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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

   • Drawing conclusions from the analysis and presenting findings in a clear and actionable manner.

Tools and Technologies

• Data Visualization: Tableau, Power BI, D3.js, Matplotlib.
• Statistical Analysis: R, SAS, SPSS, Stata.
• Big Data Processing: Apache Hadoop, Apache Spark, Hive.
• Database Management: SQL, NoSQL databases (e.g., MongoDB, Cassandra).
• Machine Learning: Python, Scikit-learn, TensorFlow, PyTorch.
• Data Integration: Apache Nifi, Talend, Informatica.

Data Analytics Classes in Pune

Data Analytics Course in Pune