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dsx-tutorials

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A collection of tutorials, demos, and use cases for IBM Data Science Experience http://datascience.ibm.com/

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- loansDataRaw.csv
- readme.md
- sample.csv
- Tidy Data Notebook R - Edition.ipynb
- - Building-Violations.ipynb
  - R-scoreData.ipynb
  - README.md
  - use1.jpeg
- - README.md
  - Spam-N-Bayes.ipynb
  - use2.jpg
- README.md
- neural_for_xor.png
- perceptron.png
- README.md
README.md

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A collection of tutorials, demos, and use cases for IBM Data Science Experience http://datascience.ibm.com/

#### Connect with us:
- Join the DSX community [in our Slack team](https://dsxcommunity.slack.com/)
- Send your feedback or product suggestions to the [DSX uservoice page](https://datascix.uservoice.com/)
- Say hi on [Twitter :-)](https://twitter.com/IBMDataScience)

---

## Table of Contents

<ul>
    <li><a href="#getting-started">Getting Started in DSX</a></li>
    <li><a href="#tuts">DSX tutorials</a></li>
    <li><a href="#demos">DSX Demos from conferences</a></li>
</ul>

---

## Getting Started <a name="getting-started"></a>

- Check out the [Getting Started in DSX repository](https://github.com/IBMDataScience/getting-started)

---

## DSX Tutorials <a name="tuts"></a>

- [Data Tidying in R](https://github.com/IBMDataScience/dsx-tutorials/tree/master/data-tidying-r)
    > Learn how to clean up your data with R in a Jupyter notebook
- [Word2Vec on Tweets](https://github.com/IBMDataScience/word2vec)
    >Hands on lab instructions to build Spark-based machine learning models for capturing word meanings
- [2016 Election Analysis](https://github.com/IBMDataScience/election2016)
    > In this study, we use social media data (specifically Twitter) to provide insights on each candidate's popularity, tweeting patterns, and most common topics. Additionally, we attempt to model and predict the success of a new candidate's tweet.
- [Twitter Analysis using DashDB, Insights for Twitter, and DSX](https://github.com/IBM/dsx-twitter-auto-analysis)
    >Code for a Cognitive Developer Journey that uses IBM's Data Science Experience. This code demonstrates how to use Jupyter Notebooks to analyze Twitter data and create data visualizations

---

## DSX Demos from conferences<a name="demos"></a>

- [Spark Summit West 2016 Demo - BlocPower Energy Use Analysis](https://github.com/IBMDataScience/SparkSummitDemo)
- [Strata East 2016 - Restaurant Health Inspections](https://github.com/IBMDataScience/Strata2016)
- [World of Watson 2016 - From Lab to Production](https://github.com/IBMDataScience/wow-lab-to-production)
- [Strata West 2017 - Hands on Lab](https://github.com/IBMDataScience/Strata2017)