Data Science using SAS 5/5 (4)

Review (3)


Data Science Using SAS is a must skill set for financial and banking analytics for most of the recruiters and bids for the highest amount of packages in analytics.

24 Sessions
144 Hours

Skills you will master

Base SAS
Diagnostic Analytics
Descriptive Analytics
Prescriptive Analytics
Predictive Analytics
Modeling Techniques
Linear Regression
Logistic Regression


02:00 PM - 06:00 PM
Mob : +91-9810600764
Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
Email : info@palin.co.in


About The Program

SAS (Statistical Analysis System) software is the world’s best information delivery system which is based on organization’s database.
SQL is Structured Query Language, which is a computer language for storing, manipulating and retrieving data stored in relational database
Tableau helps the world’s largest organizations unleash the power of their most valuable assets: Their Data & Their People.
This training will prepare candidates for rewarding and very well paying career as, Business Analyst, Fraud Analyst, Risk Analyst, programmer, developer or consultant.

During the training participants will be hands on

Data Science using SAS training

and will be going through


Most of the MNCs which have huge amount of data use SAS & Tableau in different domain such as IT Companies, Credit/Risk/Insurance Analytics, Retail Analytics, Telecom Analytics, Pharmaceuticals/Biotechnology Analytics, HR Analytics, Web Analytics & Others

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Career Advisor
Vinit Kumar, Consultant
Palin Delivers real world relevance with activities and assignments that helps students build critical thinking and analytic skills that will transfer to other courses and professional life.
Program Description
Data Science using SAS training certifies you as a business analytics professional. SAS is primarily an analytical tool which is widely used by the leading MNCs and Tableau is business intelligence tool for data visualisation and ranked as one of the best visualisation tools in the world. Largest KPOs, Banks like American Express, Barclays etc., Financial services firms like GE Money, KPOs like Genpact etc., Telecom companies like verizon (USA), consulting companies like Accenture , KPMG etc use these tools effectively
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    Session 1 - Introduction to SAS

By embracing data science tools and technologies, companies can more effectively inform strategic decision-making, reducing uncertainty and eliminating analysis-paralysis.
• What is Data Analytics & Data Science
• Different types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
• What is Artificial Intelligence
• What is IOT
• Machine Learning (Supervised & Unsupervised Learning)
• Overview of Banking, Healthcare, Telecom domain
• Working with multiple data sources – RDBMS (SQL Server, Oracle, My SQL, DB2), NOSQL (MongoDB, Cassandra, CouchDB)
• Real world Applications of Machine Learning & Deep Learning
• What to expect from this course (Salary, Market trends, job roles, Domain)

   Session 2 - Understanding Data Step Processing

Methods for getting Data into SAS, Informats(Numeric Informats, Character Informats, Date & Time Informats, Convert Dates & SAS Date Values), Formats (Numeric formats, Character formats, Date & Time formats), Pointers (Column Pointers, Line Pointers), Input Style (List input, Column input, Formatted input Modified Mixing input)

   Session 3 - Conditional Statements

Proc Import, Infile Statement, Options, File name statement with DDE, Proc Export, File & Put Statement, Automatic Variable, Assigning variable attributes (Temporary attributes, Permanant attributes), Global options, Local Options, Data Control Option,Statements, Global Statements, Local Statements, Control Statements.

   Session 4 - Functions & Joins


   Session 5 - Functions & Conditional Control Statements Countinuous

Date & Time Functions ( TODAY, DAY, HOUR, WEEKDAY, DATE, DATEPART, DATETIME, INTCK, INTNX, MDY, TIMEPART, YEAR, MONTH, QTR, DATEJUL, YRDIF), Reatain statement, Sum Statement, Conditional Control Statements(If Statement, If-then Statement, -If-Then-Else Statement Where Statement, Loops, Dountil Loop, Dowhile Loop) Array Statement.

