The AI and data tools you will use at work, taught live.
From SQL, Spark and Airflow to Claude, LangGraph and n8n: every tool on a SkilD syllabus, the week you learn it, and how often Indian job ads ask for the main ones.
Taught live by industry experts from Mastercard and Paytm.
Book a 1-on-1 call with a program advisor
*Credits apply to partner tools and platforms only. Terms and conditions apply.
PostgreSQL
Python
Spark
Databricks
Snowflake
Airflow
Kafka
dbt
scikit-learn
XGBoost
PyTorch
Hugging Face
LlamaIndex
LangGraph
MCP
MLflow
ChatGPT
Claude
Gemini
NotebookLM
Power BI
n8n
KNIME
Lovable
Airtable
Cursor
GitHub CopilotClaude Code
pytest
GitHub Actions
The tools and skills in Indian job ads, and the program that teaches each.
- Python51%
- Spark46%
- SQL41%
Azure25%- Databricks17%
- Snowflake16%
AWS16%- Airflow13%
Naukri · 160 Data Engineer job ads · 8–9 Oct 2026
- Machine learning56%
- SQL32%
- Deep learning31%
- Generative AI19%
- Cloud14%
- AI agents13%
Naukri · 100 AI Engineer and ML Engineer job ads · 8–9 Oct 2026
- Generative AI6,532
- Agentic AI6,017
- Prompt engineering4,396
- ChatGPT3,082
AI automation1,812
Indeed India · listings that name each skill · 30 Sep 2026
Only skills the program teaches are shown. A Naukri ad carries about eight skill tags, so its shares are minimums. The AI Generalist card is a count of listings, not a share. In Data Engineering you learn one cloud, AWS, Azure or Google Cloud, by your track.
Every tool on the syllabus, in the order you meet it.
PG Program in Data Engineering · 34 weeks on weekends · See the program →
- Weeks 1–2AI Launchpad
Use the main AI assistants safely and well, check what they tell you, and understand how they work without the maths.
ChatGPTClaudeGeminiCopilotNotebookLMPerplexitySheets with AIan image and a video generator
- Weeks 3–4SQL for Data
Write the SQL that data and AI jobs and interviews ask for.
PostgreSQLMySQLDBeaver
- Weeks 5–6Python for Data
Write clean, tested Python that loads, cleans and reshapes messy data.
PythonpandasNumPypytestrequestsJupyterVS Code
- Week 7Linux, Git and an AI coding assistant
Work the way engineering teams do, with an AI assistant you keep in check.
bashcronGitGitHubCopilot, Cursor or Claude Code
- Week 8Project weekend 1 · Kirana Ledger, your first pipeline
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 9–10Data foundations and modelling
Decide how data is stored and shaped before you build anything.
ParquetAvrodbdiagramPostgreSQLMongoDB
- Weeks 11–13Cloud core on your track
Build a working pipeline on the cloud you choose, secure and within budget.
AWS: S3LambdaGlueAthenaRedshiftAzure: ADLSFunctionsMicrosoft Fabric (Lakehouse, Warehouse, Data Factory)GCP: Cloud StorageFunctionsDataflowBigQuery
- Week 14Project weekend 2 · Highway Toll Data Pipeline
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 15–17Spark and Databricks
Process large data with Spark and run it in production on Databricks.
PySparkDatabricksDelta LakeIcebergUnity CatalogEMR / Azure Databricks / Dataproc
- Weeks 18–19Snowflake and warehouse cost
Load, tune and control the cost of a cloud warehouse.
SnowflakeSnowsightSnowpipe
- Week 20Project weekend 3 · Festival Sales Spark Pipeline
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 21–22Pipelines on a schedule
Run pipelines on a schedule, in containers, with alerts when they fail.
AirflowDockerMWAA / Fabric Apache Airflow job / ComposerSlack
- Weeks 23–24Data in motion
Build pipelines that react to events as they happen.
KafkaSchema RegistryKinesis / Event Hubs / Pub/SubSpark Structured Streaming
- Week 25Project weekend 4 · Real-Time Payment Monitor
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 26–27Analytics engineering and data quality
Turn raw tables into tested, documented models the business can trust.
dbt Coredbt-utilsdbt-expectations
- Weeks 28–29Production, governance and DataOps
Keep pipelines running, governed and compliant once they are live.
