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S-A-S Data Science Training - Data Science using S-A-S

Industry relevant S-A-S Data Science training with strong focus on Statistical Analysis & Predictive Modelling.
Analytics Academy > Courses > Data Science using S-A-S

This S-A-S Data Science training encompasses basic statistical concepts to advanced analytics and predictive modelling techniques using Stastical Analysis System.


This course is designed considering some specific industry segments and is preferred for reporting analytics and predictive modelling, while tools like R and Python gets an edge when it comes advance data science, machine learning and AI applications.


Post completion of this S-A-S Data Science training candidates can also appear for optional Global Certification (Certification fee not included in the course fee).


Crafted and delivered by a team of industry experts, this comprehensive S-A-S data science training has all the components required to give you a head-start into the field of advance Analytics!


S-A-S Data Science course duration: 200 hours (At least 72 hours live training + around 10 hrs of weekly self-study and practice).

Who Should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who learn SAS & R for advanced analytics job roles.

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Course Duration 200 hours
Classes 24
Tools S-A-S (Statistical Analysis System)
Learning Mode Live Training
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  • Analytics World
    • Introduction to Analytics
    • Concept of ETL
    • S-A-S in advanced analytics
  • Global Certification: Induction and walk through
    • Getting Started
    • Software installation
    • Introduction to GUI
    • Different components of the language
    • All programming windows
    • Concept of Libraries and Creating Libraries
    • Variable Attributes - (Name, Type, Length, Format, In format, Label)
    • Importing Data and Entering data manually
  • Understanding Datasets
    • Descriptor Portion of a Dataset (Proc Contents)
    • Data Portion of a Dataset
    • Variable Names and Values
    • Data Libraries
  • Understanding Data Step Processing
    • Data Step and Proc Step
    • Data step execution
    • Compilation and execution phase
    • Input buffer and concept of PDV
  • Importing Raw Data Files
    • Column Input and List Input and Formatted methods
    • Delimiters, Reading missing and non standard values
    • Reading one to many and many to one records
    • Reading Hierarchical files
    • Creating raw data files and put statement
    • Formats / Informat
  • Importing and Exporting Data (Fixed Format / Delimited)
  • Proc Import / Delimited text files
  • Proc Export / Exporting Data
  • Datalines / Cards;
  • Atypical importing cases (mixing different style of inputs)
    • Reading Multiple Records per Observation
    • Reading “Mixed Record Types”
    • Sub-setting from a Raw Data File
    • Multiple Observations per Record
    • Reading Hierarchical Files
  • Importing Tips
  • Understanding and Exploration Data
    • Introduction to basic Procedures - Proc Contents, Proc Print
  • Understanding and Exploration Data
    • Operators and Operands
    • Conditional Statements (Where, If, If then Else, If then Do and select when)
    • Difference between WHERE and IF statements and limitation of WHERE statements
    • Labels, Commenting
    • System Options (OBS, FSTOBS, NOOBS etc…)
  • Data Manipulation
    • Proc Sort - with options / De-Duping
    • Accumulator variable and By-Group processing
    • Explicit Output Statements
    • Nesting Do loops
    • Do While and Do Until Statement
    • Array elements and Range
  • Combining Datasets (Appending and Merging)
    • Concatenation
    • Interleaving
    • Proc Append
    • One To One Merging
    • Match Merging
    • IN = Controlling merge and Indicator
  • Introduction to Databases
  • Introduction to Proc SQL
  • Basics of General SQL language
  • Creating table and Inserting Values
  • Retrieve & Summarize data
  • Group, Sort & Filter
  • Using Joins (Full, Inner, Left, Right and Outer)
  • Reporting and summary analysis
  • Concept of Indexes and creating Indexes (simple and composite)
  • Connecting S-A-S to external Databases
  • Implicit and Explicit pass through methods
  • Macro Parameters and Variables
  • Different types of Macro Creation
  • Defining and calling a macro
  • Using call Symput and Symget
  • Macros options (mprint symbolgen mlogic merror serror)

