Digital Lync

What you leave with

Four things every Data Science grad walks away with.

Most programs stop at tools. Ours makes you ship pipelines, platforms, and AI-powered data products that hiring teams can verify.

Agent-Ready skills

Build, deploy, and monitor AI agents that run production workflows — not chatbot toys.

A shipped capstone

A live React + FastAPI + LangGraph app on Kubernetes, monitored, observable, public URL.

Verifiable credential

2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check.

Direct placement pipeline

GitHub + LinkedIn rewrite, resume rebuild, and warm intros to our 1,000+ hiring partners.

3 months, four phases

From "opens notebooks" to ships AI-native data science systems.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover BI and analytics storytelling. Weeks 8–10 move into PySpark, Databricks, and Microsoft Fabric Data Science. Weeks 11–12 ship Generative + Agentic AI solutions.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for data science

Python data structures, iterators, OOP
PostgreSQL querying, joins, windows, CTEs
Database design, indexing, optimization
Data file formats and transformation patterns

YOU SHIPA Python + SQL analytics workflow over production-like datasets, ready for feature and model development.

WEEKS 4–7 · ANALYTICS

Power BI and analytics storytelling

Power Query and source integrations
Star schema modeling and DAX measures
Advanced visuals, storytelling, KPI dashboards
Publishing, sharing, governance, refresh

YOU SHIPA complete analytics dashboard suite with KPI narratives and actionable insights for business teams.

WEEKS 8–10 · DATA SCIENCE PLATFORM

PySpark, Databricks, Fabric, and model workflows

Spark DataFrames, feature engineering, optimization
Databricks workflows, Delta Lake, Unity Catalog
Fabric OneLake, Lakehouse, Data Science, MLflow
Model scoring, monitoring, and governance

YOU SHIPAn enterprise-grade data science platform with tracked experiments, governed data products, and reproducible workflows.

WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analysis, prediction workflows, and decision support.

Use LLM APIs, LangChain, RAG, and LangGraph workflows with persistence and HITL. Add MCP tool access and enterprise guardrails. Your capstone connects analytics pipelines, model outputs, and AI agents into a production-ready decision intelligence system.

Partner orgs (2026)48

Capstones deployed280+

→ Placement offers82%

Course curriculum

Seven sections. 65+ modules. The AI-native data science stack.

Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.

Python for AI & Data

How modern apps work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.

5 MODULES
WEEK 1

Application types & real-world purpose

Web app fundamentals & client-server architecture

Frontend / backend / database tiers

SDLC: planning → testing → deployment

Waterfall vs Agile mindset

Scrum roles, events, artifacts

User stories, epics, estimation

Backlog management on a real board

STACKAzure BoardsJira

Computing power in the AI era

CPU vs GPU — when each matters

Cloud computing intro: IaaS, PaaS, SaaS

Data as the fuel for AI

What is AI? How AI works

ML & Deep Learning fundamentals

LLMs & image generation models

Agentic AI: autonomous AI systems

CRM with AI

HRMS powered by AI

Retail, e-commerce, healthcare AI

AI in DevOps, Data, FullStack

SQL for AI & Data

Modern React with hooks, Redux Toolkit and routing, paired with PostgreSQL fundamentals through query optimization.

10 MODULES
WEEKS 2–4

Component-based architecture

Vite / create-react-app setup

JSX, functional vs class components

STACKReact 18ViteNode.js

Props & PropTypes

useState, controlled components

Form handling with state

useEffect, dependency arrays, cleanup

useRef, useMemo, useCallback

useReducer & custom hooks

CSS Modules & Styled-Components

Context API for global state

React Router v6, nested & protected routes

HOCs & composition

Redux Toolkit + async thunks

Axios, error boundaries, code splitting

Deployment to Vercel / Netlify

STACKRedux ToolkitAxiosVercel

DBMS/RDBMS & ACID

psql & pgAdmin 4 setup

Data types, constraints, referential integrity

SELECT, WHERE, ORDER BY, LIMIT

Aggregates, GROUP BY, window functions

All JOIN types

Subqueries, CTEs, recursive CTEs

UNION, INTERSECT, EXCEPT

Transactions & isolation levels

Indexes (B-tree, Hash, GIN, GiST)

