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(2,095 ratings) 50,000+ Student

DATA SCIENCE WITH S-A-S TRAINING IN BANGALORE

This course starts with Data Science and Statistics using Python and then complete knowledge of Data Science with S-A-S. It covers both the aspects of Statistical concepts and the practical implementation using S-A-S. If you’re new to Programming , don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly.This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on Mac OS or Linux desktop systems.

Radical Technologies

50,000+ English 10 weeks Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

DATA SCIENCE WITH S-A-S TRAINING IN BANGALORE

IT Training Programme

Duration 10 weeks

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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100% placement assistance

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What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

18 sections covering the complete curriculum — a single, progressive learning arc.

10 weeks

Training Duration

Core Modules

183

Total Lessons

4.7★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a uncategorised role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

18 sections • 183 lessons • 10 weeks

Jump to Section

Total Duration

10 weeks

100% Practical · Unlimited Doubt Sessions

01 Data Science With S-A-S Course Content / Global Certification: Induction and walk through

Data Science With S-A-S Course Content

introduction To Analytics

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

02 Understanding Datasets / Base S-A-S – Accessing The Data

Understanding Datasets

Descriptor Portion of a Dataset (Proc Contents)

Data Portion of a Dataset

Variable Names and Values

Data Libraries

Base S-A-S – Accessing The Data

Understanding Data Step Processing

Data Step and Proc Step

Data step execution

Compilation and execution phase

Input buffer and concept of PDV

03 Importing Raw Data Files / Accordion TitleImporting and Exporting Data (Fixed Format / Delimited)

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

Accordion TitleImporting and Exporting Data (Fixed Format / Delimited)

Proc Import / Delimited text files

Proc Export / Exporting Data

Datalines / Cards

04 Atypical importing cases (mixing different style of inputs) / Hypothesis Testing and ANOVA

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

Concept of SAS library and SAS Catalog

Variable Types in SAS

Reading Data stored external to SAS

Importing Data by using Proc Import

Data Step SAS statements

SAS Functions

Appending and Merging using SAS

SAS Procedures like proc means, proc Univariate, proc append, proc freq and proc export.

SAS SQL

SAS Macros

Hypothesis Testing and ANOVA

One Sample t-test of comparing means

Two Sample t-test of comparing means

One Way ANOVA

Assumptions of ANOVA Modeling

n-way ANOVA

ANOVA Post Hoc Studies

05 Measure Model Performance / Data Understanding, Managing And Manipulation

Measure Model Performance

Apply the principles of honest assessment to model performance measurement

Assess classifier performance using the confusion matrix

Model selection and validation using training and validation data

Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection

Establish effective decision cut-off values for scoring

Data Understanding, Managing And Manipulation

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…)

06 Data Manipulation / Combining Datasets (Appending and Merging)

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

07 Data Mining With Proc SQL / Macros For Automation

Data Mining With Proc SQL

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

Macros For Automation

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)

08 Fundamental Of Statistics / Introduction To Predictive Modelling

Fundamental Of Statistics

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

Levels of Measurement and Variable types

Descriptive Statistics and Picturing Distributions

Confidence Interval for the Mean

Introduction To Predictive Modelling

Introduction to Predictive Modeling

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modeling

09 Data Preparation / Segmentation

Data Preparation

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

Segmentation

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

10 Linear Regression / Logistic Regression

Linear Regression

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

Logistic Regression

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

11 Time Series Forecasting / Introduction To Machine Learning

Time Series Forecasting

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

Introduction To Machine Learning

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)

12 Regression & Classification Model Building

Regression & Classification Model Building

Recursive Partitioning(Decision Trees)

Ensemble Models(Random Forest, Bagging & Boosting)

K-Nearest neighbours

13 L1 Tasks

1. Basic user account management (creating, modifying, and deleting users).

2. Password resets and account unlocks.

3. Basic file system navigation and management (creating, deleting, and modifying files and directories).

4. Basic troubleshooting of network connectivity issues.

5. Basic software installation and package management (installing and updating software packages).

6. Viewing system logs and checking for errors or warnings.

7. Running basic system health checks (CPU, memory, disk space).

8. Restarting services or daemons.

9. Monitoring system performance using basic tools (top, df, free).

10. Running basic commands to gather system information (uname, hostname, ifconfig).

14 L2 Tasks

1. Intermediate user account management (setting permissions, managing groups).

2. Configuring network interfaces and troubleshooting network connectivity issues.

3. Managing file system permissions and access control lists (ACLs).

4. Performing backups and restores of files and directories.

5. Installing and configuring system monitoring tools (Nagios, Zabbix).

6. Analyzing system logs for troubleshooting purposes.

7. Configuring and managing software repositories.

8. Configuring and managing system services (systemd, init.d).

9. Performing system updates and patch management.

10. Monitoring and managing system resources (CPU, memory, disk I/O).

15 L3 Tasks

1. Advanced user account management (LDAP integration, single sign-on).

2. Configuring and managing network services (DNS, DHCP, LDAP).

3. Configuring and managing storage solutions (RAID, LVM, NFS).

4. Implementing and managing security policies (firewall rules, SELinux).

5. Implementing and managing system backups and disaster recovery plans.

6. Configuring and managing virtualization platforms (KVM, VMware).

7. Performance tuning and optimization of system resources.

8. Implementing and managing high availability solutions (clustering, load balancing).

9. Automating system administration tasks using scripting (Bash, Python).

10. Managing system configurations using configuration management tools (Ansible, Puppet).

16 L1-L2 Transition Tasks

1. Learning basic shell scripting for automation tasks. 2. Understanding file system permissions and ownership. 3. Learning basic networking concepts (IP addressing, routing). 4. Learning how to use package management tools effectively. 5. Familiarizing with common Linux commands and utilities. 6. Understanding basic system architecture and components. 7. Learning basic troubleshooting techniques and methodologies. 8. Familiarizing with basic security principles and best practices. 9. Learning how to interpret system logs and diagnostic output. 10. Understanding the role and importance of system backups and restores.

17 L2-L3 Transition Tasks

1. Advanced scripting and automation techniques (error handling, loops).

2. Understanding advanced networking concepts (VLANs, subnetting).

3. Familiarizing with advanced storage technologies (SAN, NAS).

4. Learning advanced security concepts and techniques (encryption, PKI).

5. Understanding advanced system performance tuning techniques.

6. Learning advanced troubleshooting methodologies (root cause analysis).

7. Implementing and managing virtualization and cloud technologies.

8. Configuring and managing advanced network services (VPN, IDS/IPS).

9. Implementing and managing containerization technologies (Docker, Kubernetes).

10. Understanding enterprise-level IT governance and compliance requirements.

18 L3 Tasks and Beyond

1. Designing and implementing complex IT infrastructure solutions. 2. Architecting and implementing highly available and scalable systems. 3. Developing and implementing disaster recovery and business continuity plans. 4. Conducting security audits and vulnerability assessments. 5. Implementing and managing advanced monitoring and alerting systems. 6. Developing custom automation solutions tailored to specific business needs. 7. Providing leadership and mentorship to junior team members. 8. Collaborating with other IT teams on cross-functional projects. 9. Evaluating new technologies and making recommendations for adoption. 10. Participating in industry conferences, workshops, and training programs.

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

183+

Hands-On Lessons

Core Modules

10 weeks

Training Duration

100%

Practical Training

You don't just learn DATA SCIENCE WITH S-A-S TRAINING IN BANGALORE. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
10% Discounted Global Certification

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Global Certification

Radical Technologies is the leading IT certification institute in Bangalore, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.