Python Projects with Real Datasets (2026)

Python projects that go past toy scripts: customer segmentation, A/B tests, forecasting, optimisation, APIs and automation, each built step by step and checked as you go.

56 projects: 14 beginner, 24 intermediate, 18 advanced. Every step is checked as you go, an AI mentor helps when you are stuck, and you finish with a verifiable certificate.

Python projects

Python for DevOps: Begineer to Advanced with Project — Beginner-friendly, hands-on program where learners with zero coding background build a small but realistic “PyOps” toolkit used by SRE/DevOps teams (beginner · $1)
Python for Absolute Beginners: Build a Smart To-Do CLI — Learn Python from zero by building a real, industry-style command-line To-Do app. (beginner · $1)
Mastering AWS Lambda: From Basics to Advanced — Mastering AWS Lambda with Python: From Basics to Advanced Configurations in Real-World Cloud Workflows (beginner · $2)
Containerizing a Flask App with Docker — A modular Flask web application built step-by-step for local and Dockerized environments. (beginner · Free)
AWS Automation Toolkit using Boto3 and Python — AWS Automation Toolkit using Boto3 and Python (intermediate · $2)
FinOps on AWS: Bedrock + Lambda Cost Insights (with ChatGPT Integration) — learn finops using aws lambda and bedrock (intermediate · $2)
Real‑Time Ride Dispatch in Python: Building Uber with FastAPI — Leverage industry-standard technologies like FastAPI, SQLite, and Dijkstra's algorithm, you will gain practical insights into the world of software development and real-time systems. (advanced · $1)
Fine-Tuning LLMs: Dataset Design, LoRA, QLoRA, and Production Deployment — A deep, hands-on course on fine-tuning LLMs: dataset design, SFT/DPO/RFT, OpenAI hosted fine-tuning, local LoRA/QLoRA, evals, and a full AWS EC2 GPU capstone project. (advanced · $3)
Agent2Agent (A2A) Protocol Explained: Agent Discovery, Messaging, and Multi-Agent Gateways — Learn the Agent2Agent (A2A) protocol: AgentCard discovery, messages/tasks/artifacts, JSON-RPC, streaming, and security for building a multi-agent gateway. (advanced · $2)
How LLMs Actually Work: Tokens, Embeddings, and Transformers Explained — Learn how large language models actually work under the hood — tokenization, embeddings, transformer attention, and the training-versus-inference split — through DevOps analogies and hands-on labs. (beginner · $1)
Build MCP Servers with FastMCP: Tools, Resources, and Prompts for Any AI Client — Learn to build real MCP servers with FastMCP, exposing tools, resources, and prompts that work with Claude Desktop, Cursor, and any MCP-compatible AI client. (intermediate · $2)
Data Engineering Capstone: Ingestion to Dashboard End to End — Follow a data engineer building a full pipeline for a growing retailer: extract sources, load a warehouse, transform clean models, orchestrate with alerts, and ship a BI dashboard. (advanced · $2.50)
LLMOps for Production GenAI: Cost, Latency, and Release Gates — Learn to run GenAI like production infrastructure: resilient model gateways, cost/latency budgets, prompt caching, deployment gates, and incident response. (advanced · $1)
Local LLMs with Ollama: Run Production-Grade AI Models on Your Own Machine — Run production-grade open-weight language models locally with Ollama - no API key, no cloud costs, and no data leaving your machine. (beginner · $1)
Testing and CI/CD for Data Pipelines — Test dbt models and Python transforms, wire CI to run on every PR, and roll out pipeline schema changes safely with a real rollback plan. (intermediate · $1.50)
ETL vs ELT: Building Your First Data Pipeline — Follow a first-time data engineer at a coffee company as she replaces manual CSV exports with an automated ELT pipeline: extraction, transformation, warehouse loading, and orchestration. (beginner · $1.50)
GenAI Evals and Red Teaming: Building Quality Gates for LLM Applications — Learn to design multi-dimensional LLM evals, red-team for prompt injection and unsafe agent behavior, and wire eval results into automated ship/block release gates. (intermediate · $1)
GenAI Observability: Traces, Metrics, and OpenTelemetry for LLM Apps — Learn to instrument GenAI applications with traces, metrics, and OpenTelemetry semantic conventions so you can debug, evaluate, and audit LLM systems in production. (intermediate · $1)
GenAI Security: Threat-Modeling LLM Apps with the OWASP Top 10 — Learn to threat-model GenAI applications using the OWASP LLM Top 10, design least-privilege agent tools, and produce security review records for RAG, MCP, and agent workflows. (advanced · $1)
Linear Algebra for AI — Learn vectors, matrices, dot products, eigenvalues, and PCA by following an engineer building real embedding search and recommendation features. (beginner · $1)
Modern AI Agent Runtime: State, Guardrails, Handoffs, and Traces — Compare agent runtime patterns, design durable state and typed handoffs, and build a minimal agent trace contract ready for eval-based release gates. (advanced · $1)