Views & materialized views

Stored functions & PL/pgSQL

Triggers & audit logging

ER modeling & normalization

EXPLAIN ANALYZE & query optimization

Connection pooling & partitioning

Power BI for Data Analysis

Python from fundamentals through OOP, then FastAPI — async APIs with Pydantic validation, SQLAlchemy, and JWT auth.

15 MODULES
WEEKS 5–8

Setup, VS Code, syntax & keywords

Variables, types, type casting

Conditionals & loops

Indexing & slicing

f-strings & formatting

Search, replace, split, join

CRUD & comprehensions

Tuples: immutability, packing

Dict access & nested dicts

Sets: union, intersection, difference

Frozen sets & hashability

Counter, namedtuple, defaultdict, deque

Generators & yield

map, filter, reduce

*args, **kwargs

Local, global, non-local scope

Lambdas & recursion

Built-in vs external modules

pip & requirements.txt

requests, pandas, numpy

File I/O, os, shutil

CSV: DictReader / DictWriter

JSON serialization

Exceptions & custom exceptions

Decorators (function & class)

Context managers

Encapsulation, inheritance, abstraction, polymorphism

Static & class methods

Magic methods (__str__, __repr__)

Flask vs Django vs FastAPI

Async, Pydantic, auto-docs

Path operations: GET, POST, PUT, DELETE

STACKFastAPIUvicornPydantic

Path & query parameters

Pydantic BaseModel validation

Nested models & response models

SQLAlchemy session management

Pydantic ↔ SQLAlchemy mapping

Alembic migrations

Relationships: 1:M & M:M

Modular APIRouter, prefixes & tags

HTTPExceptions & custom handlers

Repository pattern & DI

Env vars & CORS

Bcrypt password hashing

OAuth2 password flow + JWT

Route protection & RBAC

Postman & Thunder Client testing

STACKJWTOAuth2Bcrypt

PySpark

Production data science workflows: Power BI for analytics, then Microsoft Fabric for OneLake, Lakehouse architecture, ML operations, real-time intelligence, and Copilot.

25 MODULES
WEEKS 9–14

BI fundamentals & modern analytics

Power BI components & architecture

Desktop vs Service capabilities

File, DB, cloud, web sources

Import vs DirectQuery vs Live

Credential management

Power Query interface & applied steps

Profiling, filtering, splitting, merging

Pivot & unpivot

Star vs snowflake schema

Relationships & hierarchies

Date dimensions

Visualization principles

Slicers, bookmarks, drill-through

Mobile optimization & storytelling

Calculated columns vs measures

Aggregation, logical, text, date functions

CALCULATE & FILTER

YTD, MTD, QTD, growth rates

Variables & iterator functions

DAX performance optimization

Workloads: Data Factory, Warehouse, Science

Capacity Units (F2–F2048)

Workspaces & tenant structure

STACKMS FabricOneLake

Delta Lake & Parquet formats

ACID transactions & versioning

OneLake shortcuts

Bronze → Silver → Gold

SQL analytics endpoint

Time travel & metadata

Pipelines, connectors, Dataflows Gen2

M language fundamentals

Database mirroring (Azure SQL, Cosmos)

Spark in Fabric, notebooks & Copilot

PySpark DataFrames & Spark SQL

AI functions in Spark

Eventstreams & streaming sources

KQL & Eventhouses

Real-time dashboards & alerting

Copilot for SQL, KQL, pipelines, reports

Conversational data agents

Azure AI Foundry integration

User Data Functions: serverless Python

Microsoft Purview: lineage & catalog

Git integration & CI/CD

Data mesh patterns

Databricks

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.

