A Data Analytics project follows a structured lifecycle to ensure meaningful insights and data-driven decision-making. Here are the key steps:
Before starting, it is crucial to define the business problem clearly. Understanding the objectives, stakeholders’ expectations, and success criteria helps in shaping the entire project.
Data is gathered from various sources such as databases, APIs, web scraping, and third-party providers. Ensuring that the data sources are reliable and relevant to the problem is essential.
Raw data often contains inconsistencies, missing values, and outliers. This step involves cleaning, handling missing values, normalizing, and transforming data to improve quality and usability.
EDA involves visualizing and summarizing the data to uncover patterns, trends, and anomalies. Statistical analysis and visualization tools like Python, R, Tableau, and Power BI are commonly used.
Based on the problem type (descriptive, predictive, or prescriptive analysis), appropriate statistical models, machine learning algorithms, or deep learning techniques are chosen.
The selected model is trained on historical data and evaluated using metrics such as accuracy, precision, recall, RMSE, and confusion matrix. Fine-tuning is done to improve performance.
Insights are presented using dashboards, reports, or presentations. Effective visualization tools like Power BI, Tableau, and Python libraries (Matplotlib, Seaborn) help in making data-driven decisions.
Once the model is validated, it is deployed in a production environment for real-time use. Deployment methods vary from APIs, cloud services, or embedded applications.
Continuous monitoring ensures model accuracy and efficiency over time. Retraining and fine-tuning are necessary as new data is introduced.
The final step involves measuring the impact of the insights or model on business objectives and gathering feedback for continuous improvement.
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For more details, visit SLA Consultants India or visit their Delhi center for course inquiries.
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Module 1 – Basic and Advanced Excel With Dashboard and Excel Analytics
Module 2 – VBA / Macros – Automation Reporting, User Form and Dashboard
Module 4 – MS Power BI | Tableau Both BI & Data Visualization
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