Master Dask: Python Parallel Computing for Data Science

Install Dask for standalone, cluster, or cloud setups using pip or conda, enabling core, distributed, dataframe, array, and dashboard. Verify with import and version checks and connect schedulers and workers.

Set up a Dask client and dashboard to monitor distributed computations and memory usage while performing parallel data processing and training a machine learning model with Dask and sklearn.

Explore Dask delayed for building a task graph and lazy execution, and Dask futures for dynamic, real-time parallel tasks, with setup of a Dask distributed client and dashboard.

learn how dask arrays enable parallel processing of large data by chunking arrays, overcoming numpy memory limits, converting between numpy and dask arrays, and using compute to materialize results.

Learn how dask dataframes scale pandas for big data by reading large csvs in chunks, using parallel computing to handle missing values and filter the 2023 yellow taxi trip data.

Explore Dask bags as a memory-efficient tool for non-tabular data, including logs and JSON files, covering ingestion, processing, and conversion to Dask dataframes.

Continue exploring dask bags by filtering, mapping, and saving results as json files. Convert flattened bag data to dask dataframes for structured analysis, and compute top occupations with value counts.

Explore partitioning and chunking with Dask to load large datasets in 100 MB chunks, then save partitioned parquet files and partition by year and month for time-series analytics.

Explore lazy evaluation in Dask, building a task graph and deferring computation until compute is called, and apply it to dask arrays and dataframes with filtering, aggregation, and graph visualization.

Learn to build scalable etl pipelines with Dask by extracting large csv data, transforming it to compute trip duration, and loading the results to parquet format.

Master Dask demonstrates reading and analyzing large JSON, Parquet, and HDF5 files with Dask DataFrame and Dask Array, using lazy evaluation and compute to extract insights.

Master dask to process a large csv dataset with millions of rows by setting up a dask client, reading with dask dataframe, specifying dtypes, and previewing the first ten rows.

Learn to clean and transform data with Dask by dropping unused columns, filling missing values, creating a review length feature, and computing the average review length with performance timing.

Tune partitions and use persist to optimize Dask performance, then visualize results with matplotlib and monitor execution via the Dask dashboard.

Integrate Dask with scikit-learn to train models in parallel and distribute predictions across multiple systems. Use DAGs, chunking, and lazy evaluation to optimize resource use and scale machine learning workflows.

Use Dask to parallelize hyperparameter tuning with grid search cv across multiple workers, powering efficient model training. Train logistic regression on a NYC taxi dataset sample and achieve 0.91 accuracy.

Load the 2018 combined flights dataset with Dask, set up a Dask distributed client, and read the CSV to begin building a scalable machine learning pipeline.

Preprocess flight data with dask by dropping missing values and selecting key features; compute statistics before converting to dask arrays and performing an 80/20 train-test split for linear regression.

Apply feature scaling with the standard scaler to Dask arrays, train a linear regression model on the scaled data, evaluate with mse, and save the model for a Streamlit app.

Create a real-time flight delay predictor with a Streamlit frontend that loads a pre-trained model and scaler, accepts non-negative flight features, scales input, and displays predicted arrival delay.

Explore Dask streams, a real-time data processing module that enables immediate computation, dynamic scalability, and scalable distribution across machines for continuous data like IoT and social media feeds.

Create basic streams with Dask streams, push data, and process with map, emit, and sink. Use accumulate for running totals, and sliding window for overlapping groups in real time pipelines.

Learn how to branch a single stream into multiple outputs and join streams with zip and combine latest to build dynamic, real-time data pipelines, using increment and decrement functions.

Learn to set up rabbitmq with docker, and build a real time data streaming pipeline using streams and rabbitmq with producer and consumer scripts that convert messages to uppercase.

Build a real-time cryptocurrency price tracker with Binance WebSocket API, RabbitMQ, and matplotlib CLI visualization; implement a data producer that streams prices and a consumer that processes them.

Build a streamz pipeline to process real-time stock data from RabbitMQ, decoding JSON messages and applying a transformation before printing. Set up the RabbitMQ input, stream, and consumer callback.

Create a real-time stock market dashboard by streaming cryptocurrency prices from the Binance API through RabbitMQ, then visualize live Bitcoin price updates in Python with a dynamic Matplotlib graph.