Starting from Start-ups to Conglomerates every organization is relying on data for decision making. They are looking for skilled manpower who can convert this gigantic data into sleek information for decision making. This course fuses Statistical Analysis with MS Excel because of which you would be able to churn legions of data into meaningful information within no time. Excel has hundreds of built-in functions and Data Analysis Toolpak (Excel Add-in) with which you can run descriptive statistics to predictive analysis with ease.
About this course: The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone.
This Learning R training course from Infinite Skills will teach you how to use R, a programming language used for statistical computing and graphics. This course is designed for beginners that have no previous R programming experience. You will require a fundamental understanding of statistics to get the most out of this course.
About this course: An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research.