DATA SCIENCE
Data science is a multidisciplinary field that involves using various
techniques, algorithms, processes, and systems to extract valuable insights and
knowledge from data. It combines elements of statistics, computer science,
mathematics, and domain expertise to analyze and ieterpret complex datasets
and make data-driven decisions.
Here are some key components and aspects of data science :
1.DATA COLLECTION :
Data scientists collect data from various sources.
Which can include structured data and unstructured data. Data can be
gathered from sensors, Web scraping, surveys, and more.
2.DATA CLEANING AND PREPROCESSING :
Raw data often contains error, missing values, and
inconsistencies. Data scientists clean and preprocess the data to ensure it is
accurate and ready for analysis. This may involve data imputation,
normalization, and transformation
3.EXPLORATORY DATA ANALYSIS (EDA) :
EDA involves visualizing and summarizing data to
gain a better understanding of its characteristics. Data scientists use various
tools and techniques to identify patterns, trends, outliers and potential
relationships within the data.
Data science is a multidisciplinary field that involves using various
techniques, algorithms, processes, and systems to extract valuable insights and
knowledge from data. It combines elements of statistics, computer science,
mathematics, and domain expertise to analyze and ieterpret complex datasets
and make data-driven decisions.
Here are some key components and aspects of data science :
1.DATA COLLECTION :
Data scientists collect data from various sources.
Which can include structured data and unstructured data. Data can be
gathered from sensors, Web scraping, surveys, and more.
2.DATA CLEANING AND PREPROCESSING :
Raw data often contains error, missing values, and
inconsistencies. Data scientists clean and preprocess the data to ensure it is
accurate and ready for analysis. This may involve data imputation,
normalization, and transformation
3.EXPLORATORY DATA ANALYSIS (EDA) :
EDA involves visualizing and summarizing data to
gain a better understanding of its characteristics. Data scientists use various
tools and techniques to identify patterns, trends, outliers and potential
relationships within the data.
