Data Scientist Certification Training (R, SAS & Excel) by Simplilearn equips you with the work experience needed to be a skilled professional in the fast emerging field of Data Science. This data science training has been designed to enable you to make the most of relevant data with the required statistical concepts and tools.
What is this course about?
Data Scientist Certification Training (R, SAS & Excel) by Simplilearn equips you with the work experience needed to be a skilled professional in the fast emerging field of Data Science. This data science training has been designed to enable you to make the most of relevant data with the required statistical concepts and tools. At the end of this training you will be able to take up an exciting job opportunity in the field of Data Science.
This data science course will make you an expert at understanding the problem, designing the analysis and applying predictive modelling techniques to derive business insights from data.
What are the course objectives?
By the end of Simplilearn’s training in R,SAS and Excel, you will be able to:
Master the concepts of statistical analysis like linear & logistic regression, cluster analysis and forecasting
Get proficient in using R,SAS and Excel to model data and predict solutions to business problems
Gain fundamental knowledge on Analytics and how it assists in data-driven decision making
Visualize and optimize data effectively using the built-in tools in R , SAS and Excel
Master SAS codes on SAS platform and work with ease on R language
Work on real-life industry based projects using R,SAS, Excel
Who should do this course?
The booming demand for skilled data scientists across industries makes this course suited for all individuals at all level of experience. We recommend this data science training specially the following professionals:
Software professionals looking for a career switch in the field of analytics
Professionals working in field of Data and Business Analytics
Graduates looking to build a career in Analytics and Data Science
Anyone with a genuine interest in the field of Data Science
What projects will you be working on?
You will be working on 4 real-life industry based projects spread over 4 domains. These projects are for both R & SAS and one of them will be used for clearing the certification
Healthcare : In healthcare and other industries, predictors are most useful when their knowledge can be transferred into action. The willingness to intervene is the golden key to harnessing the power of historical and real-time data. More importantly, to best judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred. Predictive analytics can also be used in healthcare to mediate hospital readmissions.
Insurance : Predictive analytics use has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling compared to 69% of companies with less than that amount of premium.
Retail : Optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them an insight of the regular happenings in the retail sector.
Internet : Internet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. In this class, we explore a number of key functions of such online services that have become ubiquitous over the last couple of years. Specifically, we look at social & information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing, and online ad auctions.
4 Additional practice projects have been provided specifically for R
Music Industry: To understand what exactly a listener prefers listening to on radio, every detail is recorded online. This recorded information is used for recommending music that the listener is likely to enjoy and to come up with a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data predict the music preferences of the user for targeted advertising.
Finance: The success and failure, which represent defaulting on the loan and not defaulting, respectively needs to be predicted based on user demographic data. You need to perform logistic regression by considering the features of the loan and characteristics of the borrower as the explanatory variables.
Unemployment: Analyze the monthly seasonally adjusted unemployment rates for the employment data of the U.S. , covering the period January 1976 through August 2010 for the 50 U.S. states. The requirement is to cluster the states into groups that are alike using a feature vector.
Airline: Flight delays are frequently experienced when flying from the Washington DC area into the NYC area. Identify flights that are likely to be delayed using logistical regression. The data set provided helps with a number of variables including airports and flight timings.