Career Path
Your Roadmap from Beginner to Job-Ready
The data science & AI career path is structured so that learners can start from basics and gradually move into high-demand roles like Data Analyst, Data Scientist, AI/ML Engineer, Generative AI Engineer, Data Engineer, and MLOps Engineer. Most professionals grow from entry-level roles into senior / specialist / leadership roles over time.
We have designed our programs to match real industry career ladders seen in current job postings and global learning platforms.
Data Science & Analytics
From data fundamentals to analytics leadership
Track covers:
Data Science and Analytics
Data Analyst (Business Analyst)
Step 1: Foundation Learner / Trainee
Typical stage
- Student
- Fresher
- Career Switcher
You build:
Basics of statistics & probability
Excel / Google Sheets for data handling
SQL fundamentals (SELECT, WHERE, GROUP BY, basic JOIN)
Python basics: variables, loops, functions, pandas, numpy
Comfort with charts, summaries, and interpreting simple datasets
Step 2 – Junior Data Analyst
Typical job titles
- Junior Data Analyst
- Reporting Analyst
- Business Analyst – Intern / Associate
You can now:
Write intermediate SQL (JOINs, aggregations, filters)
Clean and preprocess data for analysis
Build dashboards in Power BI / Tableau / Looker Studio
Do EDA (exploratory data analysis) and generate insights
Create basic KPI reports for business teams
Step 3 – Data Analyst / Business Analyst
Typical job titles
- Data Analyst
- Business Analyst
- BI Analyst
Key skills at this level:
Advanced SQL (window functions, CTEs, subqueries)
Strong dashboarding and data storytelling
Understanding of business domains (e.g., Healthcare, E-commerce, Finance, Supply Chain)
Collaborate with product, marketing, operations, and leadership on data-driven decisions
At this stage, many analysts choose one of two paths:
Advanced SQL (window functions, CTEs, subqueries)
Strong dashboarding and data storytelling
Step 4 – Data Scientist (Entry / Mid-Level)
Typical job titles
- Junior Data Scientist
- Applied Data Scientist
- ML Analyst / Associate Data Scientist
You’re expected to:
Use ML algorithms (regression, classification, clustering, time series)
Perform feature engineering, model training, and evaluation
Use Python ML stack: scikit-learn, statsmodels, etc.
Work on end-to-end projects: from data collection to model deployment (with support)
Present model results and business impact clearly
Step 5 – Senior / Lead Roles in Analytics & Data Science
Typical job titles
- Senior Data Analyst
- Senior Data Scientist
- Analytics Manager / Lead
- Head of Data / Director of Analytics
Focus areas:
Owning high-impact projects and metrics
Designing data strategy for teams or business units
Mentoring juniors and leading data teams
Working closely with C-level / senior leadership on decisions
AI, Machine Learning & Generative AI Career Path
From Python foundations to AI leadership
Track covers:
Artificial Intelligence and Machine Learning (AI & ML)
Generative AI
Step 1 – Python & Data Foundations
Building the base
Before becoming an AI / ML / GenAI engineer, learners must be comfortable with:
Python programming (functions, OOP, error handling)
Data handling (pandas, numpy)
Basic statistics & probability
Git / GitHub basics
These skills are common across all AI engineering job postings.
Step 2 – Junior ML Engineer / AI Engineer
Typical job titles
- Junior ML Engineer
- AI Engineer – Intern / Associate
- Data Scientist (ML-focused)
Skills required:
Supervised & unsupervised learning
Model evaluation & tuning (hyperparameters)
APIs and basic deployment (Flask/FastAPI)
Experience with TensorFlow / PyTorch
Step 3 – AI/ML Engineer (Mid-Level)
Typical job titles
- AI/ML Engineer
- Applied ML Engineer
- Machine Learning Engineer
You can:
Build and optimize ML pipelines for training & inference
Work with large datasets and data processing frameworks
Integrate models into products via services / microservices
Collaborate with MLOps / platform teams on deployment and monitoring
Step 4 – Generative AI Engineer / LLM Engineer
Typical job titles
- Generative AI Engineer
- LLM Engineer
- AI Solutions Engineer
Skills expected in current GenAI postings:
Work with LLMs & foundation models (OpenAI, Claude, Llama, etc.)
Prompt engineering, prompt chaining, tool-calling
Build RAG systems with vector databases
Fine-tuning / LoRA / model adaptation for enterprise data
Implementing guardrails, safety, and compliance for GenAI applications
Step 5 – Senior / Lead AI Roles
Typical job titles
- Senior AI Engineer
- AI Architect
- Head of AI
- Director of AI
You focus on:
Designing AI strategy and architecture
Evaluating build vs buy for AI platforms
Leading teams of AI/ML and GenAI engineers
Working directly with business and product leadership
Data Engineering & MLOps Career Path
From data pipelines to platform architecture
Track covers:
Data Engineering (Data Specialist)
Data Science and MLOps
Step 1 – Data Engineering / ETL Foundations
You learn:
SQL at scale
Basics of data warehousing & data modeling
ETL / ELT concepts
Python for data pipelines
These fundamentals appear across most data engineer job descriptions.
Step 2 – Junior Data Engineer
Typical job titles
- Junior Data Engineer
- ETL Developer
- BI Data Engineer
Skills:
Building ETL jobs (e.g., with Airflow, dbt, custom scripts)
Working with data warehouses (Snowflake, Redshift, BigQuery)
Ensuring basic data quality & availability
Step 3 – Data Engineer / MLOps Engineer (Mid-Level)
Typical job titles
- Data Engineer
- MLOps Engineer
- Platform Data Engineer
You do:
Build scalable data pipelines for analytics and ML
Handle streaming & batch data (Spark, Kafka, etc.)
Design and maintain ML pipelines from training to deployment
Implement CI/CD for data & ML workflows
Step 4 – Senior Data Engineer / Senior MLOps Engineer
Typical job titles
- Senior Data Engineer
- Senior MLOps Engineer
- Data Platform Engineer
- ML Platform Engineer
Focus areas:
Data & ML platform architecture
Reliability, monitoring, cost optimization
Governance, access control, and security for data & ML systems
Step 5 – Architect / Head of Data Platform
Typical job titles
- Data Architect
- Head of Data Engineering
- Director – Data Platform / ML Platform
Need help?
Our learning advisors are here to guide you. Whether you’re starting your journey in Data Science, AI, or Data Analytics, we’ll help you choose the right program based on your background and career goals.