Week-by-week schedule
You pick one track and follow it start to finish. Below is the six-week arc for each of the four tracks B.Tech students choose most often. The 45-day and 6-week programs run this schedule as it stands; the 2-month track adds a second project module, and the 6-month track repeats the cycle across four projects with deeper review. Other domains — AI and ML, Android, data analytics, PHP and WordPress, graphic design and cybersecurity basics — follow the same weekly rhythm.
Your track and project are fixed on day one. Semantic HTML, the box model, Flexbox and CSS Grid, then the first page of your actual project is built — not a tutorial page. Repository created, first commit pushed the same week.
Variables, arrays, objects, functions and ES6 syntax, then DOM selection, events, form validation and fetch. You wire the pages from week one into something that responds instead of just sitting there.
JSX, components, props, state and hooks, list rendering, conditional UI and React Router. The project front end is rebuilt properly in React, component by component, with the mentor reviewing structure.
Node fundamentals, Express routing, middleware, request and response handling, and your own REST endpoints. MongoDB schemas designed first on paper, then in Mongoose, then connected to the front end.
Login, signup, JWT tokens and protected routes. Full create-read-update-delete on your main entity, error states, and a proper test pass on every screen before anything is called finished.
The application goes live on a real host, environment variables handled correctly, README written, screenshots captured, weekly log compiled into the training report, and the project demonstrated to your mentor.
Syntax, data types, conditionals, loops, functions and error handling, all practised on small scripts. Git and GitHub set up in the same week so every exercise is committed from the start.
File and CSV handling, modules, virtual environments, classes and objects. You write two working automation scripts — the kind that rename a folder of files or pull data from a sheet on a schedule.
Arrays, Series and DataFrames, then the unglamorous part: missing values, duplicate rows, wrong data types and inconsistent labels in a real dataset. Cleaning is where data work actually happens.
MySQL queries, joins and aggregations, then Matplotlib and Seaborn charts. You stop producing charts that look nice and start producing charts that answer the question that was asked.
Train-test split, regression and classification with scikit-learn, accuracy, precision and recall explained in plain terms, and one prediction model trained end to end on your own dataset.
The model is wrapped in a Flask or Streamlit interface, results are documented, the training report is assembled, and you rehearse defending every choice — why that algorithm, why those features.
Command line navigation, file permissions, users, processes and shell scripting, plus the networking a fresher is expected to know — IP, DNS, ports, HTTP and SSH. Git and GitHub set up alongside.
EC2 instances launched and connected to, S3 buckets and policies, IAM users and roles done properly, security groups and a basic VPC. You run a small application on your own instance by the end of the week.
Images versus containers, writing a Dockerfile, layers and caching, volumes and networks, and Docker Compose for a multi-container setup. Your project from week two is containerised and runs anywhere.
Build, test and deploy stages explained, then a working pipeline in GitHub Actions or Jenkins that runs on every push. Broken builds are deliberately introduced so you learn to read pipeline logs.
Pods, deployments and services at an introductory level, plus logging and monitoring — what to watch, what to alert on, and how to find the cause when something falls over at 2am.
Everything is joined into one flow: code pushed, image built, tests run, container deployed. Architecture diagram drawn for the report, documentation finished, and the pipeline demonstrated live.
A real WordPress site set up end to end — domain, hosting, theme, pages and structure — followed by keyword research with search volume and intent. Your training project is a live site, not a mock-up.
On-page SEO, title and meta writing, internal linking, site speed, indexing and sitemaps, with Google Search Console and GA4 connected so you can see what your changes actually did to traffic.
Campaign structure, keyword match types, ad copy, quality score, budgets and bidding. A search campaign is built and run on a small real budget, then optimised on the data it produces.
Audience targeting, creative testing and retargeting on Meta, a content and social calendar, and a final performance report that ties spend to leads. This track ends with numbers you can defend in a viva.