This tutorial gives you an overview and talks about the fundamentals of Data Science.
Data science is an emerging field, with rapid changes, great uncertainty, and exciting opportunities. Our study attempts the first ever benchmark of the data science community, looking at how they interact with their data, the tools they use, their education, and how their organizations approach data-driven problem-solving. We also looked at a smaller group of business intelligence professionals to identify areas of contrast between the emerging role of data scientists and the more mature field of BI. Our findings, summarized here, show an emerging talent gap between organizational needs and current industry capabilities exemplified by the unique contributions data scientists can make to an organization and the broad expectations of data science professionals generally.
Broadening the Data Science Community: While Data Science is most often associated with Big Data, it is important to consider the host of other professions and roles that deem their work to be data science. This includes people from fields as diverse as Market Research, Financial Analysis, Information Technology, Management Consulting, Marketing and Media, Academia, Social Research, Demographic and Census Research and the Intelligence Community – it is no wonder this segment is difficult to define
“R” is an open source software program, developed by volunteers as a service to the community of scientists, researchers and data analysts who use it. R is free to download and use. Provides guidance on getting data into R from text files, web pages, spreadsheets, databases and other sources. Demonstrates techniques that use core features of the R language, and are scalable and efficient. Covers many built-in functions along with selected packages from CRAN. Since its inception, R has become one of the prominent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline which also specializes in prediction-making. Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks were designing and programming explicit, rule-based algorithms are infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition “can be viewed as two facets of the same field.
Well, this was an overview of Data Science training program, however, the Data Science online training sessions are more detailed and organized, in order to educate the learners.
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