pandas from Zero: Clean a Messy Customer Survey — Learn pandas from zero by cleaning a real-looking survey export: duplicates, 999s, mixed dates, dollar amounts and country spellings. Then direct your AI through the same job and catch its mistakes. (beginner · $1)
Real-Time Analytics: Streaming Data From Kafka Into a Warehouse Dashboard — Learn when real-time analytics beats batch, then stream Kafka events into a warehouse table and build a live, deduplicated dashboard with lag monitoring and outage recovery. (advanced · $2)
Retrieval-Augmented Generation (RAG) Tutorial: Chunking, Embeddings, and Grounded Answers — Learn to build a complete RAG system, from PDF ingestion through chunking, embeddings, hybrid retrieval, reranking, and grounded answers with citations. (intermediate · $2)
Robotics Engineering for AI and Autonomous Systems — Build and tune a real small delivery robot end to end, learning sensors, actuators, PID control, sensor fusion, and autonomous navigation through one engineer's hands-on story. (intermediate · $1.25)
Tool Calling and Function Calling for LLMs: Building the Model-Tool-Model Loop — Learn how to design tool schemas and build a safety-hardened, provider-portable model-tool-model loop so LLMs can request real data and actions instead of guessing. (intermediate · $1)
Apache Airflow Fundamentals: DAGs, Scheduling, and Orchestration — Follow a data engineer at a growing freight company as he replaces fragile cron jobs with Apache Airflow DAGs, retries, alerting, and dynamic task generation. (intermediate · $1.50)
Apache Spark Fundamentals: Distributed Data Processing — Follow a growing logistics company's data analyst as she rebuilds a crashing pandas job in Apache Spark -- architecture, DataFrames, partitioning, shuffles, and performance tuning. (intermediate · $1.50)
Audit Analytics in Python: Duplicate Payments, Benford's Law and Journal-Entry Testing — Test a full year of vendor payments and journal entries with pandas: duplicate payments, Benford's law and journal-entry tests. Then direct your AI through the same audit and catch its mistakes. (advanced · $2)
Churn Prediction with scikit-learn: Scored on Customers You Can't See — Build a churn model in pandas and scikit-learn, catch a leaky feature, and get scored on customers you can't see. Then direct your AI through the same job and catch its mistakes. (advanced · $2)
Data Governance Fundamentals: Cataloging, Lineage, and Access Control — A data engineer builds a lightweight catalog, lineage impact checks, and access control for a growing e-commerce company -- real data governance, not compliance theater. (intermediate · $1.50)
Data Lake Architecture: Parquet, Delta Lake, and the Lakehouse Pattern — Turn a messy S3 data swamp into a governed lakehouse with Parquet, Delta Lake, medallion layering, and compaction techniques, using real PySpark code. (intermediate · $1.50)
Data Observability: Catching Silent Pipeline Failures Before Your CEO Does — Learn the five pillars of data observability, build real freshness, volume, schema, and anomaly monitoring, and design an incident response process for data pipeline failures. (intermediate · $1.50)
Data Quality Engineering: Validation and Testing with Great Expectations — Stop shipping silent data bugs. Learn Great Expectations to write validation suites, catch bad data before it spreads, and build quality reports stakeholders trust. (intermediate · $1.50)
Exploratory Data Analysis in Python: Bike-Share Demand in the Monsoon — Explore six weeks of real-looking bike-share trips and weather with pandas and matplotlib, find what the data really says, then direct your AI through the same analysis and catch its mistakes. (intermediate · $1.25)
Memory Management in Agentic AI: Short-Term, Long-Term, and Episodic Memory Explained — Learn how to design short-term, long-term/semantic, episodic, reflective, and procedural memory for AI agents, with governance controls that keep it safe and trustworthy. (intermediate · $2)
Multimodal and Realtime AI: Images, Audio, and Voice Agent Event Streams — Model multimodal inputs and realtime voice-agent event streams, then apply safety boundaries around transcripts, tool calls, and vision/document analysis. (intermediate · $1)
Production-Grade ELT: Airflow + dbt + a Cloud Warehouse End to End — A capstone project: orchestrate dbt transformations from Airflow, enforce data quality gates, handle safe incremental backfills, and monitor a production ELT pipeline end to end. (advanced · $2)
Prompt Engineering for Reliable AI Systems: Zero-Shot, Few-Shot, and Prompt Injection Defense — Learn to write system prompts, calibrate model behavior with few-shot examples, force reliable step-by-step reasoning, extract clean structured output, and defend AI tools against prompt injection. (beginner · $1)
Spark Structured Streaming for Real-Time Pipelines — Build real-time PySpark pipelines with Structured Streaming: consume Kafka events, apply windowed aggregations with watermarking, and write reliable, exactly-once streaming output. (advanced · $2)
Customer Segmentation in Python: RFM Scores and k-Means Clustering — Segment an online store's customers with pandas and scikit-learn: RFM scores, named segments and k-means clusters. Then direct your AI through the same job and catch its mistakes. (advanced · $2)
