Data Science is a Simple way to Gain Insight Into your Business
Data science jobs are plentiful, but very few people possess the data science skills required to fulfill these important positions. Science For Dummys is the ideal starting point for IT professionals or students looking to get a quick overview of all aspects of the vast data science field. The book focuses on business cases and covers topics in data science, big data, and engineering. It also discusses how these three areas can be combined to create tremendous value. This book will help you to understand programming languages and the mathematical methods that are available to you to start a new career.
This book is a fantastic science party favor guide to the vast, often intimidating field of big data, and data science. However, it does not provide an instruction manual for actual implementation. Here are some things you can expect:
This course provides a foundation in big data engineering and data engineering, before you move on to data science and how it is applied to create value.
- Coverage of big data frameworks such as Hadoop, MapReduce and Spark, MPP platforms and NoSQL.
- Describes machine learning, many of its algorithms, as well as artificial intelligence.
- Data visualization techniques to show, summarize and communicate data insights that you have generated.
- Science For Dummys will help you harness the power of big data and give your company a competitive advantage.
Research Management for Science For Dummys
I wasn’t very good at managing research data during my PhD. My former PI would probably tell you that I was quite bad at managing my research data. After seeing my lab book, she called an emergency meeting with the rest to discuss it. It may seem odd, but it is the main focus of my work now. My main goal when I founded figshare was to liberate all research outputs that would not be published using modern methods of research dissemination. This is the most important thing in academia today. It seems that funding agencies and governments are in agreement.
This is not the end, however, of the research cycle or at least of how most researchers deal with their outputs. Reusing, mining, and building on top of existing research can still unlock all of the power. It was the steps that came before that I fell on. My lab book was not well-organized and I struggled to document my research. Laziness was half of the problem. The other half was due to the fact that lab notebooks that are text-based are not suitable for the digital world in which we live.