Data Science and Analytics for AI
Description
Comprehensive data science skills including data collection, cleaning, analysis, visualization, and statistical modeling essential for AI applications.
Learning Objectives
Learners will master data collection and preprocessing techniques for AI applications, perform exploratory data analysis and statistical modeling, create effective data visualizations and dashboards, implement big data processing for large-scale AI systems, conduct A/B testing and experimental design for AI products, and apply business intelligence techniques to derive actionable insights from AI-generated data.
Topics (8)
Big data technologies including Apache Spark, Hadoop ecosystem, distributed computing concepts, and cloud-based big data services for processing large-scale datasets required for AI training and inference.
Data collection methods including web scraping, API integration, database querying, and sensor data collection, along with preprocessing techniques for handling missing data, outliers, and data quality issues.
EDA techniques including descriptive statistics, correlation analysis, hypothesis testing, regression analysis, and statistical modeling for understanding data distributions and relationships in AI datasets.
Data visualization principles and tools including matplotlib, seaborn, plotly, Tableau, Power BI, and D3.js for creating static and interactive visualizations and dashboards for AI applications.
Time series analysis including trend analysis, seasonal decomposition, ARIMA models, exponential smoothing, and deep learning approaches (LSTM, Prophet) for forecasting and temporal pattern recognition.
Experimental design principles including A/B testing, multivariate testing, statistical power analysis, and causal inference techniques for evaluating AI system performance and business impact.
Business intelligence concepts including KPI development, performance metrics, analytics frameworks, and business analytics tools for translating AI insights into business value and strategic decisions.
Advanced analytics including predictive modeling, prescriptive analytics, optimization techniques, simulation methods, and decision support systems for AI-powered business intelligence and strategic planning.