Learning resources for data science to win political campaigns?
This is an interesting and relevant question. I think that from data science perspective, it should not be, in principle, any different from any other similar data science tasks, such as prediction, forecasting or other analyses. Similarly to any data science work, the quality of applying data science to politics very much depends on understanding not only data science approaches, methods and tools, but, first and foremost, the domain being analyzed, that is politics domain.

Rapidly rising popularity of data science and machine learning (ML), in general, certainly has a significant impact on particular verticals and politics is not an exception. This impact can be seen not only in increased research interest in applying data science and ML to political science (for example, see this presentation, this paper, this overview paper and this whole virtual/open issue in a prominent Oxford journal), but in practical applications. Moreover, a new term - political informatics or poliInformatics or poli-informatics - has been coined to name an interdisciplinary field, which stated goal is to study and use data science, big data and ML in the government and politics domains. As I've said earlier, the interest in applying data science to politics goes beyond research and often results in politics-focused startups, such as PoliticIt or Para Bellum Labs. Following the unfortunate, but established trend in startup ecosystem, many of those ventures fail. For example, read the story of one of such startups.

I am pretty sure that you will be able to find neither proprietary algorithms that political startups or election data science teams used and use, nor the their data sets. However, I am rather positive that you can get some understanding about typical data sets as well as data collection and analysis methods via the resources that I have referenced above. Hope this helps.