What are the key steps in a Data Analytics project lifecycle?
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Description
Key Steps in a Data Analytics Project Lifecycle
A Data Analytics Project Lifecycle follows a structured approach to extracting insights from data and driving business decisions. The key steps are:
1. Understanding Business Requirements
Identify the business problem or goal.
Define key objectives and success criteria.
Engage with stakeholders to understand expectations.
2. Data Collection
Gather data from multiple sources such as databases, APIs, logs, or spreadsheets.
Ensure data availability and accessibility.
3. Data Cleaning and Preprocessing
Handle missing values, duplicates, and inconsistencies.
Transform raw data into a structured format.
Standardize and normalize data for analysis.
4. Exploratory Data Analysis (EDA)
Perform descriptive statistics to understand data distribution.
Identify trends, correlations, and patterns using visualization tools like Tableau, Power BI, Python (Matplotlib, Seaborn).
Detect anomalies and outliers.
5. Data Transformation and Feature Engineering
Create new variables/features to improve model performance.
Use SQL, Python (Pandas), or R for data manipulation.
Apply dimensionality reduction techniques if necessary.
6. Model Selection and Implementation
Choose the right statistical/machine learning models.
Train and validate models using Python (Scikit-learn), R, or Alteryx.
Optimize hyperparameters for better accuracy.
7. Data Visualization & Reporting
Build interactive dashboards using Power BI, Tableau, or Excel.
Present findings in a business-friendly format.
Use storytelling techniques to communicate insights effectively.
8. Deployment & Monitoring
Implement models into production environments.
Monitor model performance and accuracy over time.
Update models and dashboards as needed.
9. Decision Making & Business Impact
Provide data-driven recommendations.
Support strategic decision-making processes.
Measure ROI and effectiveness of analytics solutions.
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Data Analytics Training in Delhi NCR
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