Customer churn analysis
Define requirements, build the solution, document decisions, and present the outcome for review.
Python
Data & AI career programData Science & Artificial Intelligence
Learn Python, SQL, statistics, machine learning, visual analytics, responsible AI, and practical generative-AI workflows.
Admissions counsellingRequest syllabus & fees Speak to an advisor
Is this program right for you?
24 weeks
Indicative program duration
Indicative program duration
Classroom + live online
Subject to batch availability
Subject to batch availability
Foundation to applied
Starting level and progression
Starting level and progression
Career preparation
Portfolio, resume, and interview practice
Portfolio, resume, and interview practice
Final syllabus, fees, batch times, and prerequisites must be confirmed before enrolment.
Build practical capability, not just familiarity.
Prepare and analyse real datasets
Write analytical SQL queries
Build and evaluate ML models
Communicate findings through dashboards
Use generative AI responsibly in data workflows
A progressive learning sequence.
This curriculum is representative and may be adjusted for batch level, technology updates, and confirmed academic plans.
Python foundations, NumPy, Pandas, notebooks, cleaning, transformation, and reproducible analysis.
Queries, joins, window functions, relational concepts, schemas, and analytical problem solving.
Descriptive statistics, probability, inference, experimentation, Matplotlib, and dashboards.
Feature engineering, supervised and unsupervised learning, model evaluation, and explainability.
Neural-network concepts, NLP foundations, generative-AI workflows, prompt evaluation, and responsible use.
Capstones, storytelling, business cases, GitHub portfolio, assessments, and mock interviews.
Projects designed to make learning visible.
Sales forecasting dashboard
Define requirements, build the solution, document decisions, and present the outcome for review.
Applied AI knowledge assistant
Define requirements, build the solution, document decisions, and present the outcome for review.
Entry-level roles this path can support.
Data AnalystJunior Data ScientistBI AnalystML Associate
Role titles are examples of common career directions. Eligibility and hiring depend on qualifications, experience, performance, market conditions, and employer requirements.
Before you enrol.
Prerequisites vary by program and batch. Counselling should confirm your current level and whether foundation preparation is recommended.
The learning model includes guided labs and portfolio-oriented projects. Final project scope should be confirmed in the official syllabus.
No employment outcome can be guaranteed. PROSKILL IT provides placement assistance and career preparation; hiring decisions remain with employers.