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Data Science


What is Data Science?

Data Science

Data Science is an interdisciplinary field that extracts insights and knowledge from data using statistical techniques, machine learning, data mining, and data visualization. It encompasses the entire data lifecycle, from data collection to analysis and interpretation.

Key Applications of Data Science at ZU

  • Predictive Analytics: Using AI/ML models to identify at-risk students and predict graduation rates.

  • Sentiment Analysis: Using tools like Blue ML to analyze unstructured communication data, such as student comments, and extract actionable insights.

  • Trend Prediction: Predicting future job market trends by analyzing job vacancies and in-demand skills to align educational offerings.

  • Ranking Prediction: Leveraging predictive analysis of key ranking-related metrics to predict ZU's placement in future rankings.

  • Resource Optimization: Optimizing resources by analyzing patterns and trends.

Data Science Use Cases at ZU

  • Predicting 'At-Risk' Students: AI/ML models assign a 'risk' score to students, helping identify those who may require additional support.

    • Uses data from 2016 onwards, structured by Term and overall students.

    • Employs the Azure Machine Learning Workspace.

    • Achieves greater than 88% accuracy in predicting at risk students.

  • Prediction of Years of Graduation: AI/ML models predict the number of years a student will take to graduate, aiding in forecasting graduation rates.

    • Uses data from 2016 onwards, structured at PIDM level.

    • Employs the Azure Machine Learning Workspace.

    • Achieves greater than 90% accuracy in predicting years of graduation.

Data Science Life Cycle

The data science life cycle includes the following stages:

  • Business Understanding

  • Data Mining

  • Data Cleaning

  • Data Exploration

  • Feature Engineering

  • Data Visualization

  • Predictive Modeling

Benefits of Data Science

  • Informed Decision-Making: Provides insights derived from data analysis, leading to more accurate and strategic decisions.

  • Competitive Advantage: Identifies market trends, consumer preferences, and operational inefficiencies.

  • Risk Management: Helps mitigate potential risks through data analysis.