GitHub ActionsTerraformUnity Catalog / Purview / DataplexCloudWatch / Azure Monitor
- Weeks 30–31The data layer under AI
Build the data pipelines that AI products and agents depend on.
pgvectorChromaDBLangChainMLflowBedrock / Azure OpenAI / Vertex AI
- Week 32Hiring week
Pass data engineering interviews and show your work.
ExcalidrawGitHub Pages
- Weeks 33–34The end-to-end project
Prove you can build and present a full data platform.
Your track’s full stack
PG Program · AI Engineer · 34 weeks on weekends · See the program →
- Weeks 1–2AI Launchpad
Use the main AI assistants safely and well, check what they tell you, and understand how they work without the maths.
ChatGPTClaudeGeminiCopilotNotebookLMPerplexitySheets with AIan image and a video generator
- Weeks 3–4SQL for Data
Write the SQL that data and AI jobs and interviews ask for.
PostgreSQLMySQLDBeaver
- Weeks 5–6Python for Data
Write clean, tested Python that loads, cleans and reshapes messy data.
PythonpandasNumPypytestrequestsJupyterVS Code
- Week 7Linux, Git and an AI coding assistant
Work the way engineering teams do, with an AI assistant you keep in check.
bashcronGitGitHubCopilot, Cursor or Claude Code
- Week 8Project weekend 1 · The No-Show Model, your first model
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 9–10Statistics and seeing the data
Understand data before modelling it, and say how sure you are of a result.
SciPystatsmodelsSeabornpandas
- Weeks 11–13Machine learning
Understand how the core models learn, build them, and choose between them by what a mistake costs.
NumPyscikit-learn
- Weeks 14–15Better models
Make models stronger, explain them, and forecast and recommend.
XGBoostLightGBMOptunaSHAPProphetimplicit
- Week 16Project weekend 2 · The Credit Call
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 17–18Neural networks and vision
Understand exactly how a neural network learns, and build image models.
NumPyPyTorchTensorFlow/KerastorchvisionColab GPU
- Weeks 19–20Sequences, attention and transformers
Understand how models read sequences, and why attention replaced the RNN.
PyTorchHugging Face TransformersspaCysentence-transformers
- Week 21Project weekend 3 · The Complaint Desk
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 22–24LLMs, RAG and evaluation
Understand how an LLM produces text, build applications on your own data, and prove they work.
PyTorchOpenAIAnthropicGemini APIsLlamaIndexChromaDBpgvectorRAGASLangfuse or LangSmithPEFT
- Weeks 25–27Agentic AI and AgentOps
Understand an agent as a loop, then build, secure and measure agents with a human in charge.
PythonLangGraphCrewAIMCPPydanticLangfuse
- Week 28Project weekend 4 · The Policy Navigator gets an agent
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 29–30From notebook to product
Ship a model or LLM app to the cloud, fast and at a known cost.
FastAPIDockerStreamlit or GradioMLflowGitHub Actionsone cloud: AWS (Bedrock, SageMaker) or Azure (Microsoft Foundry, formerly Azure AI Foundry)
- Week 31Running it in production
Keep models accurate and up to date once users arrive.
EvidentlyFeastLangfuseGrafanaDVC
- Week 32Hiring week
Pass AI engineering interviews and show your work.
ExcalidrawGitHub Pages
- Weeks 33–34The end-to-end project
Take an AI product from a brief to production, in your own domain.
By track: CrewAIFeastvLLMenterprise connectorsKubernetes
PG Program · AI Generalist · 22 weeks on weekends · See the program →
- Weeks 1–2AI Launchpad
Use the main AI assistants safely and well, check what they tell you, and understand how they work without the maths.
ChatGPTClaudeGeminiCopilotNotebookLMPerplexitySheets with AIan image and a video generator
- Week 3Your assistants, made into a system
Turn scattered AI chats into a system you and your team rely on.
ChatGPT ProjectsClaude ProjectsGemini GemsGemini in Google WorkspaceMicrosoft 365 Copilot
- Week 4How AI works, and a team knowledge base
Understand how AI learns and answers, without code, and build a knowledge base your team can ask.
