Why do we choose Data Analytics?

Types of Data Analytics

1. 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.
2. 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.
3. 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.
4. 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 Analytics Process

1. Data Collection
   • Gathering data from various sources such as databases, APIs, logs, and sensors.
2. Data Cleaning
   • Removing or correcting inaccuracies and inconsistencies in the data.
3. Data Transformation
   • Converting data into a suitable format or structure for analysis.
4. Data Analysis
   • Applying statistical and computational techniques to extract insights.
5. Data Visualization
   • Representing data and analysis results through charts, graphs, and dashboards.
6. Interpretation and Reporting
   • Concluding the analysis and presenting findings clearly and effectively.