Data Analysis Projects | INIsPHO
The , which is an up-to-date version of the Strategic Plan, shows ongoing, completed, and anticipated near-term LTPP data analysis projects, as well as closely related National Cooperative Highway Research Program () projects.
Many people – in academic as well as business circles – consider bioinformatics as an application discipline, merely using IT solutions to help solve biological problems. This could not be further from the truth, especially when we reach the data interpretation phase. Here bioinformatics becomes almost pure science with a wide range of serious scientific questions waiting to be answered. The key lessons we learned from our many high-throughput data analysis projects are the following:
Many of the data analyses I have done in classes/assigned in class have focused on a problem with exactly the right information with relatively little extraneous data or missing information. But I have been slowly evolving these problems; as an example is a data analysis project that we developed last year for the qualifying exam at JHU. This project is what I consider a first step toward a “less helpful” project model. Our highly skilled statisticians are always ready to provide you the best service with your data. Our statisticians are all highly qualified with solid background in both theoretical and applied statistics as well as experienced of working in numerous data analysis projects in diverse disciplines including social sciences, economics, engineering, medical sciences, public health, psychology, business, education, nursing etc. We are equally experienced in working on data analysis projects for clients from both academia and industry.In March 2011 MTD completed data analysis project on annual Client Satisfaction Survey for M & R Badiri for the . The project was funded by the National Treasury and and covered Gauteng province.. He has several exploratory data analysis projects on Github that you can look at (e.g., "data-baby-names"), and given the awesomeness of ggplot2/plyr/reshape, I have a default (but admittedly blind) trust in his best practices, particularly with respect to his own packages.It is of great importance to bring efficiency to the PM model development process. We address planning; stating project use; study design efficiencies; data collection, construction and early use; model development; dealing with long runs; efficient use of time between runs; and report writing. We do not imply that the proposals given here are the only ways to improve the efficiency in a population modeling project. Indeed, all population modeling projects are different, so some of the ideas presented in this chapter may not always be applicable and, therefore, suitable solutions need to be set up for each project. The way to proceed in each specific case will be dependent on the resources available and the organization of the company. Further, the applicability of our ideas is also dependent on whether the data analysis project is in the initial, intermediate or late phases of the drug development program. However, hopefully this chapter will given the reader some concrete ideas on how efficiency can be improved and some thoughts on how to make PK/PD modeling an efficient aid in drug development decision‐making. Planning and interdisciplinary communication within the team may not resolve all problems and obstacles during a modeling project, but they are definitely good foundations for success in keeping within the time constraints of a modeling project. You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!