Medical Laboratory made easy

The modern Medical Laboratory will encompass every aspect of technological advancement in the future. This era of Laboratory Medicine will be built on genetics and artificial intelligence which will revolutionize engineering and all its disciplines. This era will use the basic topics for programming principles in artificial intelligence. These topics are Lists, Tuples, Dictionaries, Loops, Sets, Strings, Variables, Exceptions, Functions, as well as Classes/Objects. Life is the most advanced form of technology which makes many engineers focus on programming biology. Programming biology will require a combination of biological, chemical and physical engineering in order to create forms of life that do not exist in nature. This final invention will disrupt every industry that ever existed in human history via the creation of superhumans that have achieved immortality and supernatural powers. Such ideas seem fictional to many but the science behind the possibilities indicate more fact than fiction. This is the easiest way to see how the Medical Laboratory will change the world.

This new reality of biological programming will be at the forefront of all forms of manufacturing and product development. There are biomaterials now being used to build houses and cars which are major manufacturing industries. Medical Laboratories will be at the center of genetically programming these biomaterials as the world combats climate change and lean more towards clean energy and other clean forms of manufacturing and product development. Currently there are biomaterials used to make plastics, bricks, and other essential raw materials. Books on data science are available online that can help Medical Laboratories advance in the field of artificial intelligence. These books include:

• Bruce, P., and Andrew Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts. O’reilly Media, 2017. ISBN-13: 978-1491952962 | ISBN-10: 1491952962 
• Rosen, Kenneth. Discrete Mathematics and Its Applications, 7th Edition, McGraw Hill, 2012. ISBN-13: 978-0073383095 | ISBN-10: 0073383090
• Introductory statistics: Concepts, models, and applications, by David W. Stockburger, http://www.psychstat.missouristate.edu/sbk00.htm
• Guttag, John V., Introduction to Computation and programming using Python: With Application to Understanding Data (Second Edition), Cambridge MA - The MIT Press, 2017. ISBN: 9780262529624. 
• Baase, Sara, and Allen Van Gelder, Allen. Computer Algorithms: Introduction to Design and Analysis, 3rd Edition. Pearson, 2000.
• Igual, Laura, and Santi Seguí. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. Springer, 2017. ISBN-10: 3319500163
• Lanzetta, Bianchi. R Data Visualization Recipes: A cookbook with 65+ data visualization recipes for smarter decision-making, Packt Publishing Ltd., 2017, ISBN: 978-1-78839-831-2. 
• Wexler, Steve, Shaffer Jeffrey and Cotgrave Andy. The Big Book of Dashboards, John Wiley & Sons Inc., 2017, ISBN: 9781119282716
• Unwin, Antony. Graphical Data Analysis with R (Chapman & Hall/CRC The R Series), Chapman and Hall/CRC, 2015, ISBN-10: 1498715230
• Learning Spark: Lightning-Fast Big Data Analytics. Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia. 2015. O'Reilly Media, Inc. 
• Mining of Massive Datasets. Anand Rajaraman and Jeff Ullman, Cambridge Press, http://infolab.stanford.edu/~ullman/mmds/book.pdf
• O'Neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books, 2017. ISBN-10: 0553418815 
• Evergreen, Stephanie DH. Presenting data effectively: Communicating your findings for maximum impact. SAGE Publications, 2017. ISBN-10: 1506353126 
• Witten, I.H., Frank, E. and Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, The Morgan Kaufmann Series in Data Management Systems, ISBN-13: 987- 0123748560. 
• Han, J., Kamber, M. and Pei, J. (2006), Data Mining: Concepts and Techniques, 3rd Edition, The Morgan Kaufmann Series in Data Management Systems, ISBN-13: 978-0123814791. 
• Russell, S. and Norvig, P., (2010), Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall.
• Elmasri, R. and Navathe, S. Fundamentals of Database Systems, 6th Ed., Addison-Wesley (2010). 
• Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom. Database Systems: The Complete Book, 2nd Ed., Prentice Hall (2009).

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