Why do we choose data analytics - Los Angeles

Descriptive Analytics

• Purpose: To understand what has happened in the past.
• Techniques: Data aggregation and data mining.
• Tools: Reporting tools, dashboards, and visualization tools (e.g., Tableau, Power BI).
• Example: Summarizing sales data to identify trends and patterns.

Diagnostic Analytics

• Purpose: To understand why something happened.
• Techniques: Drill-down, data discovery, and correlations.
• Tools: Statistical analysis software (e.g., SAS, SPSS).
• Example: Analyzing customer feedback to determine the cause of a drop in sales.

Predictive Analytics

• Purpose: To predict what is likely to happen in the future.
• Techniques: Machine learning, forecasting, and statistical modeling.
• Tools: Python, R, machine learning frameworks (e.g., Scikit-learn, TensorFlow).
• Example: Predicting customer churn based on historical data.

Prescriptive Analytics

• Purpose: To recommend actions to achieve desired outcomes.
• Techniques: Optimization, simulation, and decision analysis.
• Tools: Advanced analytics software (e.g., IBM Decision Optimization, Gurobi).
• Example: Recommending the best marketing strategy to increase customer engagement.

Data Collection

• Gathering data from various sources such as databases, APIs, logs, and sensors.

Data Cleaning