Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Docker image for Jupyter notebooks with PySpark

c++, data_analysis, deep_learning, docker, git, python etc.

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to…

Jupyter Notebook showing how to process Telecom datasets using PySpark (SparkSQL and DataFrames) and plotting the results using Matplotlib.

Hadoop environment with HDFS, Spark, Hue, Jupyter Notebooks, etc. all installed in docker-compose

📓 [Active] Portafolio of data science projects. Using: Python, PyTorch, Spark, Tensorflow, Scikit, Keras. Includes Classification, Regression, Time series, NLP, Deep learning, among others.

hdp-notebook

Recently updated with 50 new notebooks! Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

A spark cluster configuration with the Apache Toree notebook

Exercise of using the Streaming API with Hadoop to determine the word count of Wikipedia articles.

Hadoop beginner exercise in analyzing European football teams' statistics over the last 20 years. The goal is to determine which team had the highest win percentage-rate.

Simple examples of using spark RDDs with a remote jupyter notebook

Simple notebook illustrating Hadoop example with map reduce on a large file for big data.

Stack to run Zeppelin notebooks over Hadoop and Spark

This is the final project I had to do to finish my Big Data Expert Program in U-TAD in September 2017. It uses the following technologies: Apache Spark v2.2.0, Python v2.7.3, Jupyter Notebook (PySpark), HDFS, Hive, Cloudera Impala, Cloudera HUE and Tableau.

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