Introduction to Data Analysis sets a foundation for those interested in pursuing more advanced topics in data analysis.

Becoming a data analyst requires proficiency in several areas:

• Data wrangling
• Programming
• Statistical methods
• Machine learning
• Data visualization

There is truly a vast amount of open data available to anyone who knows where to find it, but data is often found in a form that does not lend itself to analysis.

Data wrangling is the use of tools and techniques to transform unstructured data into structured data from which descriptive, predictive, and prescriptive analytics can be derived. Business leaders depend on high-quality, real-time analytics to make informed business decisions. This "business intelligence" enables a company to differentiate itself from its competition, but high-quality, real-time analytics is the end game. One has to walk before they can run. Data analysts often point out that roughly 70% of their work involves data wrangling.

This course sets you on a successful path in data analysis by building a solid foundation. A course will follow that introduces learners to machine learning via Microsoft Azure Machine Learning Studio.

Recommended preparation: Algebra and basic statistics. No programming experience is required - the necessary R programming will be learned in the context of working with data sets.