Live online beginner pathway
Learn Python, SQL, statistics, data visualisation and machine-learning foundations through live classes and portfolio projects.
Build the skills to analyse real business data, explain your decisions and present work that can be reviewed in interviews—not simply collect another certificate.
Learning path
Placement support follows written eligibility, scope and exclusion terms and does not guarantee employment.
You will learn how to prepare and query data, explore patterns, test assumptions, build baseline machine-learning models and communicate findings through dashboards and project reports. The emphasis is on making defensible decisions with data and producing work another person can review and reproduce.
| Skill | Practical work | Evidence |
|---|---|---|
| Python and pandas | Load, clean, transform and validate tabular data | Reproducible analysis notebook or script |
| SQL | Filter, join, aggregate and summarise relational data | Documented SQL query pack |
| Statistics | Describe samples, quantify uncertainty and test appropriate assumptions | Short statistical decision note |
| EDA and visualisation | Find patterns, anomalies and limitations; select charts that match the question | Exploratory analysis report |
| Business intelligence | Define KPIs and create a usable filtered dashboard | Dashboard plus decision memo |
| Machine-learning foundations | Frame regression or classification problems and establish valid baselines | Model experiment report |
| Evaluation | Choose relevant metrics, check leakage and analyse errors | Evaluation and limitations section |
| Portfolio communication | Package assumptions, methods, results and next steps | README and project presentation |
This pathway is designed for learners who need a structured beginning in Python, SQL, statistics and applied data work. You can come from a technical or non-technical degree, but you should be ready to practise coding, solve quantitative problems and complete projects consistently.
If you have recently completed BTech, BCA, BSc, BCom, BBA, MBA or another degree, this course can help you turn academic knowledge into practical work you can discuss in interviews. Your degree alone does not make you job-ready; the value comes from completing and understanding the projects.
If you work in sales, finance, operations, marketing, MIS or customer support, you already understand business questions. The course helps you add Python, SQL, analysis and modelling skills so that you can investigate those questions with data.
You can begin without an engineering degree. Expect the first weeks of programming and statistics to require steady practice. Prior coding experience is helpful but not compulsory if you are prepared to practise consistently.
If you can already clean data independently, write SQL joins, validate models and explain evaluation choices, a more advanced programme may fit better.
Before you start
You will complete projects that move from business analysis to predictive modelling. Each project should include a clear question, reproducible work, validation, limitations and a final explanation—not only a finished chart or copied notebook.
Learner work samples (an anonymised dashboard preview, a report excerpt or a sample README) are added here as they are consented and verified—never a decorative mockup labelled as learner work.
Assessment standards
Projects are reviewed for whether the data work is correct, the evaluation is appropriate, another person can reproduce the result, and the learner can explain the decision. A polished dashboard cannot compensate for incorrect data or weak validation.
Types, joins, missing values, duplicates, transformations and target definition are handled correctly.
Split strategy, baselines, metrics and checks match the problem; leakage is avoided.
Files, dependencies, steps and assumptions are organised so the work can be rerun.
Choices are justified and alternatives or limitations are acknowledged.
Conclusions follow from the evidence and do not overstate what the data proves.
README, visuals and presentation make the work understandable to technical and non-technical reviewers.
Marks, pass percentages, reviewer frequency and reassessment rules follow the written academic policy; they are not invented on this page. For forecasting work, evaluation preserves time order; for classification or regression, the chosen metrics and validation method should match the problem rather than only produce a higher score.
The syllabus progresses from programming and data foundations to analysis, dashboards and classical machine learning. Each module should produce a practical output that becomes input to later work, so learners understand how the pieces connect.
What this beginner pathway does not cover in depth
Advanced deep learning, LLM fine-tuning, production RAG, Agentic AI orchestration and MLOps infrastructure are separate specialisations. This course builds the Python, data, statistics and modelling foundation needed before many of those pathways.
The course runs for six months through live online classes, guided practice and independent project work. Learners can join from across India. The exact weekly timetable, recording-access period and support schedule must match the current batch information shared before enrolment.
