01
Data Analytics + Gen AI
Moderate
Foundational
High
Very high
Light
Applied layer
Program comparison
Four programs. Four distinct career directions.
Compare the work each pathway develops: analysing and communicating data, supporting business decisions, building predictive systems or engineering LLM-powered applications.
01 Start with role goal
02 Check technical depth
03 Confirm time commitment
01 · ANALYTICS
Data Analytics + Gen AI
Best suited to querying data, finding patterns, building dashboards and communicating insights clearly.
02 · BUSINESS
Business Analytics + Gen AI
Best suited to KPI design, commercial analysis, decision support and evidence-backed recommendations.
03 · MODELLING
Data Science & AI
Best suited to deeper coding, mathematical reasoning, machine learning and model deployment.
04 · AI APPS
Generative AI
Best suited to LLM applications, retrieval systems, multimodal tools and agentic workflows.
Each program includes eight portfolio projects, live online weekday or weekend batches and up to 12 zero-interest EMIs. The profile match and depth labels are directional guidance, not admissions decisions or eligibility scores.
Interactive profile match
Start with two questions, not forty features.
Select the description closest to your current background, then the kind of work you want to move toward. The result is a directional recommendation designed to help you compare intelligently.
STEP 01 Which starting point is closest?
STEP 02 What work do you want to do most?
Depth profiles
The courses overlap in tools. Their centre of gravity is different.
Programs may share tools while developing different capabilities. These qualitative profiles show where each pathway places greater learning emphasis.
02
Business Analytics + Gen AI
Light–moderate
Foundational
Very high
High
Light
Applied layer
03
Data Science & AI
Very high
Very high
Moderate
Moderate
Core focus
Adjacent
04
Generative AI
High
Conceptual
Moderate
Light
Conceptual
Core focus
These are qualitative comparison profiles—not test scores, prerequisites, admissions cut-offs or measured percentages.
Side-by-side program details
Compare what shapes your learning experience.
Review duration, curriculum, fee, technical intensity, project direction and role alignment before choosing your program.
Program-fit guidanceProgram details
| Dimension | 01Data Analytics + Gen AI | 02Business Analytics + Gen AI | 03Data Science & AI | 04Generative AI |
|---|---|---|---|---|
| Program duration | 6 months | 6 months | 12 months | 4 months |
| Structured curriculum | 18 weeks | 18 weeks | 36 weeks | 16 weeks |
| Modules | 6 | 5 | 8 | 5 |
| Portfolio projects | 8 | 8 | 8 | 8 |
| Program fee | ₹1,10,000 + GST | ₹1,10,000 + GST | ₹1,40,000 + GST | ₹90,000 + GST |
| Delivery | Live online · weekday or weekend batches | Live online · weekday or weekend batches | Live online · weekday or weekend batches | Live online · weekday or weekend batches |
| Payment option | Up to 12 zero-interest EMIs | Up to 12 zero-interest EMIs | Up to 12 zero-interest EMIs | Up to 12 zero-interest EMIs |
| Primary work | Analysis, dashboards, reporting and insight communication | Business diagnosis, KPI design, forecasting and recommendations | Predictive modelling, experimentation, deep learning and ML systems | LLM applications, retrieval, tool use and AI workflows |
| Best starting background | Graduates, career switchers, operations/MIS learners, early analysts | Business, operations, finance, marketing, consulting or MBA-oriented learners | Technical/quantitative learners, analysts moving into modelling, developers | Developers, technical analysts, product builders and AI application learners |
| Coding guidance | Moderate; Python becomes an analysis tool | Light–moderate; programming supports business analysis | Very high; Python is a core working environment | High; application logic and API integration matter |
| Math guidance | Statistics for analysis and interpretation | Statistics for decisions, forecasting and business reasoning | Strong mathematics for ML and deep learning | More architecture/application reasoning than heavy mathematical derivation |
| Portfolio centre | Dashboards, analytical investigations and reporting workflows | Decision packs, KPI systems, business cases and recommendation narratives | Predictive models, experiments, deep-learning systems and deployment | RAG apps, AI assistants, tool-using workflows and GenAI prototypes |
| Role direction | Data Analyst, Business Intelligence Analyst, Reporting Analyst and junior data-focused roles | Business Analyst, Strategy Analyst, Operations Analyst and Marketing Analyst | Data Scientist, Machine Learning Engineer, AI Developer and research-oriented data/AI roles | Generative AI Engineer, LLM Developer, AI Product Engineer and other applied LLM-building roles |
| Choose this if... | You want to become strong at turning raw data into decisions people can understand. | You want analytics to sit close to business strategy, operations or commercial decisions. | You genuinely want deeper coding, modelling and technical AI problem-solving. | You want to build applications around LLMs rather than study the full classical ML stack first. |
| Consider another program if... | Your real goal is deep ML engineering or advanced model research. | You want a highly technical ML path with substantial mathematical depth. | You mainly want dashboards, business reporting or a shorter analytics transition. | You want broad classical ML, computer vision and deep-learning foundations. |
Curriculum sequence
Review how each learning path progresses.
