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team sporthttp://radar.oreilly.com/2013/06/theres-more-than-one-kind-of-data-scientist.html
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How to start a data science project in the DataScience Experiencehttps://blog.godatadriven.com/how-to-start-a-data-science-project-in-python
Adding a custom library to a Jupyter Notebook in the DataScience Experiencehttps://datascience.ibm.com/blog/add-a-custom-library-to-a-jupyter-scala-notebook-in-ibm-data-science-experience-dsx/
How to Use dashDB in the DataScience Experiencehttps://datascience.ibm.com/blog/enhanced-dashdb-and-dsx-integration-now-available/
A visual introduction to Machine Learninghttp://www.r2d3.us/visual-intro-to-machine-learning-part-1/
Tour of Machine Learning Algorithmshttp://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Machine Learning Progression for Software Engineershttps://github.com/ZuzooVn/machine-learning-for-software-engineers
Recommended Resources for Beginnershttp://www.datasciguide.com/recommended-resources-for-beginners/
Notes on Data Science w/Chris Albonhttps://chrisalbon.com/
https://github.com/justinmccoy/datascience_resources#videos
https://www.youtube.com/playlist?list=PLn_pSzzTCsBDItVeyQkpHM1A6wwJ0_jVw
https://github.com/justinmccoy/datascience_resources#examples
Using correlations To understand Your Data in Rhttp://amunategui.github.io/Exploring-Your-Data-Set/
Collection of examples, notebooks and exercies in data science and machine learninghttps://github.com/jdwittenauer/ipython-notebooks
Analyze traffic data from the city of San Franciscohttps://developer.ibm.com/code/journey/analyze-san-francisco-traffic-data-with-ibm-pixiedust-and-data-science-experience/
Analyze and create data visualizations with Jupyter Notebookshttps://developer.ibm.com/code/journey/analyze-twitter-jupyter-notebooks/
Starcraft Replay Analysishttps://github.com/IBM/starcraft2-replay-analysis?cm_mc_uid=&cm_mc_sid_50200000=1500560380
Developing IBM Streams applications w/Pythonhttps://datascience.ibm.com/blog/developing-ibm-streams-applications-with-the-python-api-version-1-6-2/
IBM Watson Data Lab Githubhttps://github.com/ibm-watson-data-lab/
Examples of pattern classificaionhttps://github.com/rasbt/pattern_classification
Deep Learning Anomaly Detectionhttps://www.ibm.com/developerworks/library/iot-deep-learning-anomaly-detection-1/
Cognitive IoT Machine Learninghttps://www.ibm.com/developerworks/library/iot-cognitive-iot-app-machine-learning/
Realtime anomaly detectionhttps://developer.ibm.com/recipes/tutorials/realtime-anomaly-detection-on-the-iot-edge-using-nodered-and-moving-zscore/
Python Connectors for Loading and Saving Data in Notebookshttps://datascience.ibm.com/docs/content/analyze-data/python_load.html
Train and Deploy Core ML Models for iOS and macOShttps://datascience.ibm.com/blog/leverage-scikit-learn-models-with-core-ml/
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Achieve faster training of machine learning with TensorFlow and PowerAIhttps://developer.ibm.com/code/journey/accelerate-training-of-machine-learning-algorithms/
https://github.com/justinmccoy/datascience_resources#data
Awesome list of public datasetshttps://github.com/caesar0301/awesome-public-datasets
Historical and Current Financial Datahttps://www.assetmacro.com/market-data/
Socratahttps://www.assetmacro.com/market-data/
Data.govhttps://catalog.data.gov/dataset
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