SAS Analysis Training
SAS Analytics Training is intended to educate learners with thorough skills in using SAS software for data analysis, statistical modelling, and predictive analytics. The training covers many areas of data processing, visualization, and interpretation, giving participants the ability to extract actionable insights from complex datasets.
- 10+ Courses
- 30+ Projects
- 400 Hours
SAS Analytics Training is intended to educate learners with thorough skills in using SAS software for data analysis, statistical modelling, and predictive analytics. The training covers many areas of data processing, visualization, and interpretation, giving participants the ability to extract actionable insights from complex datasets. SAS Training objectives often include:
Mastering SAS Tools & Techniques: Learn how to manipulate, analyze, and visualize data with SAS software.
Understanding Statistical Procedures: Learn about the statistical procedures and strategies used in SAS to understand data and make informed judgments.
Practical Application: Apply SAS skills to real-world scenarios and projects to solve business problems and gain valuable insights.
Certification Preparation: Prepare for SAS certifications to confirm skills and advance your career in analytics and data science professions.
Career Advancement: Develop the abilities required to seek positions such as Data Analyst, Business Intelligence Analyst, Statistical Analyst, or Data Scientist in sectors that rely on data-driven decision-making.
Professional Development: Continuously learn and adapt SAS methodologies to keep up with market trends and analytics advancements.
Completing SAS Analytics Training provides the door to many job prospects in the United States, including:
Data Analyst: Data Analysts examine data to identify trends, patterns, and insights that influence business choices.
Business Intelligence Analyst: Creates reports and dashboards using SAS to aid in data-driven decision-making.
Statistical Analyst: Uses SAS tools for statistical modelling and hypothesis testing in fields like healthcare, finance, and marketing.
Data Scientist: Data Scientists use SAS for advanced analytics, machine learning, and predictive modelling to tackle difficult business problems.
Market Research Analyst: Market research analysts use SAS to analyze survey data and forecast market trends.
These positions are in great demand across a variety of industries, where SAS analytics expertise is appreciated for its ability to improve operational efficiency, optimize strategy, and drive innovation based on data-driven insights.
- Introduction to SAS: Interface (SAS Studio, Enterprise Guide)
- SAS libraries & datasets
- Data importing (Excel, CSV, SQL databases)
- Data step basics
- Understanding variables and observations
- Data step programming
- Conditional logic (IF-THEN-ELSE, DO loops)
- Creating new variables
- Data sorting and filtering (PROC SORT, PROC MEANS)
- Merging and appending datasets
- PROC FREQ, PROC MEANS, PROC UNIVARIATE
- Cross-tabulation and percentiles
- Handling missing values
- Generating reports using PROC REPORT
- PROC SGPLOT, SGPANEL, and SGSCATTER
- Bar charts, histograms, box plots, line charts
- Customizing visuals with titles, labels, colors
- Exporting graphs to Excel/PDF
- Introduction to PROC SQL
- Select, Where, Group By, Having
- Joins (Inner, Left, Right)
- Subqueries and set operators
- Creating views using SQL
- Descriptive vs inferential statistics
- PROC CORR (Correlation), PROC REG (Regression)
- PROC ANOVA (Analysis of Variance)
- Logistic regression basics
- Model interpretation and assumptions
- Introduction to Macros
- Macro variables and functions
- Macro loops and conditional logic
- Creating reusable macro programs
- Performance tuning basics
- Customer segmentation using demographics and transaction data
- Sales forecasting using regression
- Fraud detection using analytics
- Healthcare or Retail case studies
- Final project presentation
- Mock Interviews & Job Placement
It is a program that teaches you how to use SAS tools for data analysis, reporting, and predictive modeling. You learn how to turn raw data into meaningful insights for business decisions.
There are no strict prerequisites, but a basic understanding of statistics or Excel is helpful. Enthusiasm to learn data analytics is most important.
The duration is 2 months (8 weeks), with sessions held 2 times per week (either during week or weekends), including theory, hands-on practice and project work.
Yes, upon successful completion, you’ll receive a Certificate of Completion from Checkmate IT Tech.
While the focus is on SAS, we may also touch on Excel, SQL basics, and report automation, especially in the context of analytics workflows.
We offer online training classes to promote easy access to all candidates. Recordings are also made available for revision or if you miss a session.
Yes. We provide resume reviews, mock interviews, LinkedIn optimization, and guidance on job portals to help boost your chances in the job market.
We use SAS OnDemand for Academics, which is free, cloud-based, and accessible to all students for learning and practice.
Yes, some basic programming using SAS language is involved. However, it is beginner-friendly and easier to learn compared to many other programming languages.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
Email info@checkmateittech.com OR Call Us +1-347-4082054
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Job opportunities in USA and Canada
Completing SAS Analytics Training provides the door to many job prospects in the United States, including:
Data Analyst: Data Analysts examine data to identify trends, patterns, and insights that influence business choices.
Business Intelligence Analyst: Creates reports and dashboards using SAS to aid in data-driven decision-making.
Statistical Analyst: Uses SAS tools for statistical modelling and hypothesis testing in fields like healthcare, finance, and marketing.
Data Scientist: Data Scientists use SAS for advanced analytics, machine learning, and predictive modelling to tackle difficult business problems.