   Session 6 - Procedures

Proc Print, Proc Transpose, Proc Contents, Proc Sort, Proc Formats, Proc Append, Proc Summary, Proc Datasets, Proc Tabulate, Proc Import, Proc Report, Proc Export, Proc Datasets, Proc Freq, Proc Means, Proc Reg

   Session 7 - Merging & Combining SAS Datasets

Subsetting Datasets, Concatenating, Merging, One-to-one merging, One-to-many merging, Many-to-one merging, Many-to-many merging, Matching merging, Updating, Retrieving Data From Multiple Table ( Natural Merge, Inner Merge, Outer Merge)

   Session 8 - SAS Analytics

Data Structure  Introduction to SAS interface and library structure and definition  Reading data using datalines and importing and exporting datasets  Infile statement - reading raw data  Formats and Informat  Variable attributes and data modification using Data and Set statements Data Management  Using conditional statements to modify data - Where, If and Nested IF  Appending and Merging datasets  SAS Functions for data manipulation  Loops and Arrays in SAS

   Session 9 - Fundamental of Statistics & Modeling Techniques

Basic Stats, Introduction to Predictive Analytics, Predictive modeling in SAS, Relevance in industry and need of the hour, Types of analytics – Marketing, Risk, Operations, etc, Future of analytics and critical requirement.

   Session 10 - Basic Analytics

Data Sampling, Hypothesis Testing, Sampling Distribution, Inference, P- Value, Critical Region, T-Test, Chi Square Test, F- Test, Data Preparation, Different Data Type, Data Cleaning, Derived Variables, Aggregate Data

   Session 11 - Correlation Linear Regression

The Correlation Techniques, Pearson's Correlation Coefficient (r), Variance, Co Variance, Correlation matrix, types of correlation, Simple Linear Regression, Regression Formulas, Assumptions of Classical Linear Regression Model, Zero Covariance and No Multicollinearity, Parameter Estimation Method, Deciding important variables, Model Selection Criterion.

   Session 12 - Multiple Linear Regression

Introduction to Predictive Analytics, Predictive modeling in SAS, Relevance in industry and need of the hour, Types of analytics – Marketing, Risk, Operations, etc, Future of analytics and critical requirement.

   Session 13 - Logistic Regression

Generalized Linear Models (GLMs), Binary Logistic Regression, SAS Logistic Regression Output Explanation, Fitting the Model in SAS, Fitting the Model in R, Test for Regression Coefficients, Validation Statistics, Goodness of Fit for Logistic Regression(Chi-square Test), Concordance, Prediction Using Logistic Regression, Making Predictions, Multicollinearity in Logistic Regression.

   Session 14 - Clustering & Customer Segmentation

Inter variable dependence, Hierarchical Clustering, k means clustering, Principal Component Analysis(PCA), Data Reduction, Eigen values, Eigen vectors, Assumption of PCA, Interpretation of results

   Session 15 - Predictive Modeling & Forecasting Techniques

Time Series Analysis, Assumptions of Time Series Analysis and Identifying Patterns in Time Series Data, General aspects of analysis, Autoregressive and moving averages, ARIMA Procedures and Time Series Model.

   Session 16 - case studies & project discussion

Domain Specific Case Study (Healthcare, Retail, Banking, E-Commerce etc.) and Project Discussion with Industry Experts.

   Session 17 - Tableau Introduction

Business Overview, Introduction to Data Visualisation, Introduction to Business Intelligence, Data Visualization tools, Data Connection, Tableau Server & Tableau Reader, Tableau Features

   Session 18 - Reports & Dashboards in Tableau

Basic Chart Type, Excel Data to Visualisation, Data Visualisation on Transformed Data using SQL, Steps to create basic charts (Bar Chart, Pie Chart, Line Chart, Dual Axis Chart, Stacked Area Chart, Scatter Plot Chart, World Map Visualisation), Creating Dashboards(Filters & Highlighting).

   Session 19 - Introduction to SQL

Overview of SQL, Installing the Test environment, Editors & Platform to Learn SQL, Complete SQL in Class, Using the basic SELECT statement, Selecting rows, Selecting columns, Counting rows, Inserting data, Updating data, Deleting data, Fundamental of SQL. Function: Numbers and SQL, About numeric types, Finding the type of a value, Integer division and remainders, Rounding numbers, Dates in SQL, Use of Dates and times, Date- and time-related functions, Aggregates Functions, How, aggregates work, Using aggregate functions, Aggregating DISTINCT values, Exploring SQL Transactions, Why use transactions?, Using transactions

