Data Analytics

Turn raw data into powerful business insights

Our Process

Step-by-Step Workflow

Every project follows this proven 10-step process to ensure quality, accuracy and on-time delivery.

Step 1

Business Problem Understanding

Clarify the analytics objective — reporting, forecasting, trend analysis, or KPI tracking.

Step 2

Data Source Identification

Identify all data sources — databases, APIs, CSV files, web scraping.

Step 3

ETL/ELT Pipeline Design

Design Extract-Transform-Load pipeline for data integration from multiple sources.

Step 4

Data Cleaning & Transformation

Handle nulls, duplicates, type mismatches, and apply standardization rules.

Step 5

Exploratory Data Analysis (EDA)

Run statistical summaries, correlation analysis, and distribution studies.

Step 6

Statistical Analysis & Modeling

Apply regression, time-series forecasting, clustering or association analysis.

Step 7

Visualization Dashboard

Build interactive dashboards using Matplotlib, Seaborn, Plotly, or Power BI.

Step 8

Insights & Recommendations Report

Translate findings into clear business recommendations with supporting charts.

Step 9

Code Documentation

Documented Jupyter notebooks / scripts with methodology and interpretation.

Step 10

Final Presentation & Delivery

Complete project with data, analysis code, dashboards and presentation slides.

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Get in touch and our team will reach out within 2 hours with a customised plan and quote.