From Spreadsheets to DataFrames
Learn to transition from spreadsheets to Python DataFrames, exploring web scraping, data processing, analysis, and visualization using popular libraries like Pandas and Matplotlib. Gain skills for efficient data management and problem-solving.
Hands-On Regular Expressions in Python
Learn to create, understand, and effectively use regular expressions in Python for data validation, parsing, and normalization. Discover when and how to apply regex for optimal results.
Dashboards for All
Learn to create interactive dashboards using Python libraries like Bokeh and Panel. Explore data visualization techniques, analyze diverse datasets, and tell compelling stories through customized, user-friendly interfaces.
Hacking Dask - Diving Into Dask's Internals
Explore advanced Dask features: task graph optimization, plugins, cluster inspection. Gain deeper understanding of internals and apply to data-intensive workloads.
Functional Python
Explore functional programming concepts in Python, including higher-order functions, immutability, and lazy evaluation. Learn to write cleaner, more efficient code using functional paradigms.
No, Maybe and Close Enough - Using Probabilistic Data Structures in Python
Explore probabilistic data structures in Python for efficient counting and item recognition, focusing on Hyperloglog and Bloom Filter algorithms for fast, scalable solutions.
Statistical Typing - A Runtime Typing System for Data Science and Machine Learning
Statistical typing: A runtime typing system for data science and ML, extending primitive types to statistical data types. Enables multivariate schemas, generative data contracts, and easier data testing with pandera.
PyTesting the Limits of Machine Learning
Explore techniques and tools for testing machine learning components in Python, addressing challenges of nondeterminism and ensuring reliability in ML-driven applications.
Optimizing Data Retrieval with Python Celery
Explore asynchronous data retrieval using Python Celery to overcome browser connection limits, improve web application performance, and enhance user experience when visualizing large datasets.
Large Scale Data Validation - with Spark and Dask
Explore data validation techniques for large-scale datasets using Spark and Dask. Learn to transition from single-machine to distributed computing, combining frameworks for efficient validation across partitions.
Getting an Edge with Network Analysis with Python
Explore network analysis with Python to uncover hidden connections in everyday interactions, from social media to transportation, and gain insights into their impact on our lives.
Dask-SQL - Empowering Pythonistas for Scalable End-to-End Data Engineering
Explore dask-sql for Python-based SQL data extraction from enterprise datalakes, enabling seamless integration with PyData tools for machine learning, analytics, and data transformation workflows.
What We Learned from Papermill to Operationalize Notebooks
Explore notebook automation with Papermill, learning parameterization techniques and execution strategies to enhance troubleshooting pipelines and empower on-call developers.
Reproducible and Maintainable Data Science Code with Kedro
Discover how Kedro, an open-source Python framework, transforms experimental data science code into production-ready ML pipelines, emphasizing reproducibility, maintainability, and modularity.
Creating Extensible Workflows with Off-Label Use of Python
Discover how to create flexible yet constrained workflow systems using Python's type annotations, coroutines, and dataclasses for extensible APIs in data processing, ETL, CI/CD, and more.
Patterns of ML Models in Production
Explore ML model deployment patterns for production, covering online serving, offline processing, and scalable frameworks like Ray Serve, with insights from 100+ user interviews.
Data Processing on Ray
Explore Ray's architectural changes for scaling Python applications in machine learning and data processing, addressing challenges in large-scale workloads.
From NumPy to PyTorch - A Story of API Compatibility
Explore PyTorch's approach to NumPy compatibility, its importance, challenges, and necessary divergences in this insightful discussion on API design and data science workflows.
Effective Data Visualization
Mastering data visualization: from choosing plots to preprocessing datasets. Learn to effectively communicate data using Python libraries like seaborn, matplotlib, and plotly. Covers density estimation, dimensionality reduction, and interactive plotting.
A Hands-On Introduction to Multi-Objective Optimization
Hands-on Python tutorial exploring multi-objective optimization using Pareto Fronts. Learn to balance conflicting objectives like quality and cost, with practical applications in various fields including supply chain and therapeutics development.
Dock Your Jupyter Notebook
Learn to containerize Jupyter Notebooks with Docker for reproducible and shareable machine learning projects, enabling smooth execution on remote machines.