  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Introduction to Predictive Modeling
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modeling
  • Need of Data preparation
  • Data Audit Report and Its importance
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioural Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling
  • Interpretation of results - Implementation on new data
  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation - Implementation on new data
  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve,
    Probability Cut-offs, Lift charts, Model equation, Drivers, etc)
  • Interpretation of Results - Business Validation -Implementation on new data
  • Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • Basic Techniques - Averages, Smoothening, etc
  • Advanced Techniques - AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
  • Statistical learning vs. Machine learning
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
  • Recursive Partitioning(Decision Trees)
  • Ensemble Models(Random Forest, Bagging & Boosting)
  • K-Nearest neighbours
Study the flights data along with their schedules, weather and also plane information thereby understanding flight routes and delays. This case study comprises of four sections (Import and understand the data, Preparing data for analysis, Perform exploratory analysis, forming & testing hypothesis etc)
Data mining the sales transaction data using SAS to find key sales trends.
Understand customer spend & repayment behavior, along with evaluating areas of bankruptcy, fraud, and collections. Also, respond to customer requests for help with proactive offers and service.
Perform different graphical analysis (bar chart, pie chart, box plot, histogram, stacked charts, heat maps, scatter plots, panel charts etc) for solving various business problems
A credit card company wishes to understand its customer behavior so to have an enriched customer profile by having intelligent KPI’s. The idea is to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.
A wireless telecom companies wants to reduce customer churn by developing a proactive churn management model. The idea is to build a logistic regression based predictive model to develop an incentive plan for enticing would-be churners to remain with the company.
A bank would like to build credit risk model (application score card using PD models) to accept/ reject applications for loans. Also it wants to understand the key drivers for default or delinquency.
The objective of this case study is to understand what's driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process and help set the credit limit and designing new product offerings.
Use time series analysis to forecast the outbound passenger movement for next few quarters.
Objective of this analysis is to obtain data from Twitter and check how the sentiment varies by country for a particular brand/keyword/company.

Access to 108 hours instructor led live classes of 24x3 hours each, spread over 18 weekends, apart from 18 hours of video content

Video recordings of the class sessions for self study purpose

Weekly assignment, reference codes and study material in PDF format

Module wise case studies/ projects

Specially curated study material and sample question for SAS Global Certification

Career guidance and career support post the completion of some selected assignments and case studies

Don’t worry. You will always get a recording for the class in your Learning Management System (LMS) account. Have a look at that and reach out to the faculty in case of doubts. All our live classes are recorded for self-study purpose and future reference, and these can also be accessed through our LMS. Hence, in case you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question in the beginning of the subsequent class.

You can also repeat any class you want in the next one year after your course completion. Batch change policies will however, apply in this case.

Please note that incase you are not able to complete your course within one year of course validity, due to reasons at your end, limited support might be available post the completion of one year.

1 year post your course commencement. If needed, you can also repeat any number of classes you want in the next one year after course completion. Batch change policies will however, apply in this case.

In case required because any genuine reasons, the recordings access can be extended further for upto 1 year post the completion of one year validity. Please note that given the constant changes in the Analytics industry, our courses continue to be upgraded and hence old courses might no longer hold relevance. Hence, we do not promise lifetime access just for marketing purposes. 

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are integral part of AnalytixLabs intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under copyright act. Incase a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated well, the certificate shall be awarded.

Please note that incase you are not able to complete the course within the one year validity, AnalytixLabs might hold a mock interview/viva, apart from your submissions, before issuing the certificate.

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals (as and when applicable) through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

To avoid any kind of ambiguity, we strongly suggest that you go through our course brochures.

Please also note that incase you are unable to complete the course in the one year course validity period, we might not be able to assist with placements. The discussion you would have had with our counsellors/faculty at the time of registration is also basis the profile you hold at that time. Incase your course has got delayed due to reasons at your end, this might effect the profile, in which case it might be difficult for us to assist in placements.

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

Yes we have classroom option for Delhi-NCR and Bengaluru candidates. However, most of our students end up doing instructor led live online classes, including those who join classroom in the beginning. Based on the student feedback, the learning experience is same both in classroom and instructor led live online fully interactive mode.

We provide both the options and for instructor led live online classes we use the gold standard platform used by the top universities across the globe. These video sessions are fully interactive and students can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.

To attend the online classes, all you need is a laptop/PC with a basic internet connection. Students have often shared good feedback of attending these live classes through their data card or even their mobile 3G connection, though we recommend a basic broadband connection.

For best user experience, a mic-headphone is recommended to enhance the voice quality, though the laptop’s in-built mic works fine and you can ask your question over the chat as well.

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

  • Instructor Led Live online or Classroom - Within 7 days of registartion date and latest 3 days before batch start
  • Video-based - 2 days

Yes. While making the fee payment, most of the courses have the installment option.

For all the courses, we also provide the recordings of each class for their self-reference as well as revision in case you miss any concept in the class. In case you still have doubts after revising through the recordings, you can also take one-to-one time with the faculty outside classes during. Furthermore, if students want to break their courses in different modules, they get one year time to repeat any of the classes with other batches.

There is no particular system requirement for this course since the tools required for this course (Excel, SAS and R) can easily be installed on almost every laptop with basic configuration available these days. However, if possible, it is recommended to have 64-bit operating system.

“

I learned Data Science, various analytic techniques, and their application to business from “Analytix labs”. Institute pays an individual attention to every student and provide much needed guidance to shape the career in analytics. Faculties paced the course well, and kept it extremely engaging and practical. They provide case studies and small projects which gave me a real-life flavor to my learning. Moreover their live sessions were quite interactive, the pre-recorded sessions offered flexibility for working professionals.


Anshul Joon
(Consultant, Infosys)
Have Questions?
Contact us and we shall get back with answers.
ASK NOW >

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