Practical by design
Exercises and projects turn concepts into repeatable workflows.
Programs for international learners
Learn deeply. Build confidently. Move forward.
Develop practical foundations in data, progress into machine learning, and explore modern AI engineering through guided study and portfolio-oriented work.
Practical learning
Portfolio projects
Interview preparation
Job-search assistance
LEARNING PATHData → ML → modern AI
PRACTICEBuild and explain projects
3progressive pathways
2–5 monthsof structured learning
Project-ledpractice throughout
Career preparationbeyond the curriculum
WHY MINIMAXI
A clear route from foundational knowledge to applied work
Minimaxi Private Limited offers structured technology training for learners building skills in analytics, data science and AI engineering. The programs connect concepts with practice, help learners assemble demonstrable project work, and include preparation for presenting that work in career conversations.
Students can begin with the pathway that matches their current foundation. Each program states its prerequisites and direction clearly, so learners can choose without assuming that course completion alone guarantees a job or a particular outcome.
Progressive structure
Foundations come before advanced modeling and AI application topics.
Portfolio-aware
Learners practise documenting decisions, results and limitations.
Career-conscious
Support focuses on readiness, communication and informed job search.
ONE PROGRESSIVE PATHWAY
Start at the right level and keep building
The three programs are distinct choices with connected foundations. Select one based on your goals and current experience; progressing through every program is not required.
- 01Data AnalyticsClean, query, visualize and communicate data.
- 02Data ScienceUse statistics and machine learning to study patterns and predictions.
- 03AI EngineeringDevelop LLM, retrieval and automation applications.
PROGRAMS
Choose the learning depth that fits your direction
Fees shown on first load are for international learners. Regional presentation updates after region resolution.
DATA FOUNDATION
Data Analytics
Learn to transform raw data into reliable analysis, business answers and clear dashboards.
Ideal learner
Students, graduates and early-career professionals who want a practical entry into data work.
Prerequisites
Comfort using a computer and spreadsheets. No prior programming experience is required.
Six curriculum stages
- Python essentialsVariables, conditions, loops, functions and working with files.
- NumPy and PandasDataFrames, indexing, cleaning, transformation, grouping and merging.
- Exploratory analysisDistributions, outliers, relationships and responsible interpretation.
- SQL for analysisFiltering, aggregation, joins, CTEs and window functions.
- Excel and Power QueryFormulas, lookups, pivots and repeatable preparation workflows.
- Dashboards and storytellingKPIs, data models and interactive reporting in Power BI or Tableau.
Tools
Python, Pandas, SQL, Excel, Power Query, Power BI and Tableau.
Projects
Exploratory report, SQL business analysis, spreadsheet workflow and dashboard.
Learning outcomes
Prepare data, investigate business questions, create clear visuals and explain analytical choices.
Role directions
Data analyst, BI analyst and junior reporting roles, subject to employer requirements.
STATISTICS + MACHINE LEARNING
Data Science
Build on data foundations with statistics, supervised and unsupervised learning, NLP and deployment concepts.
Ideal learner
Learners who want to progress from analysis into predictive modeling and applied machine learning.
Prerequisites
Basic Python and comfort with data tables; analytics foundations are included for revision.
Six curriculum stages
- Data and statisticsPython, SQL and EDA review; probability, distributions and inference.
- Supervised learningRegression, classification, regularization and tree-based models.
- Model developmentFeatures, validation, tuning, metrics and model comparison.
- Unsupervised learningClustering, dimensionality reduction, anomalies and interpretation.
- NLP and neural foundationsText features, embeddings, classification and neural-network basics.
- Production foundationsPipelines, experiment tracking concepts, APIs, containers and drift awareness.
Tools
Python, Pandas, scikit-learn, SQL, XGBoost or LightGBM, PyTorch, FastAPI and Docker.
Projects
Predictive modeling study, model comparison, NLP application and deployment-oriented capstone.
Learning outcomes
Frame modeling problems, compare approaches, communicate limitations and package a basic model service.
Role directions
Junior data scientist, ML intern and junior ML roles, subject to employer requirements.
MODERN AI APPLICATIONS
AI Engineering
Connect data and ML foundations with LLM applications, retrieval systems, evaluation, agents and automation.
Ideal learner
Learners with programming interest who want to build and evaluate applied generative-AI systems.
Prerequisites
Basic Python is recommended. Relevant data, ML and NLP foundations are reviewed within the pathway.
Six curriculum stages
- ML and deep-learning foundationsModel workflows, NLP concepts, PyTorch and production awareness.
- Transformers and LLMsAttention, tokenization, model families, open models and quantization concepts.
- LLM application engineeringAPIs, prompting, structured output, validation and tool calling.
- Retrieval-augmented generationIngestion, chunking, embeddings, retrieval, reranking and grounding.
- Evaluation and safetyTracing, quality checks, latency and cost awareness, guardrails and PII care.
