---
title: "README"
author: "Filip Wästberg"
date: "11/4/2019"
output: md_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Welcome to Smart Energi's Data Science Portal

Hello! And welcome to Smart Energis Data Science Portal.

District heating is one of the most environment friendliest ways of heating a city today. Thus, it is a crucial partner in tackeling the threat of Climate Change around the world. Despite its advantages there is a lot to be done to make District Heating more efficient and more environment friendly. In order to do this the District Heating industry needs to become better in exploating its data, and this is what this portal is about.

Our belief is that leading companies in this industry has a lot to learn from each other when it comes to Data Science and that it is in the benefit of all that we find common solutions to common problems together. This portal is the result of a joint project between Universities and District Heating companies. It is supposed to be a place where we can post, discuss and evaluate different Data Science methods to approach problems common in the District Heating industry. 

The portal follows a structure where we have *Analytical challenges*, *data sets* and *tutorials*.

## Analytical challenges

Analytical challenges is at the core of this portal. They are decided by the Data Science board, that consists of the founding partners. 

Typically an analytical challenge will be something like: "Can you find the leak within this distribution net?" or "Can you develop a method for anomaly detection?".

The answers to these challenges can be posted as a Notebook and generally you will use a progamming language, such as R or Python, to do the analysis. The reason we use Notebooks is because we want to make sure that the anaylsis posted here are reproducable and open for anyone with access to the portal to read and discuss. 

## Data sets

A data set is most commonly a CSV or Excel-file with data that relates to some kind of analytical challenges. It can be historical heating consumption, weather data, a list of faults within a distribution net, sensor data and so on.  

## Tutorials

Tutorials will be posted for those of you who want to get started with Data Science or the portal itself. **See: Get started with data analysis in Python**

## How can I contribute?

To contribute to an analytical challenge is easy. Just download the data that relates to the analytical challenge and do your anlaysis, then you simply upload your notebook and related files.

**See: How to upload a Notebook to the Data Science portal.**

If you want to post or discuss an analytical challenge please contact per.grosskopf@ferrologic.se and we will take it from there.