Data Science & AI Roadmap for Candidates and Career Switchers
Clear steps to learn the right skills, pick the right role, and meet industry demand—without guesswork.
12-16
Weeks to become job-ready with focused learning
6+ Roles
Analyst, Scientist, ML/AI Engineer, Data Engineer, Architect
5 Projects
Portfolio to showcase skills to recruiters
2026
Aligned to current tools & industry demand
Actionable Plan
6-Step Roadmap for Candidates
Start: Computer + Data Foundations
Python basics, Git/GitHub, Linux shell, spreadsheets/SQL fundamentals.
Step 2: Data Analysis & Visualization
EDA, cleaning, dashboards; tell stories with data for business teams.
Step 3: Statistics & Math for ML
Descriptive stats, probability, regressions, gradients—enough to reason about models.
Step 4: Machine Learning & Deep Learning
Supervised/unsupervised learning, deep neural networks, model evaluation, feature engineering, pipelines.
Step 5: Big Data & Generative AI
Big data processing with PySpark on Databricks, Generative AI models, Agentic AI systems, and production deployment.
Step 6: Portfolio & Hiring
Ship 3–5 projects, publish write-ups, mock interviews, and tailor resumes to each role.
Career Paths
Pick the Role That Fits You
Python / Automation engineer
Build automation and data solutions
Automation scripts, web scraping, data analysis, API development for business solutions.
Data / BI Analyst
Tell stories with data
Dashboards, KPIs, stakeholder insights, quick wins.
Data Engineer
Move data at scale
ETL/ELT, warehouses, orchestration, quality.
AI / ML engineer
Ship models reliably
Pipelines, APIs, monitoring, GPU/LLM workflows.
Gen AI engineer
AI features that ship
RAG, Agents, prompt engineering, evaluation, safety.
Data Scientist
Model and experiment
Predictive models, experimentation, feature engineering.
What to Learn
Core Skills by Track
Foundations
- Python: data types, loops, functions, notebooks
- SQL: joins, window functions, CTEs
- Git/GitHub, command line, virtual envs
Analytics / BI
- EDA, data cleaning, KPI design
- Dashboards (Power BI)
- Storytelling: insights to business actions
Machine Learning
- Supervised/unsupervised, metrics, overfitting
- Feature engineering, cross-validation
- Model explainability & fairness basics
Data Engineering
- Warehousing (Snowflake/BigQuery/Redshift)
- ETL/ELT with Airflow/Prefect
- PySpark on Databricks for big data
MLOps / Deployment
- API serving (FastAPI), CI/CD basics
- Containers (Docker), cloud (AWS/GCP/Azure)
- Monitoring, drift, model retraining
AI & LLMs
- Prompt engineering, RAG patterns
- Vector databases, embeddings
- Evaluation, safety, governance
Future & Demand
What Industry Needs in 2026
Generative AI Everywhere
LLM-powered features (chat, summarize, recommend). RAG, prompt engineering, guardrails, evaluation.
Production ML & MLOps
Pipelines, monitoring, cost efficiency, A/B testing, rollback strategies.
Modern Data Stacks
Cloud warehouses, data modeling, event pipelines, metrics layers.
Responsible AI & Privacy
Bias checks, interpretability, compliance (GDPR/DPDP), security of data/PII.
Action Plan
90-Day Launch Plan
Days 1–30
Lay Foundations
- Python fundamentals, Git version control
- Databases (SQL), Web scraping
- Data analysis, Data engineering basics
- Big data processing with PySpark
Days 31–60
Build ML & AI Skills
- Machine Learning fundamentals
- Deep Learning with PyTorch/TensorFlow
- Generative AI models and applications
- Agentic AI systems and workflows
Days 61–90
Ship & Apply
- Project development and portfolio building
- Profile/Resume/LinkedIn optimization
- Interview preparation and practice
- Active job hunt with tailored applications
Learning Path Overview
Skill & Tool Progression
Portfolio & Career Milestones
Ready to move
Get a personalized roadmap with a mentor
Book a free counseling session to map your background to the right role, projects, and interview prep plan.