Email A/B Testing in Python: Is the Uplift Real? — Analyse a real-looking email A/B test with pandas and SciPy, prove whether the uplift is real, then direct your AI through the same analysis and catch its mistakes. (intermediate · $1.25)
Pay-Equity Audit in Python: Raw Gap, Regression and a Remediation Budget — Audit a company's pay for gender equity with pandas and statsmodels: the raw gap, a regression that compares like with like, and a remediation budget. Then direct your AI through the audit. (advanced · $2)
Reinforcement Learning and Agents: From Bandits to Policy Gradients — Build a learning agent from scratch through a real story: multi-armed bandits, Markov Decision Processes, Q-learning, and policy gradients, all applied to an ad-tech bidding system. (intermediate · $1.25)
Sales Forecasting in Python: Beat the Baselines on a Hidden Future — Turn three years of messy daily sales into an honest weekly demand forecast with pandas and NumPy, backtest it against baselines, then direct your AI through the same job and catch its mistakes. (advanced · $2)
Banking Secure Coding L1: Fix SQL Injection in a Login Query — Reproduce a prepared login bypass, bind query values and prove that valid banking logins still work. (beginner · $1)
Banking Secure Coding L2: Stop Stored XSS in Customer Feedback — Trace stored feedback into a reviewer page, remove unsafe HTML trust and verify safe text rendering. (beginner · $1)
Banking Secure Coding L3: Remove Username Enumeration from Login — Make authentication failure responses uniform while preserving password verification and valid customer access. (beginner · $1)
Banking Secure Coding L4: Verify JWTs and Reject Tampered Tokens — Restore JWT verification and prove that valid, modified and expired token cases behave correctly. (intermediate · $1)
Banking Secure Coding L6: Enforce Account Ownership at the API — Stop cross-user account disclosure with a server-side ownership check and prove both legitimate owners retain access. (beginner · $1)
Banking Secure Coding L7: Fund Transfer Security Review — Review a 60-line transfer specimen, support four findings and prioritise three corrections by banking impact. (intermediate · $1)
Card Fraud Detection in Python: Imbalanced Classes, Precision-Recall and a Cost-Based Threshold — Build a card fraud model in pandas and scikit-learn, choose a cutoff in rupees, and get scored on transactions you can't see. Then direct your AI through the same job and catch its mistakes. (advanced · $2)
Credit Risk Scorecard in Python: WOE, IV and a PD Model, Scored on Applicants You Can't See — Build a bank-style credit scorecard for an NBFC in Python: WOE, IV, a logistic PD model, Gini, KS and points, scored on hidden applicants. Then direct your AI through it. (advanced · $2)
Operations Optimisation in Python: Production Mix (LP) and Depot Network (MILP) with scipy — Plan a snack plant's production mix (LP) and choose its depots (MILP) in scipy, checked against the true optimum of your own data. Then direct your AI through the same job and catch its mistakes. (advanced · $2)
Predictive Maintenance in Python: Rolling Features, Anomaly Alerts and Failures You Can't See — Build a 24-hour motor failure warning with pandas and scikit-learn: rolling features, a rule-based alert, a model scored on hidden breakdowns. Then direct your AI through it and catch its mistakes. (intermediate · $1.25)
Support Ticket Triage with NLP: TF-IDF and a Linear Model, Scored on Tickets You Can't See — Build a support-ticket router in Python: clean customer text, build TF-IDF features, train a linear model, get graded on hidden tickets, then direct your AI through it and catch its mistakes. (intermediate · $1.25)

Practise Python more

Python course: Learn Python by building, with code that runs in your browser.
DSA course in Python: Data structures and algorithms with practice problems and interview prep.
Data analyst job simulation: A simulated analyst workday: real requests, real data, scored work.

Frequently asked questions

Are these Python projects suitable for beginners?

Each project lists its level. Start with a beginner project, or take the Python course first if you have never written code; it runs in your browser.

Do I need to install anything for these Python projects?

Most projects run in labs inside your browser (SQL, Excel, Python, Power BI, Git and more). Cloud projects say up front when they use your own AWS account or a simulator instead.

How is a Bartman project different from a tutorial?

You do the work yourself, step by step. Each step is checked automatically, and an AI mentor helps when you are stuck, so you finish with work you can show rather than a video you watched.

Do I get a certificate?

Yes. Finishing a project earns a certificate with its own ID that anyone can verify on bartman.ai.

What is Bartman?

Bartman (bartman.ai) is a learning platform where you build real projects with an AI mentor, practise in free browser labs, take mock tests and practice exams, and run job simulations. It was founded in 2025 and is based in Hyderabad, India.