- Agents and automationTool-based workflows, orchestration, memory concepts, n8n and service integration.
Tools
Python, PyTorch, Hugging Face, vector databases, FastAPI, LangGraph or CrewAI, and n8n.
Projects
LLM application, grounded knowledge assistant and tool-using automation capstone.
Learning outcomes
Design an AI workflow, ground responses, evaluate behavior and discuss safety and operational trade-offs.
Role directions
Junior AI, applied AI, GenAI and AI automation roles, subject to employer requirements.
LEARNING EXPERIENCE
A four-step rhythm from concept to communication
- 01
Understand
Study the core idea, its purpose, assumptions and common limitations.
- 02
Practise
Use guided exercises to develop familiarity with tools and workflows.
- 03
Build
Apply the skills in projects with decisions, outputs and documentation.
- 04
Present
Explain the problem, approach, results, constraints and next steps.
FOR LEARNERS IN INDIA
Internship Program + Certificate
A separate practical option with guided tasks and project work. A certificate is provided on successful completion of the applicable internship requirements.
- Data Analytics InternshipPractical tasks and mini project₹5,000
- Data Science InternshipPractical tasks and project work₹10,000
- AI Engineering InternshipPractical tasks and project work₹15,000
LEARN BY BUILDING
Projects make the learning visible
Project work helps learners practise decisions and discuss what they built. Individual project scope may vary with the program and batch.
Business analytics dashboard
Clean a dataset, answer questions with SQL and present findings through an interactive dashboard.
Predictive modeling study
Prepare features, compare models, evaluate trade-offs and document model limitations.
Grounded knowledge assistant
Build ingestion, chunking, retrieval, reranking and answer grounding over a document collection.
Tool-using AI workflow
Connect tools and orchestration steps to automate a defined, reviewable workflow.
CAREER PREPARATION
Prepare to discuss your skills and pursue relevant opportunities
Career support complements training; it does not guarantee employment, a specific title or salary.
Interview preparation
Review technical topics and practise explaining decisions made in projects.
Portfolio preparation
Organize project context, methods, outputs and limitations for review.
Job-search assistance
Identify relevant role directions and present skills clearly in applications.
Communication practice
Build confidence discussing technical work, collaboration and learning goals.
Placement-fee terms
Where a separate placement-support arrangement is offered and accepted in writing, a 5% fee may apply only to the candidate's first-year gross salary after joining through that specific service.
Availability, eligibility, calculation, timing and exclusions must be confirmed in the signed terms before participation. This is not a placement guarantee and is not automatically part of a course purchase.
BROCHURE HUB
Review the details for your region
Open the brochure that matches your location and area of interest.
International Programs
Program information and international fees for Data Analytics, Data Science and AI Engineering.
Open brochureIndia Programs
Program information and India fees for Data Analytics, Data Science and AI Engineering.
Open brochureInternship Program
Practical internship options and successful-completion certificate information.
Open brochure
ABOUT THE COMPANY
Technology education grounded in progression and practice
Minimaxi Private Limited focuses on career-aware learning across data analytics, data science and AI engineering. The curriculum is organized to help learners understand why a technique is used, practise how it works, and develop a project they can explain.
The learning paths deliberately distinguish training from employment outcomes. Minimaxi provides instruction, project guidance, interview preparation and job-search assistance; results also depend on each learner's effort, eligibility, performance, market conditions and employer decisions.
Prospective learners are encouraged to ask about current schedules, delivery format, prerequisites, fees and applicable terms before enrolling.
LEARN • BUILDEXPLAIN • PREPARE
QUICK ANSWERS
Frequently asked questions
Which program should a beginner choose?
Data Analytics is the most direct starting point for learners new to programming and data work. Ask Minimaxi for guidance based on your current skills and goal.
Does Data Science cover analytics foundations?
It reviews relevant Python, SQL and exploratory-analysis foundations before moving into statistics and machine learning.
Does AI Engineering include machine-learning foundations?
It reviews relevant ML, NLP and deep-learning concepts before progressing into LLM applications, retrieval, evaluation, agents and automation.
Are the programs practical?
The pathways combine concepts, guided exercises and portfolio-oriented projects. Project scope can vary by program and batch.
Is prior programming experience required?
Data Analytics does not require prior programming. Basic Python is helpful for the longer pathways; review content is included.
What career support is included?
Support includes interview, portfolio, communication and job-search preparation. Employment and salary outcomes are not guaranteed.
How do I confirm schedules and enrollment terms?
Use the enquiry form, email or WhatsApp to request current batch dates, delivery details and applicable terms before enrolling.
Are the displayed fees region-specific?
Yes. The page presents either India or international program pricing after resolving the visitor's region.
Is the Internship Program the same as a training program?
No. It is a separate India-only practical option. A certificate is provided on successful completion of the applicable requirements.
ENQUIRE
Tell us what you want to learn
Ask about curriculum, current batches, fees, enrollment or which pathway may fit your starting point.