques to make predictions or classify data. https://www.sevenmentor.com/data-science-course-in-pune.php

DATA SCIENCE Data science is a multidisciplinary field that combines statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves various processes, including data collection, cleaning, analysis, and visualization. Key components of data science include: Data Collection: Gathering data from various sources, such as databases, APIs, or web scraping. Data Cleaning: Preparing the data for analysis by removing errors, handling missing values, and ensuring consistency. Exploratory Data Analysis (EDA): Analyzing the data to discover patterns, trends, and relationships through visualization and summary statistics. Modeling: Applying statistical and machine learning techniques to make predictions or classify data. Evaluation: Assessing the performance of models using metrics like accuracy, precision, and recall. Deployment: Implementing models in real-world applications, often through APIs or integrated software solutions. https://www.sevenmentor.com/data-science-course-in-pune.php