   Session 20 - Triggers & View in SQL

Studying Triggers in SQL, Updating a table with a trigger, Preventing automatic updates with a trigger, Automating timestamps with a trigger, What are Subselect and Views in SQL, Creating a simple subselect, Searching within a result set, Creating a view, Creating a joined view, A Simple CRUD Application in SQL, Touring the CRUD application, The SELECT functions, The INSERT, UPDATE, and DELETE functions

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Program Highlights
Live interactive and classroom training will include the practical approach and assessments on regular basis.
Live Case Study
Domain Specific Live interactive case study using industry specific data, problem statements, solution architecture.
Assignments which helps in conceptual understanding, Prepared for interview training & techniques.
Lifetime Access
Class recording, Study material ppt’s, pdf, assignments, datasets, case studies can be accessed through out the lifetime.
24 X 7 Support
We will support If any concern is raised related to the training, assignments, projects, case studies & interview questions.
After successful completion of the training and case study Palin will certify you as Palin certified Data Scientist.
Career Counselor
Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.

Highly motivated with leadership skills having Master’s Degree in Statistics. Holds good working experience in different domains like Retail, Banking, Healthcare FMCG, Marketing analytics using SAS, R, Python, STATA, Tableau etc.
   Who can go for Data Science using SAS?
Anyone who wants to learn programming with SAS can start right away! The course is exclusively designed for students and professionals aspiring to make a career in Data Analytics with SAS. • SAS Developers • Analytics Professionals Other professionals, from technology field, willing to acquire a solid foundation of this widely-used Analytical language, can also opt for this course.
   What is Data Science using SAS?
SAS (previously "Statistical Analysis System") is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics
   What is the future of Data Science using SAS?
In view of the recent rise in the market of Data Science and Predictive Analytics, SAS seems to have a good future ahead. The future of SAS might go down if data scientists/ prediction analysts fail to deliver as expected.Learning R and Python in addition to SAS can help as a contingency plan, because many mid-level employers rely on open-source software. Many government departments and big corporations use SAS
   Pre-requisites for Data Science using SAS?
It would be advantageous for you to have prior programming experience and familiarity with basic concepts such as variables, flow-control, and functions along with basic knowledge of Stats. But if you have none and are motivated you will do fine
   Is this a classroom training or online?
Palin Analytics offers you both the training modes. You can opt either classroom or online.
   Which one is better Online or Classroom?
Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.
   What all topics will be covered in Data Science using SAS?
Introduction to SAS Programming, Overview of libraries, Understanding Data step Processing, Functions, Conditional Statements, Procedures, Combining SAS Datasets, SAS Merging, Introduction to Business Analytics, Fundamental of Statistics, Data Prep & Reduction techniques, Customer Segmentation, Regression Modelling, Predictive Modeling & Forecasting, Tableau for reports, charts & dashboards and SQL
   After the course for which companies I can apply for?
Almost every organisation which has a large customer base is shifting to SAS Analytics. Companies like Accenture, IBM, EXL, KPMG, AON, American Express, Bank of America, Cognizant are highly in need for SAS professionals.
   What are pay packages for fresher in Data Science using SAS?
On an average a fresher gets 4-6 LPA with a skill set of Data Science using SAS
   What is the process for Data Science using SAS?
We'll be starting from introduction to SAS programming and it's libraries to understand data step processing and working with files & databases. Then there will be advanced SAS learning which covers SAS/SQL & SAS Macro and advance statistical techniques to get hands on business analytics with regression modeling, Time Series analysis and then to understand business intelligence we cover Tableau, and SQL to understand data base


    Nothing compares to Palin. Passionate. High standards. Leaders. Benchmarking excellence. Supportive. Got your back. Thriving. Encouraging growth. Fun. Laughter. Real outcomes. Community benefits. Quality. Professional and full of people, clients and a community of brilliant, phenomenal people. As I said, its like nothing else. . It is sublime and so welcoming. You become part of a family that serves not only you, but others, colleagues and the whole community. It’s awesomeness!

    Thanks for the Training & the Knowledge you delivered to me. This real time training is extremely helpful in my career growth. Best Infrastructure and the Best Team of Professional Trainers.
    Thanks for your backup Support as well that helped me out to clear my doubts.

    Thank you Palin for enhancing my skills in Data science. *Highly Recommended*

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