5 MODULES
WEEK 15

Set theory, logical operations, functions

Vectors: dot product & magnitude

Matrices: inverse, determinant

Eigenvalues, eigenvectors, PCA intuition

Sample spaces & axioms

Conditional probability & Bayes' theorem

Random variables, PMF, PDF

Monte Carlo simulations

Discrete: Binomial, Poisson, Geometric

Continuous: Normal, Exponential, Uniform

CLT & sampling distributions

Mean, median, std dev, IQR

Outlier detection: histograms, boxplots

Hypothesis testing: p-values, t-tests, ANOVA

Confidence intervals, Type I/II errors

Covariance & correlation

Linear & logistic regression

Cost functions: MSE, cross-entropy

Gradient descent

Microsoft Fabric

A complete ML curriculum: from regression and ensembles to SVM, unsupervised methods, and deployment with FastAPI and drift monitoring.

10 MODULES
WEEKS 16–18

AI vs ML vs DL — the landscape

Supervised, unsupervised, RL

Setup: Python, Jupyter, Scikit-learn

STACKScikit-learnJupyter

Loss functions: MSE, MAE, cross-entropy

Gradient descent variants

Adam, RMSprop, AdaGrad

CRISP-DM workflow

EDA, missing data, outliers

Feature engineering & cross-validation

Simple & multiple regression (OLS)

R², adjusted R², RMSE, MAE

Multicollinearity & VIF

Bias-variance tradeoff

Ridge, Lasso, Elastic Net

GridSearchCV, RandomizedSearchCV

Logistic regression & softmax

Naive Bayes variants

Laplace smoothing

Decision trees: entropy & Gini

Random Forest & OOB error

XGBoost, LightGBM, CatBoost

STACKXGBoostLightGBM

SVM & kernel trick

Precision, recall, F1, confusion matrix

ROC, AUC, SMOTE

K-Means, hierarchical, DBSCAN

PCA & t-SNE

Curse of dimensionality

RL: agents, MDP, Q-Learning

Prediction APIs with FastAPI

Drift detection & monitoring

Responsible AI & bias mitigation

Generative & Agentic AI

Neural networks from scratch in NumPy, then PyTorch and TensorFlow — CNNs for vision, RNNs/LSTMs for sequence, and the full NLP pipeline through seq2seq.

10 MODULES
WEEKS 19–22

ANNs, perceptrons, activations

Forward & back propagation

SGD, Adam, RMSprop

Building ANNs from scratch in NumPy

Tensors, graphs, autograd

nn.Module & data loading

Training loops & checkpointing

GPU acceleration

STACKPyTorchTensorFlow

Convolution, filters, strides, padding

LeNet, AlexNet, VGG

Visualizing features

ResNet, Inception, DenseNet

Batch norm, dropout, transfer learning

Object detection & segmentation

Sequential data & vanilla RNN

Vanishing gradient & BPTT

LSTM gates & GRUs

Bidirectional & stacked RNNs

Seq2Seq encoder-decoder

Teacher forcing

Tokenization, BoW, TF-IDF

Word2Vec, GloVe, FastText

Language modeling & metrics

CNN, RNN, LSTM pipelines for text

Sentiment analysis

Imbalanced text datasets

Neural machine translation

Beam search & decoding

BLEU & ROUGE

NER & POS tagging

BiLSTM-CRF architectures

Domain-specific NER

Generative & Agentic AI

The frontier 2026 stack: LangChain 1.0, LangGraph workflows, RAG, Model Context Protocol, persistence, HITL, and multi-agent systems with A2A.

