Python Training Digital Lync

What you leave with

Four things every Python Training 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.

01

Agent-Ready skills

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

02

A shipped capstone

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

03

Verifiable credential

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

04

Direct placement pipeline

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

3 months, four phases

From "loads CSVs" to ships AI-native data pipelines.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover Power BI and data storytelling. Weeks 8–10 move into PySpark, Databricks, and Fabric. Weeks 11–12 ship Generative + Agentic AI data agents.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for Python Training

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

YOU SHIPA Python + SQL ingestion and transformation workflow over production-like datasets.

WEEKS 4–7 · ANALYTICS

Power BI and business intelligence

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

YOU SHIPA complete Power BI reporting suite consumed by business teams and leaders.

WEEKS 8–10 · DATA PLATFORM

PySpark, Databricks, and Microsoft Fabric

Spark DataFrames, joins, windows, optimization
Databricks workflows, Delta Lake, Unity Catalog
Fabric OneLake, Lakehouse, Warehouse, RTI
Streaming, orchestration, and governance

YOU SHIPAn enterprise-grade ELT platform with scheduled jobs, observability, and governed data products.

WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

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

Partner orgs (2026)48

Capstones deployed280+

→ Placement offers82%

Course curriculum

Seven sections. 65+ modules. The AI-native Python Training stack.

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

01

Fundamentals of IT & AI

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

10 MODULES
WEEK 1

1. What is an Application? 2. Types of Applications 3. Web Application Fundamentals 4. Web Technologies: (List key technologies and their roles) Frontend: HTML, CSS, JavaScript, React Backend: Python, Java, Node.js Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB). 5. Software Development Life Cycle (SDLC) Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance. 6. Application Development Methodologies Agile: Core principles, Scrum, Kanban Waterfall

1. What is Data 2. Types of Data 3. Data Storage 4. Data Analysis 5. Data Engineering 6. Data Science

1. The Importance of Computing Power 2. Key Computing Technologies: CPU (Central Processing Unit) GPU (Graphics Processing Unit) 3. Cloud Computing: What is the Cloud? Cloud Service Models: IaaS (Infrastructure as a Service) PaaS (Platform as a Service) SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)? 2. How AI Works? 3. Key Concepts: Machine Learning (ML) Deep Learning (DL) 4. Generative AI: What is Generative AI? Examples: Large Language Models (LLMs), image generation models. 5. AI in Everyday Learning

1. Customer Relationship Management (CRM) 2. Human Resource Management Systems (HRMS) 3. Retail & E-Commerce 4. Healthcare

02

Basic Python

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

10 MODULES
WEEKS 2–4

1. Python's applicability across various domains 2. Installation, environment setup, and path configuration 3. Writing and executing the first Python script

1. Keywords, Identifiers, and basic syntax 2. Variables, Data Types, and Operators 3. Introduction to Input/Output operations

Control Structures and Functions

1. String operations and manipulations 2. Lists and their operations 3. Introduction to Tuples and Sets

1. Detailed exploration of Dictionaries 2. Frozen Sets and their use-cases 3. Advanced list comprehensions

03

Advanced Python

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

15 MODULES
WEEKS 5–8

1. Advanced methods in Lists, Tuples, and Dictionaries 2. Sets, Frozen Sets, and operations 3. Comprehensive look into Python Collections

1. Exploring types of Functions and Arguments 2. Lambda functions and their applications 3. Map, Reduce, and Filter functions

1. File operations and handling different file formats 2. Working with Excel and CSV files in Python 3. Understanding and using Python Modules and Packages

1. Deep dive into Classes, Objects, and Methods 2. Constructors, Destructors, and Types of Methods 3. Inheritance, Polymorphism, and Encapsulation

1. Exception Handling: Try, Except, Finally 2. Creating and using Custom Exceptions 3. Utilizing Regular Expressions for pattern matching

04

Django Python Framework

Production Python Training : Power BI for analytics, then Microsoft Fabric — OneLake, Lakehouse medallion architecture, Spark, real-time intelligence, and Copilot.

25 MODULES
WEEKS 9–14

1. Introduction to Django and its features 2. Setting up a Django project and understanding its structure 3. MVC Model, creating views, and URL mapping

1. Database models and migrations 2. Admin interface and deploying Django applications 3. Forms and handling user inputs

1. Advanced URL routing and views 2. Class-based views and middleware 3. Working with static and media files

1. Building RESTful APIs with Django REST Framework 2. Serializers and request handling 3. Authentication and permissions in APIs

1. Writing tests for Django applications 2. Deployment strategies and best practices 3. Configuring Django applications for production

05

Python for Data Science

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

5 MODULES
WEEK 15

1. Introduction to Data Science with Python 2. Data manipulation with Pandas 3. Data visualization with Matplotlib and Seaborn

1. Advanced Pandas techniques and operations 2. Time Series data analysis with Pandas 3. Combining, merging, and reshaping data frames

1. Advanced visualization with Matplotlib 2. Interactive visualizations with Plotly 3. Geospatial data visualization

1. Basics of machine learning with Python 2. Using Scikit-learn for machine learning models 3. Model evaluation and validation techniques

1. Introduction to Neural Networks and Deep Learning 2. Working with text data and Natural Language Processing (NLP) 3. Introduction to Big Data technologies with Python

06

Cloud & DevOps For Python

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

5 MODULES
WEEK 15

1. Cloud Computing Basics Understanding cloud computing: Definitions, service models (IaaS, PaaS, SaaS), and deployment models (public, private, hybrid, multicloud). 2. Cloud Service Providers Overview Introduction to major cloud platforms (e.g., AWS, Azure, Google Cloud), focusing on their core services relevant to developers. 3. Cloud-based Development Environments Setting up and utilizing cloud-based IDEs and development tools to streamline development workflows. 4. Deploying Applications on the Cloud Basic concepts of application deployment on the cloud, including containerization basics with Docker and initial orchestration concepts.