The module sequence shows how each program develops capability from foundations through applied work and career preparation.
01
Data Analytics + Gen AI
6 months · 18 weeks · 6 modules
- 01Foundation of Data Analytics
- 02Excel & SQL Mastery
- 03Python for Data Analysis
- 04Data Visualisation & Reporting
- 05Statistics & Business Storytelling
- 06Capstone & Career Preparation
02
Business Analytics + Gen AI
6 months · 18 weeks · 5 modules
- 01Excel & Business Fundamentals
- 02Data Visualisation Tools
- 03Programming for Analytics
- 04Statistics & Analytics Theory
- 05Capstone & Career Preparation
03
Data Science & AI
12 months · 36 weeks · 8 modules
- 01Python & Mathematics Foundation
- 02SQL & Data Analysis
- 03Machine Learning Fundamentals
- 04Advanced Machine Learning
- 05Deep Learning
- 06Computer Vision & NLP
- 07MLOps & Deployment
- 08Capstone & Career Preparation
04
Generative AI
4 months · 16 weeks · 5 modules
- 01Generative AI Foundations
- 02Prompt Engineering
- 03LLM Application Development
- 04RAG & Vector Databases
- 05Advanced Generative AI Applications
Common two-course dilemmas
If you are stuck between two, compare the centre of gravity.
These are the comparisons that matter more than whether both courses happen to mention Python, SQL or AI.
Analytics vs Business Analytics
“Do I want to work closer to the data, or closer to the decision?”
Data Analytics
VSPrioritise querying, analysis, dashboards, reporting and explaining patterns.
Business Analytics
Prioritise KPI framing, commercial context, diagnosis and recommendations.
Analytics vs Data Science
“Do I want to explain what happened, or build models for what may happen next?”
Data Analytics
VSFaster route toward analysis and BI-oriented work with moderate coding depth.
Data Science & AI
Longer technical route into modelling, deeper mathematics and ML systems.
Data Science vs Generative AI
“Do I want the broad ML foundation, or a focused LLM application path?”
Data Science & AI
VSBroader classical ML, deep learning, CV/NLP and deployment foundations.
Generative AI
Focused on LLMs, prompting, retrieval, tool use and GenAI applications.
Business Analytics vs Generative AI
“Do I want AI to improve decisions, or do I want to build the AI application itself?”
Business Analytics
VSUse analytics and AI as decision-support tools inside business functions.
Generative AI
Build AI-enabled products, assistants and application workflows.
Comparison FAQs
Questions that can help you choose.
Compare the work you want to do, the depth you are ready for and the support you need—not just the tools listed in the curriculum.
Browse all program pagesNo. A longer technical program only makes sense if the target role requires that depth. Someone targeting BI or business analysis may get more value from a focused analytics pathway than from studying deep learning simply because it sounds more advanced.
Data Analytics + Gen AI and Business Analytics + Gen AI are usually the first two worth comparing. Choose between them based on whether you want to spend more time working directly with data and reporting, or closer to business framing, KPIs and recommendations.
Yes. Analytics experience can create useful foundations in data handling, SQL and business problem framing. Moving into Data Science still requires a meaningful step up in Python, mathematics, modelling and experimentation.
No. Generative AI is a focused application domain built around modern language-model systems. Data Science & AI covers a broader machine-learning foundation, including advanced ML, deep learning, computer vision, NLP and MLOps.
Yes. Data Analytics + Gen AI and Business Analytics + Gen AI are ₹1,10,000 + GST each; Data Science & AI is ₹1,40,000 + GST; and Generative AI is ₹90,000 + GST. All four programs offer up to 12 zero-interest EMIs.
Every program is delivered live online in a cohort of no more than 20 learners, with weekly one-to-one mentoring and written feedback. Learners also receive class recordings for 12 months after completion, lifetime access to learning materials, the option to repeat a batch for up to five years and five years of doubt support.
Yes. After meeting the program requirements, learners receive a Certificate of Completion in their specific program, issued and digitally verifiable by Skillsbiz Education.
Need help choosing?
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