10 MODULES
WEEKS 23–27

LLM fundamentals & transformers

GPT, Claude, Gemini, DeepSeek

Tokenization & cost optimization

2026 frontier model evolution

Zero-shot, few-shot, CoT prompting

Reducing hallucinations

Multimodal prompting

OpenAI, Anthropic, Google, DeepSeek APIs

create_agent abstractions

Streaming, function calling, structured outputs

Multi-provider middleware

STACKLangChain 1.0OpenAIAnthropic

ChromaDB, Pinecone, Qdrant

Hybrid search & agentic RAG

MCP-enhanced retrieval

Self-improving retrieval

Streamlit & Gradio interfaces

EU AI Act compliance

API security & rate limiting

Plan, Reason, Act fundamentals

Model Context Protocol (MCP)

Enterprise agent architecture

STACKMCPLangChain 1.0

Graph-based logic & architecture

State management & node caching

Pre/post hooks for guardrails

STACKLangGraph 1.0

Parallel execution & conditional routing

Iterative refinement loops

Decision trees & multi-stage approval

PostgreSQL & Redis state

HITL multi-day workflows

Audit trails & compliance

Multi-agent systems & Google A2A

LangSmith observability

MCP security & prompt injection

Agent guardrails

STACKLangSmithA2A

Cloud, Testing & AI Ops

Production-grade ops for the agentic era: Linux, Git, CI/CD, Docker, Kubernetes, Terraform, Prometheus, Grafana, and AI Ops with LangSmith and MLflow.

9 MODULES
WEEKS 28–32

DevOps culture & the Dev-Ops gap

CI/CD, IaC, collaboration

Server & networking essentials

IaaS, PaaS, SaaS overview

Linux architecture & navigation

Permissions & processes

Shell scripting: loops, functions

Cron jobs & log management

Branching, merging, rebasing

Pull request workflows

GitHub Actions basics

STACKGitGitHub

Build, test, deploy stages

GitHub Actions workflows

CI/CD for AI/ML training

Blue-green & canary deployments

Images, containers, registries

Multi-stage Dockerfiles

Docker Compose

Containerizing FastAPI & AI services

STACKDockerCompose

Master vs worker nodes

Pods, Deployments, Services, Ingress

Scaling & rolling updates

Persistent volumes for AI workloads

STACKKubernetesEKS

IaC concepts & Terraform CLI

Providers, resources, variables, state

Modules for reusable infra

STACKTerraform

Metrics, logs, traces

Prometheus, exporters, PromQL

Grafana for AI model latency

Alerting rules

STACKPrometheusGrafana

Model drift & data drift monitoring

LangSmith & MLflow observability

Bias detection & compliance

GPU optimization & cost control

STACKLangSmithMLflow

Tools you'll master

40+ tools, one production capstone.

Not a shallow tour. You'll use every one of these in at least one graded exercise.

Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the capstone, you've built the whole stack around them.

Hero project · weeks 3–12

LMS analytics platform

Ingest learner events, build transformation layers, and publish executive and academic dashboards with AI-generated insight summaries.

PySparkDatabricksPower BILangGraphPostgreSQL

View project →

Enterprise · weeks 6–11

HRMS data pipeline

Build secure ETL workflows for employee, payroll, and performance datasets with governed semantic models and decision-ready KPIs.

MS FabricDelta LakePower BIUnity Catalog

Real-time · weeks 8–12

CRM intelligence stream

Create near real-time customer analytics with streaming events, automated anomaly flags, and AI-assisted executive reporting.

Structured StreamingKQLPower BILangChain

Capstone · weeks 11–12

Your AI data science agent in a real partner org.

Pick a real partner data problem. Deploy production analytics and model workflows with an AI agent that explains metrics, detects risks, and accelerates business decisions.

2026: 220+ deployed76% → placement offers

See capstone gallery →

Your instructor

Taught by engineers who shipped agentic AI to production.

Not a career trainer. A practitioner who still ships code.

Aarav Sharma

Lead Instructor · Data Science & AI

React · FastAPI · PyTorch · LangChain

"A 2026 full-stack engineer doesn't stop at React + an API. They train the model, deploy it behind FastAPI, wrap it in an agent, and ship the whole thing to a real org. That's what we build, every cohort."

10 yrs

FULL STACK

2,400+

LEARNERS

4.9 /5

RATING

Aarav started as a React engineer at an Indian unicorn before leading platform teams across three continents. He's shipped React + FastAPI products for a healthcare network with 80M users, trained NLP classifiers in production for a top-3 bank, and — most recently — deployed the first LangGraph agent into a Fortune-500 insurer's claims pipeline.