Topics: 1. Understanding DevOps The philosophy, practices, and benefits of DevOps in modern software development, emphasizing collaboration, automation, and integration. 2. Version Control with Git Deep dive into using Git for source code management, including best practices for branches, commits, merges, and pull requests. 3. Continuous Integration/Continuous Deployment (CI/CD) Introduction to CI/CD pipelines, overview of tools ( GitHub Actions), and setting up basic pipelines for automated testing and deployment. 4. Monitoring and Feedback Basics of application monitoring, log management, and utilizing feedback for continuous improvement.

Topics: 1. Containers and Docker Introduction to containers, Docker fundamentals, creating Docker images, and container management basics. 2. Managing Application Infrastructure Basic strategies for managing infrastructure as part of the application lifecycle, including introduction to infrastructure as code (IaC) principles.

1. Scalable Application Design Principles of designing scalable applications that can grow with user demand, focusing on microservices architecture and stateless application design. 2. Cloud-native Services for Developers Leveraging cloud-native services (e.g., AWS Lambda, Azure Functions, Google Cloud Run) for building and deploying applications. 3. Databases in the Cloud Overview of cloud database services (SQL and NoSQL) and integrating them into web applications. 4. Security Basics in Cloud and DevOps Understanding security best practices in cloud environments and throughout the DevOps pipeline.

Topics: 1. Agile and Scrum Methodologies Incorporating Agile and Scrum practices into team collaboration for efficient project management. 2. Code Review and Collaboration Tools Utilizing code review processes and collaboration tools GitHub, to enhance code quality and team productivity. 3. Automation in Development Exploring automation beyond CI/CD, including automated testing frameworks, database migrations, and environment setup. 4. DevOps Culture and Best Practices Cultivating a DevOps culture within teams, embracing continuous learning, and adopting industry best practices for sustainable development.

06

Gen AI & AI Agents

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

5 MODULES
WEEK 15

Introduction to Generative AI 1. What is Generative AI? 2. Key Applications: Text (ChatGPT, Claude, LLaMA) Images (DALL·E, MidJourney, Stable Diffusion) Audio (Music Generation, Voice Cloning) Code (GitHub Copilot, Cursor) 3. Evolution of GenAI: Rule-Based → Deep Learning → Transformers GANs vs. VAEs vs. LLMs

1. Effective Prompt Design Instruction-Based, Few-Shot, Zero-Shot 2. Advanced Techniques: Chain-of-Thought (CoT) Prompting Self-Consistency & Iterative Refinement Hands-on: Optimizing prompts for GPT-4, Claude, LLaMA

Transformer Architecture 1. Why Transformers? (Limitations of RNNs/LSTMs) 2. Key Components: Self-Attention & Multi-Head Attention Encoder-Decoder (BERT vs. GPT) 3. Evolution: BERT → GPT → T5 → Mixture of Experts 4. Large Language Models (LLMs) 5. Pre-training vs. Fine-tuning 6. Popular Architectures: GPT-4, Claude, Gemini, LLaMA 3 BERT (Encoder-based) vs. T5 (Text-to-Text)

Introduction to AI Agents 1. What are AI Agents? 2. vs. Traditional AI: 3. Applications: AI Agent Frameworks 1. CrewAI (Multi-Agent Collaboration): 2. n8n (Workflow Automation):

Designing AI Agents CrewAI + n8n: Automating Business Workflows Multi-Agent Systems: Collaboration & Specialization Real-World Applications Case Studies: AI Customer Support Agents

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 Python Training agent in a real partner org.

Pick a real partner data problem. Deploy a production data pipeline and 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.

AS

Aarav Sharma

Lead Instructor · Python Training & 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 Python Training 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 + Python Training ), 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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Python Training
+ AI Agents

Four things every Python Training grad walks away with.

From "loads CSVs" to ships AI-native data pipelines.

Python + SQL for Python Training

Power BI and business intelligence

PySpark, Databricks, and Microsoft Fabric

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Seven sections. 65+ modules. The AI-native Python Training stack.

Fundamentals of IT & AI

Basic Python

Advanced Python

Django Python Framework

Python for Data Science

Cloud & DevOps For Python

Gen AI & AI Agents

40+ tools, one production capstone.

You don't watch videos. You ship software.

LMS analytics platform

HRMS data pipeline

CRM intelligence stream

Your AI Python Training 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

Python Training + AI Agents

Four things every Python Training grad walks away with.

From "loads CSVs" to ships AI-native data pipelines.

Python + SQL for Python Training

Power BI and business intelligence

PySpark, Databricks, and Microsoft Fabric

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Seven sections. 65+ modules. The AI-native Python Training stack.

Fundamentals of IT & AI

Basic Python

Advanced Python

Django Python Framework

Python for Data Science

Cloud & DevOps For Python

Gen AI & AI Agents

40+ tools, one production capstone.

You don't watch videos. You ship software.

LMS analytics platform

HRMS data pipeline

CRM intelligence stream

Your AI Python Training 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

Python Training
+ AI Agents

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