Completion and career guidance
Learners who meet the published completion requirements receive a School of Core AI course-completion certificate. Placement support is a separate, eligibility-based career service and does not guarantee an interview, offer, salary or employment.
The current completion requirements are provided with the batch and enrolment documents. The certificate confirms completion; it is not an industry or university accreditation.
Placement support is delivered under written eligibility conditions. Confirm the included services for the current batch before enrolling.
Fees and instalment options can change by batch. Request the current written fee sheet, including GST, payment dates and cancellation terms, before enrolling.
Last curriculum review: 2026-09-09
Choose Data Science when you need foundations in Python, SQL, statistics, analysis and predictive modelling. Choose Generative AI when you already have programming foundations and want to work with LLMs, RAG and multimodal systems. Choose AI Engineering when your goal is to build, integrate and deploy complete AI applications.
Data Science
You are viewing this courseGenerative AI
AI Engineering
Yes. Previous coding experience is helpful but is not compulsory for this beginner pathway if you are prepared to practise consistently. The course starts with Python and data-handling foundations. Learners from BTech, BCA, BSc, BCom, BBA, MBA and non-IT backgrounds should expect to write, understand and debug their own code rather than only copy notebooks.
You do not need advanced mathematics to begin. Comfort with arithmetic, percentages, averages, graphs and school-level algebra is helpful. The course develops the statistical reasoning needed to interpret data and evaluate models, but you will still need to practise the concepts through exercises and projects.
The course runs for six months. Your weekly commitment includes scheduled live classes, guided exercises and independent project work. The current timetable and expected workload are shared with the batch details before enrolment.
Yes. You learn Python, SQL, statistics, exploratory analysis, data visualisation and dashboards before moving into machine-learning foundations and model evaluation. Advanced deep learning, LLM systems and production AI engineering are separate pathways rather than hidden promises inside this beginner course.
The planned portfolio includes retail performance analysis, a business dashboard, a customer-retention model, an applied forecasting or segmentation track and a role-aligned capstone. Each project should include the problem, data checks, method, validation, limitations and a deliverable you can explain during a review or interview.
You need a personal laptop that can run a modern browser, Python, Jupyter or VS Code, database tools and the approved BI application. A dedicated GPU is generally not required for this foundational course. Admissions should provide the current operating-system, memory and software requirements before enrolment.
Classes are delivered live online, so learners can join from across India. The approved recording-access period, attendance expectations and missed-class support for the current batch are shared before enrolment.
The fee section shows the approved total fee position for the current batch, including GST treatment, the booking amount and the available instalment or EMI schedule where confirmed. Before paying, request the written payment, cancellation and refund terms rather than relying on an older advertisement or verbal quotation.
You receive a School of Core AI completion certificate after meeting the requirements defined for the current cohort. The applicable attendance, assignment, assessment and capstone conditions are stated in the enrolment documents. The certificate confirms completion; your ability to explain your work remains more important in interviews.
Placement support is structured career assistance delivered under written eligibility conditions; it does not guarantee employment. The current policy defines the included portfolio or resume reviews, interview preparation, profile-sharing process, participation requirements, support period and exclusions.
This six-month Data Science course focuses on Python, SQL, statistics, analysis, dashboards and introductory machine learning. The ten-month Full Stack Data Science pathway is intended for learners who want wider end-to-end coverage and a longer project journey. Compare the detailed syllabus and prerequisites before choosing.
Choose Data Science if you first need foundations in analysing data, using SQL, applying statistics and evaluating predictive models. Consider Generative AI if you already have suitable Python or development foundations and want deeper work with LLMs, RAG, model adaptation, multimodal systems and evaluation.
Consider AI Engineering when your main goal is to build, integrate and deploy complete AI applications and systems. Choose Data Science when you need stronger foundations in analysing data, statistical reasoning and predictive modelling. Compare the AI Engineering prerequisites with your current programming experience before selecting a starting point.
Share your current background and the role or skills you are working toward. The admissions team can help you compare the Data Science, Generative AI and AI Engineering pathways and send the current syllabus, fee sheet and batch details.