His cohorts get two things other programs don't give you: a real engineer who still ships code, and a curriculum rewritten every quarter to match what hiring managers actually ask about.

FAQ

Questions we actually get — answered honestly.

If the answer you need isn't here, book a 20-minute advisor call. No-slides, no-pitch — just your questions.

No. About 40% of our Data Science cohort comes from non-CS backgrounds — mechanical, electrical, and commerce. The first phase is foundations by design. What you need: consistency and around 12–15 hours/week.

Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours, and roughly 5 hours of asynchronous project work. Weekends are optional office hours with the TA team.

Yes. Every student gets a dedicated placement advisor from week 8 onwards — not a helpdesk. They review your resume, redo your LinkedIn, mock-interview you, and make direct warm introductions to our 1,000+ hiring partners. We track individual outcomes, not cohort averages.

Full refund within 7 days of cohort start, no questions. Pro-rata refund through week 4 if the program isn't working for you. We'd rather refund than have an unhappy alum.

You actually build. Sections 6 (ML), 7 (DL/NLP), and 8 (Generative + Agentic AI) are hands-on — you'll train classifiers, build a RAG pipeline, ship a LangGraph workflow, and deploy your capstone agent into a real partner org. Nothing in the AI track is theory-only.

You get the Agent-Ready 2026 credential, graded on a 1–5 band with a public verification URL. It's co-branded with our partner ecosystem (Salesforce Partner + ServiceNow), and it names the specific capstone artifact you deployed. Recruiters can verify in 10 seconds.

All three. On-campus at our Hyderabad flagship; online cohorts on IST and PST; weekend cohorts for working professionals. Every format ships the same three projects and the same capstone.

We'd rather pause your cohort than push you through. You can freeze your seat for up to 90 days and rejoin the next cohort without paying again. TAs run catch-up sessions every Saturday for anyone more than one week behind.

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.

Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real capstones from cohort 022.

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Data Science
+ AI Agents

Four things every Data Science grad walks away with.

From "opens notebooks" to ships AI-native data science systems.

Python + SQL for data science

Power BI and analytics storytelling

PySpark, Databricks, Fabric, and model workflows

Deploy AI agents that automate analysis, prediction workflows, and decision support.

Seven sections. 65+ modules. The AI-native data science stack.

Python for AI & Data

SQL for AI & Data

Power BI for Data Analysis

PySpark

Databricks

Microsoft Fabric

Generative & Agentic AI

Generative & Agentic AI

Cloud, Testing & AI Ops

40+ tools, one production capstone.

You don't watch videos. You ship software.

LMS analytics platform

HRMS data pipeline

CRM intelligence stream

Your AI data science agent in a real partner org.

Taught by engineers who shipped agentic AI to production.

Questions we actually get — answered honestly.

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.

Get Skilled

Data Science+ AI Agents

Four things every Data Science grad walks away with.

From "opens notebooks" to ships AI-native data science systems.

Python + SQL for data science

Power BI and analytics storytelling

PySpark, Databricks, Fabric, and model workflows

Deploy AI agents that automate analysis, prediction workflows, and decision support.

Seven sections. 65+ modules. The AI-native data science stack.

Python for AI & Data

SQL for AI & Data

Power BI for Data Analysis

PySpark

Databricks

Microsoft Fabric

Generative & Agentic AI

Generative & Agentic AI

Cloud, Testing & AI Ops

40+ tools, one production capstone.

You don't watch videos. You ship software.

LMS analytics platform

HRMS data pipeline

CRM intelligence stream

Your AI data science agent in a real partner org.

Taught by engineers who shipped agentic AI to production.

Questions we actually get — answered honestly.

Cohort 014 starts 14 May 2026.40 seats. 12 already claimed.

Get Skilled

Data Science
+ AI Agents

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.