Data science applications in business verticals include BFSI, IT, retail, hospitality, entertainment, healthcare, logistics, and security. Typical examples are:
Prediction. Predicting unseen values, such as missing data.
Forecast. Forecasting possible future values and trends, such as revenue, demands, sales, or customer retention.
Recognition. Recognizing from unstructured data such as images, text, audio, or videos.
Classification. Detecting patterns or behaviors to match known groups, such as detecting spam emails, or labeling important emails.
Recommendations. Recommendations based on consumption and learned preferences, such as clothes, books, movies, or restaurants.
Pattern detection. Identifying possible patterns and behaviors without known classification groups, such as weather patterns or financial market patterns.
Anomaly detection. identifying values that do not fit known patterns and behaviors, such as fraud or disease detection.
Process automation and decision-making. Examples include rule or score-based processes such as background checks, credit rating, loan approval.
Why is data science important?
Data-driven organizations seeking strategic business value from big data are now looking towards data science to provide answers. Making sense of the constant data deluge is beyond BI tools and machine learning algorithms need domain-specific models to generate contextual insights.
Data science can leverage domain knowledge and statistical methods to build predictive models for machine learning algorithms, delivering refined and accurate actionable insights.
What is data science?
Data Science is a modern interdisciplinary field employing statistical methods, mathematical models, data analysis processes, and machine learning algorithms to extract knowledge and insights from structured as well as unstructured data. Business users and analysts generate business value from the resulting insights.
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