NotebookLMdeep-research modesTeachable Machine
- Weeks 5–6Numbers and the story
Analyse numbers with AI, check its arithmetic, and present the story.
Excel / Sheets with AIPower BI DesktopLooker StudioGammaCopilot in Power BI
- Week 7Where AI moves the number
Find where AI saves time or money, and prove it before anyone builds.
A spreadsheetMiro
- Week 8Project weekend 1 · Five Hours Back
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Week 9Content, image, video and voice
Produce on-brand content in every format, faster.
ClaudeChatGPTCanva AIIdeogram or MidjourneyVeo or HeyGenElevenLabs
- Week 10Work that runs itself
Automate repetitive work end to end, and keep it running when something breaks.
n8nGmailSheetsSlack
- Week 11AI that predicts, without code
Use machine learning for forecasts, classifications and segments, and judge when to trust it.
KNIME
- Weeks 12–13Agents without code
Build AI agents that decide and act, without writing code.
n8nCustom GPTsGemini GemsVapi or Retell (voice agents)Copilot Studio
- Week 14Project weekend 2 · The First Week Unattended
A weekend to build the project live, get it reviewed, and catch up on anything missed.
Everything taught so far
- Weeks 15–16Agentic AI systems
Understand how agents decide, test them, and fix them when they go wrong.
n8n (with its built-in evaluations)MCPLovableAirtable
- Weeks 17–19Function Lab
Apply everything inside your own function, with deliverables your manager recognises.
Jira / Asana AICRM AIWhatsApp BusinessLooker Studiodocument AIby function
- Week 20Governed adoption and leading change
Roll AI out across a team safely, legally, and with people on board.
Policy and rollout templatescost model sheet
- Weeks 21–22The end-to-end project and Demo Day
Prove AI changed a number in your own work, and step into the next role.
Everything above
Five courses, each the start of a PG Program or a skill on its own.
- 2 weeksAI Launchpad
Week 1 and 2 of every PG Program, as a course on its own.
ChatGPTClaudeGeminiCopilotNotebookLMPerplexitySheets with AI
- 8 weeksData Engineering Fundamentals
Weeks 1–8 of the Data Engineering program, ending in the Kirana Ledger.
PostgreSQLPythonpandaspytestGitGitHubCopilot, Cursor or Claude Code
- 8 weeksAI Engineer Fundamentals
Weeks 1–8 of the AI Engineer program, ending in the No-Show Model.
PostgreSQLPythonpandasNumPyJupyterGitscikit-learn
- 8 weeksAI at Work
Weeks 1–8 of the AI Generalist program, ending in Five Hours Back.
ChatGPTClaudeGeminiNotebookLMExcel / Sheets with AIPower BIGammaMiro
- 8 weeksAI-Assisted Development
Cursor, Copilot and Claude Code, compared on your own work.
CursorGitHub CopilotClaude CodepytestGitHub Actions
Every tool and its price, in one file.
- Every tool across the three PG programs and the five courses
- What each one does, and the job it is best for
- Free, free plan or paid, for each
- The program and the week where you use it
Download it now. A program advisor calls within one working day to match the tools to the role you want.
Your tool map is ready.
A program advisor calls within one working day to walk through it.
Taught live by industry experts who use these tools at work.
They teach live, review your builds, and answer in the cohort chat between sessions, by name.
Meet the faculty before you apply: try a free live build, ninety minutes, live and free.
How we choose the AI and data tools we teach.
It has to be in the job posts
The stack comes from what Indian employers ask for, read every fortnight for The SkilD Brief. If employers stop asking for a tool, we stop teaching it.
One tool per job, taught properly
Two hours on one tool beats twenty minutes each on six. Where the market is still choosing, as with coding assistants, you learn all three side by side.
Opened in a live class
Every tool on the syllabus is one you open in a live session and use on a build the faculty review.
Pick the job you want, and the tools come with it.
Every tool here belongs to a job, and every job has a PG Program with placement support, from week 20 to the first offer.
Data Engineer
Spark · Airflow · Kafka · dbt · SnowflakeBuilds the pipelines every report and AI product runs on: moved, cleaned, tested, scheduled and costed.
