market research & analysis studies the attractiveness and the dynamics of a special market within a special industry. It is part of the industry analysis and thus in turn of the global environmental analysis. The market analysis is also known as a documented investigation of a market that is used to inform a firm's planning activities, particularly around decisions of inventory, purchase, work force expansion/contraction, facility expansion, purchases of capital equipment, promotional activities, and many other aspects of a company.

Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.

You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.

The course’s objective is to provide a theoretical framework for considering corporate finance problems and
issues and to apply these concepts in practice.
I have three primary goals for the course: (1) to give everybody a base level of finance knowledge that an MPA
from a top business school should possess, (2) to give everybody the ability and confidence to tackle common
financial problems in practice, and (3) to provide adequate preparation for future finance classes, especially the
advanced corporate and investment classes at the McCombs School of Business.

This course presents the fundamental concepts of data analysis required to prepare students for advanced topics like acceptance sampling, statistical process control, reliability, and design of experiments. The material covered includes graphical presentation methods, basic concepts of counting (permutations and combinations) and probability, the discrete probability distributions of quality (hypergeometric, binomial, and Poisson), the normal, Students, chi-square, and F distributions. Students will learn to use these distributions to construct confidence intervals and perform hypothesis tests to make data based decisions. Examples will be taken from acceptance sampling and SPC applications. Introductions will be presented to linear regression, correlation, analysis of variance, and reliability. Extensive homework assignments will be given.