Why learn Data Science with SAS?
SAS is a leader in 2017 Gartner Magic Quadrant for Data Science Platform. The average salary for a Business Intelligence Developer skilled in SAS is $104k (Source - Paysa)
What are the course objectives?
Simplilearn’s Data Science with SAS online training course is designed to enable learners to become adept in analytics techniques using SAS data science tools. This online course covers a holistic overview of analytics and graphic user interface (GUI). You will learn how to combine dataset methods, understand select statements and joins in SQL, and comprehend the need for macro variables. This online training course will also teach you how to apply data manipulation and optimization techniques; advanced statistical concepts like clustering, linear regression and decision trees; data analysis methods to solve real-world business problems and predictive modeling techniques.
What skills will you learn?
This course will enable you to:
Who should take this course?
- Understand the role of data scientists, various analytics techniques, and widely used tools
- Gain an understanding of SAS, the role of GUI, library statements, importing and exporting of data and variable attributes
- Gain an in-depth understanding of statistics, hypothesis testing, and advanced statistical techniques like clustering, decision trees, linear regression, and logistic regression
- Learn the various techniques for combining and modifying datasets like concatenation, interleaving, one-to-one merging and reading. You will also learn the various SAS functions and procedure for data manipulation
- Understand PROC SQL, its syntax, and master the various PROC statements and subsequent statistical procedures used for analytics including PROC UNIVARIATE, PROC MEANS, PROC FREQ, PROC CORP, and more.
- Understand the power of SAS Macros and how they can be used for faster data manipulation and for reducing the amount of regular SAS code required for analytics
- Gain an in-depth understanding of the various types of Macro variables, Macro function SYMBOLGEN System options, SQL clauses, and the %Macro statement
- Learn and perform data exploration techniques using SAS
- Understand various time series models and work on those using SAS
- Model, formulate and solve data optimization by using SAS and OPTMODEL procedure
There is an increasing demand for skilled data scientists across all industries that make this course suitable for participants at all levels of experience. We recommend this data science training especially for the following professionals:
- Analytics professionals who want to work with SAS
- IT professionals looking for a career switch in the fields of analytics
- Software developers interested in pursuing a career in analytics
- Graduates looking to build a career in analytics and data science
- Experienced professionals who would like to harness data science in their fields
Prerequisites: There are no prerequisites for this course. The free SAS Base Programmer course provides some additional coding guidance.
What projects are included in this course?
The SAS Certification training course includes five real-life, industry-based projects including Walmart demand prediction. Successful evaluation of one of the following projects is a part of the certification eligibility criteria.
Project 1: Products rating prediction for Amazon
Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.
Project 2: Demand Forecasting for Walmart
Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.
Project 3: Improving customer experience for Comcast
Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.
Project 4: Attrition Analysis for IBM
IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.
Domain: Workforce Analytics
Project 5: Attrition Analysis
Analyze the employee attrition rate of a leading BPO company. The dataset is maintained for the attrition analysis, and it has records of employee id, retain indicator, sex indicator, relocation indicator, and marital status.
Project 6: Retail Analysis
Forecast sales based on independent variables such as profit, quantity, marketing cost, and expenses using the regression model.
Two additional projects have been provided for practice:
Project 7: Data-driven Macro Calls
Generate a list of all data sets in SAS which have sales-related information and pass it on as the macro variable.
Project 8: Customer Segmentation
Perform customer segmentation with RFM methodology on an e-commerce website’s customer data set. Segment customers based on frequency, recency, and monetary value.