AI Engineer
PyTorch · LlamaIndex · LangGraph · MLflowTrains, ships and watches models and LLM products: measured, deployed, monitored and costed.
The AI lead in your function
ChatGPT · n8n · Power BI · LovableFinds where AI moves a number in the team’s work, builds it without code, measures it and leads the rollout.
Pay: recrew.ai India Data Engineer and AI/ML Engineer salary guides, 2026 (top 10% at 8+ years) · TeamLease Digital, 2025.
- One price, everything includedevery project, every clinic, every mock interview.
- Live means livea room of thirty, every question answered in the session.
- Dates that holdplan your months around us.
- A mentor who knows your namefirst reply within one working day.
- Miss a weekwatch the recording, then bring your questions to the Saturday doubt clinic.
- Everything you build is yourscode, projects, portfolio. For good.
Start your application.
Three short steps. Step one takes a minute; finish the rest now or later.
Details saved.
Two short steps finish the application, now or later. A program advisor calls within one working day either way.
Your application is in.
A program advisor calls you within one working day with the timetable, the faculty list and the cohort calendar.
AI and data engineering tools: your questions, answered.
What tools are used in data engineering?
SQL and Python first, with Git; then a cloud (AWS, Azure or Google Cloud); Spark and Databricks for scale; Snowflake for the warehouse; Airflow and Docker to schedule and ship; Kafka to stream; dbt to model and test; and GitHub Actions to deploy. The PG Program in Data Engineering teaches them in that order, over 34 weeks.
Which data engineering tools should I learn first?
SQL, Python and Git, in that order. They are weeks 3 to 7 of the PG Program in Data Engineering, and every tool after them stands on them.
Will AI replace ETL tools and data engineers?
AI now writes a good share of ETL code. Companies still hire the engineer who tests it, schedules it, reruns it safely and fixes it when it breaks. Weeks 28 and 29 of the program are that job.
Which AI tools are taught in the AI Engineer program?
scikit-learn, XGBoost and SHAP; PyTorch and Hugging Face; the OpenAI, Anthropic and Gemini APIs with LlamaIndex, pgvector and RAGAS; LangGraph, MCP and CrewAI for agents; and FastAPI, Docker, MLflow, Evidently and Langfuse in production, plus the tools of the track you pick in week 33.
Which AI tool is best for data analysis?
If you work in spreadsheets, ChatGPT or Claude on an uploaded file, with every number checked: weeks 5 and 6 of the AI Generalist program. If you write code, Python and pandas with an assistant drafting it: weeks 5 to 7 of Data Engineering or AI Engineer.
Which coding assistant should I learn: Cursor, GitHub Copilot or Claude Code?
All three. AI-Assisted Development compares them and ends with one tool shipped in public, with its log of every suggestion kept or refused.
Can I learn AI tools without coding?
Yes. The PG Program · AI Generalist uses no code at any point: ChatGPT, Claude, Gemini, NotebookLM, Power BI, n8n, KNIME and Lovable, over 22 weeks of weekends.
What is n8n used for?
Automating work across apps, and building AI agents on a visual canvas, with a person approving anything that can’t be undone. Weeks 10 to 16 of the AI Generalist program build in it.
Do I need paid plans for the AI tools in the course?
Free plans, trials and open-source versions cover your coursework. The PG Programs add cloud credits (Data Engineering) and GPU and API credits (AI Engineer) for approved projects, and AI Generalist ends with a cost model for running the tools afterwards.
Is there an AI tools course with placement support?
Yes: all three PG Programs include placement support in five steps: timed whiteboard rounds, a panel review of your end-to-end project, mock interviews with people who have hired for the role, portfolio reviews, and introductions into the 39+ companies our learners work at.
Most people will read about these tools. Join the ones who build with them.
22 to 34 weeks on weekends, the industry experts who use each tool teaching it live, a portfolio you built on them, and placement support to your first offer.
Not ready to apply yet? Get the full tool map first.
Data and AI openings on WhatsApp, and the hiring market twice a month.
The roles the companies our learners work at are hiring for, and the dates of the next free live build. Read three issues of the Brief and you’ll know whether we’re your kind of school.
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The first alert lands the next time our network is hiring, and the next Brief lands in your email.