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How to write a great data science thesis

#artificialintelligence

There are probably more than a thousand manuals on how to write a great thesis (some of my favorites can be found here, here and here). They will stress the importance of structure, substance and style. They will urge you to write down your methodology and results first, then progress to the literature review, introduction and conclusions and to write the summary or abstract last. To write clearly and directly with the reader's expectations always in mind. All of these tips are very valuable, but which tips apply to writing academically in the domain of data science?


Putting Data to Work : Research Library

#artificialintelligence

Data are constantly evolving; the only way to keep up is to start thinking differently about the data you collect and use in order to make better data-driven decisions. Putting Data to Work, by Ellen D. Wagner, explores how to do this, as well as identifies opportunities to advance the skillset required to be on top of your game in a data-driven digital world. This report analyzes how data are acting as a catalyst for change within the eLearning field. The time to start thinking about data is now, not in the future. Download this report to determine if you are using the right data for the best decisions in your work.


Research and Markets - Market Research Reports - Welcome

#artificialintelligence

As the volume of on-road automobiles increases dramatically, the search for new technologies to reduce fuel emissions and lower fuel consumption is key. We take a look at some new developments gaining popularity with leading automobile companies.


[Policy Forum] Opportunities for advances in climate change economics

Science

There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies.