The Data Analytics Lifecycle. A Guide pdf

The Data Analytics Lifecycle is a structured approach to solving data-driven problems and generating insights. It consists of six key phases: Discovery, where business objectives are identified; Data Preparation, involving data collection and cleaning; Model Planning, to choose analytical techniques; Model Building, where models are developed and tested; Communicate Results, focusing on presenting findings; and Operationalize, where solutions are deployed for real-world use. This lifecycle ensures a systematic workflow that aligns data efforts with business goals, leading to informed decision-making.

1. THE DATA ANALYTICS
LIFECYCLE

2. Overview of the
Lifecycle
Discovery
Data Preparation
Model Planning
Model Building
Communicate Results
Operationalize

3. Understand business objectives
Identify problems/opportunities
Assess resources, timeline, stakeholders
Define success metrics
Phase 1 – Discovery

4. Phase 2 – Data Preparation
Collect relevant data from multiple sources
Clean, format, and organize the data
Handle missing values, outliers

5. Phase 3 – Model Planning
Choose appropriate techniques (e.g., regression,
clustering)
Define data transformations
Visualize data to understand patterns

6. Split data into training and testing sets
Apply algorithms to train the model
Tune hyperparameters for performance
Phase 4 – Model Building

7. Visualize findings with dashboards/charts
Explain model results to stakeholders
Provide actionable insights
Phase 5 – Communicate
Results

8. PHASE 6 – OPERATIONALIZE
Deploy the model in a real environment
Monitor performance over time
Maintain and update models as needed

9. Data quality issues
Choosing the right model
Communicating to non-technical stakeholders
Best Practice: Always keep business goals in mind
Challenges & Best
Practices

10. Thank you very much!
VISIT HERE: HTTPS://UNCODEMY.COM/COURSE/DATA-ANALYTICS-TRAINING-COURSE-IN-DELHI

11. STRATEGIES
BORCELLE
Planning
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Competitive
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Analysis
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Execution
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Growth
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