Nine tracks. Different ways to solve a problem.
Each track is described by the work you would do and the first roles it prepares for, not by a list of videos. Compare them, or enquire and the team will suggest one on the call.
Find the questions you want to work on.
AI and Generative AI
Learn AI tools, prompt engineering, chatbots, AI assistants, automation workflows, and business-focused AI use cases.
Explore the trackMachine Learning
Learn how machines learn from data and how models are built, trained, tested, and evaluated.
Explore the trackData Analytics
Learn how to convert raw data into clear dashboards, reports, and business insights.
Explore the trackData Science
Learn how to explore, clean, analyse, visualise, and present data through practical data projects and case studies.
Explore the trackCloud Computing
Learn cloud fundamentals and understand how modern digital systems are hosted, stored, deployed, secured, and managed.
Explore the trackDevOps
Learn how technology teams manage deployment, automation, releases, infrastructure workflows, and system reliability.
Explore the trackCybersecurity
Learn cybersecurity fundamentals, web security concepts, responsible testing practices, security checklist preparation, and audit reporting.
Explore the trackAI Automation
Learn how AI tools, prompt workflows, chatbots, AI assistants, and automation systems can improve business processes and productivity.
Explore the trackBusiness Analytics
Learn business KPIs, reports, dashboards, performance analysis, and business decision-making.
Explore the trackCompare the work, the preparation and the first roles.
Preparation guides, not admission requirements. Level 1 has no prerequisite.
| Track | Typical work | Coding | Mathematics | Realistic first roles |
|---|---|---|---|---|
| AI and Generative AI | Build applications on top of AI tools and language models, and test what they produce. | Start with tool-based exercises; integrations need programming. | Basic numeracy and careful evaluation to start. | AI and machine-learning postings are the fastest-growing category in India's white-collar hiring reports, but the senior titles (ML Engineer, AI Engineer) expect experience. The realistic first roles are analyst, associate and support roles that use AI tools, where a tested project and clear writing stand out. |
| Machine Learning | Train, evaluate and ship models with proper validation. | Python practice is central to model-building work. | Algebra, probability and introductory statistics. | Machine Learning Engineer roles in India typically expect prior experience. The track prepares you for junior data and ML-adjacent roles first, where a correctly evaluated model and a clear write-up are what get noticed. |
| Data Analytics | Answer business questions with SQL, spreadsheets and dashboards. | Spreadsheets to start; SQL is valuable for querying data. | Percentages, averages, rates and distributions. | Data analyst, MIS and reporting roles are among the most accessible entry points in Indian tech hiring, and they are the first roles this track prepares for. Employers look for SQL, a BI tool and the habit of checking a number against its source. |
| Data Science | Build and test statistical and predictive models, then explain what they show. | Python and notebook-based analysis. | Probability, statistics and algebra, developed with practice. | Entry-level Data Scientist roles are rare in India; the title usually follows analyst experience. The track prepares you for analyst roles where statistical thinking and clear write-ups are valued, with modelling as the next step. |
| Cloud Computing | Run applications on cloud infrastructure with access control and cost awareness. | Command-line skills; scripting becomes useful with practice. | Basic numeracy for capacity, traffic and cost estimates. | Industry reports describe a wide cloud skills gap in India, but it is mostly for experienced engineers. Freshers start through cloud support and associate roles; hands-on proof of a deployed, documented, torn-down setup is what those roles look for. |
| DevOps | Make building, testing and deploying software repeatable. | Git, Linux and basic scripting; some application familiarity. | Basic numeracy for durations, failure rates and resource use. | DevOps Engineer is usually a second role in India, after a year or two in development, testing or operations. The track builds toward it honestly: your first roles are development, release support or infrastructure support, with a working pipeline as proof. |
| Cybersecurity | Find and fix weaknesses with authorised testing and clear evidence. | Networking and web basics first; scripting supports deeper work. | Basic numeracy and logical reasoning for introductory practice. | SOC analyst is the usual first role in Indian cybersecurity hiring. Industry reports note that automation is reducing some entry-level operational tasks, so clear evidence of authorised lab work and good reporting matters more, not less. |
| AI Automation | Connect tools into workflows that use AI and keep a person at the key decisions. | Visual workflows can be a starting point; APIs and custom logic add code. | Percentages, counts and basic performance measurement. | 'AI automation' is a skill more than a defined job title in Indian job posts. It makes operations, analyst and marketing hires more valuable, and it is how many professionals add AI to a role they already have. We do not promise a particular title. |
| Business Analytics | Turn requirements and reports into decisions for a business team. | Spreadsheet-led starting point; SQL can extend your analysis. | Ratios, percentages, unit economics and basic statistics. | Business analyst roles in India lean on communication and domain knowledge, and increasingly expect basic SQL and one BI tool. The overlap with data analyst roles is growing; the first roles are business analyst trainee, MIS and reporting associate. |
If you like this, look at that.
If you like answering a question with numbers and charts
Data Analytics →If you like deciding what a business should do next
Business Analytics →If you like trying AI tools and checking whether the answer is right
AI and Generative AI →If you like connecting tools so repetitive work runs itself
AI Automation →If you like coding and finding out why a model's result changed
Machine Learning →If you like framing a question carefully and explaining uncertainty
Data Science →If you like setting up systems and knowing who can reach what
Cloud Computing →If you like making builds and releases repeatable
DevOps →If you like finding how things break, with permission
Cybersecurity →
Six of the nine tracks involve data or AI and overlap. In short: Data Analytics answers questions with data; Business Analytics turns answers into decisions; Data Science frames the question and the statistics; Machine Learning builds and validates the model; AI builds applications on top of models; AI Automation connects tools around them. The comparison guides go deeper.
Read the track comparisonsHave a direction in mind?
Enquire about a track and the team will confirm the starting level with you. The Career Audit is optional.
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