Data Analysis Cycle

📘 Roles in the Data / AI Project Cycle (Improved & Expanded Notes)

A Data/AI project usually moves through 4 major phases:

1️⃣ Understanding the Problem
2️⃣ Building & Preparing Data
3️⃣ Analyzing and Modeling
4️⃣ Building & Deploying AI Solutions

Each role fits into one or more of these phases.

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🔹 1. Business Analyst (BA)

Main Duties:

• Identifies business problems and goals.

• Talks to clients and understands needs clearly.

• Converts business needs into technical requirements and documents.

• Ensures the technical team understands the purpose of the project.

Their Importance:

• Without a BA, the project may be built wrongly because the technical team won’t know the real business need.

Connection:

• BA explains WHAT to build.

• Team Lead decides HOW to build it.

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🔹 2. Team Lead

Main Duties:

• Manages the entire team.

• Assigns tasks and checks progress.

• Solves conflicts and ensures cooperation.

• Keeps contact between BA, engineers, analysts, and scientists.

• Ensures project is completed on time.

Their Importance:

• Acts as the “captain” of the team.

• Ensures every role works smoothly together.

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🔹 3. Data Engineer

Main Duties:

• Builds pipelines to collect raw data from multiple sources (apps, websites, sensors, databases).

• Cleans, filters, and organizes data.

• Converts messy data into clean, usable format.

• Ensures data is fast and available for analysis.

• Works with big data tools.

Their Importance:

• Without clean data, analysis and AI models will fail.

Connection with Database Engineer:

• Data Engineer: Manages how data flows.

• Database Engineer: Manages where data lives.

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🔹 4. Database Engineer

Main Duties:

• Designs databases (SQL / NoSQL).

• Controls how data is stored, organized, and secured.

• Ensures database is fast, safe, and optimized.

• Maintains backups and data integrity.

Their Importance:

• Acts like the “architect” of all stored data.

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🔹 5. Data Analyst

Main Duties:

• Reads, studies, and interprets data.

• Creates dashboards, charts, and visualizations.

• Helps businesses understand trends, patterns, and insights.

• Answers questions like “Why did sales drop?” or “Which product is performing best?”

Their Importance:

• Converts data into meaningful insights that companies can use to take decisions.

Connection:

• Uses clean data prepared by Data & Database Engineers.

• Helps BA understand what the data reveals.

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🔹 6. Data Scientist

Main Duties:

• Builds machine learning and predictive models.

• Uses advanced math, statistics, and algorithms.

• Tests multiple models to pick the best one.

• Finds hidden patterns in data.

Their Importance:

• Turns analysis into future predictions.

• Enables automation and smart decision-making.

Connection:

• Uses structured data prepared by Data Engineers.

• Passes trained models to AI Engineers.

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🔹 7. AI Engineer

Main Duties:

• Takes the ML model created by Data Scientists.

• Converts it into real products (apps, APIs, chatbots, image recognition systems).

• Deploys models on cloud servers.

• Regularly updates and maintains AI systems for accuracy.

Their Importance:

• Without AI Engineers, AI models stay only in the lab and never reach real users.

Connection:

• Data Scientist builds the model.

• AI Engineer makes the model work in real life.

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📊 Updated and More Detailed Comparison 

Role	Main Responsibility	Key Skills	Pipeline Stage	Works With
Team Lead	Manages team, tasks, timeline	Leadership, planning, communication	All stages	All roles
Business Analyst (BA)	Understands business needs → writes requirements	Documentation, communication	Stage 1: Understanding	Team Lead, Data Analyst
Data Engineer	Collects, cleans, and moves data (pipelines)	Python, SQL, Big Data  tools	Stage 2: Data Building	Database Engineer, Data Scientist
Database Engineer	Designs and maintains databases	SQL, DB design	Stage 2: Data Building	Data Engineer
Data Analyst	Analyzes data, finds insights, creates dashboards	Excel, SQL, Tableau/Power BI	Stage 3:Analysis	BA, Data Scientist
Data Scientist	Builds machine learning & predictive models	Statistics, ML, Python	Stage 4: Modeling	Data Engineer, AI Engineer
AI Engineer	Converts AI models into real applications	Python, Cloud, APIs, ML Ops	Stage 5: Deployment	Data Scientist