Merge pull request #418 from dellucifer/contrib

Rectified text formatting issue

README.md

@@ -80,7 +80,7 @@ Data Science is one of the hottest topics on the Computer and Internet farmland
 | [Wikipedia](https://en.wikipedia.org/wiki/Data_science) | _Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data._ |
 | [How to Become a Data Scientist](https://www.mastersindatascience.org/careers/data-scientist/) | _Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations._ |
 | [a very short history of #datascience](https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science.  The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
-|[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools.|
+|[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._|
 
 ## Where do I Start?
 **[`^        back to top        ^`](#awesome-data-science)**