Data Analytics using SAS Course

|| Data Analytics using SAS Course for Indore

The Data Analytics Using SAS course at BIT Indore is meticulously designed to empower learners with the analytical skills needed to drive data-informed decision-making using the robust capabilities of SAS software. This comprehensive program starts by introducing students to the fundamentals of SAS programming, including data input methods, data manipulation, dataset merging, and descriptive statistics. Through interactive sessions and practical assignments, learners quickly become comfortable navigating the SAS environment and using its versatile tools to explore and manage data effectively.

Moving beyond the basics, the course delves into advanced topics such as hypothesis testing, linear and logistic regression, multivariate analysis, and decision trees. Special emphasis is placed on real-world application, enabling students to use statistical models to solve complex business problems. Participants also learn to work with SAS Enterprise Guide and SAS Visual Analytics, providing them with valuable experience in building dashboards, visualizing data trends, and generating actionable insights that can influence organizational strategies.

Throughout the course, learners engage in hands-on projects and case studies drawn from various sectors including banking, healthcare, telecom, and e-commerce. These projects not only reinforce technical learning but also develop critical thinking and analytical storytelling. BIT Indore ensures that students receive expert guidance from seasoned instructors, access to the latest tools, and preparation for industry-recognized SAS certifications. Upon completion, participants emerge as skilled analytics professionals ready to harness data for strategic advantage across a wide range of industries.

|| Choose Data Analytics Using SAS Course From BIT

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|| What will I learn?

Students will gain a solid understanding of the SAS programming language, including syntax, data structures, and procedural logic.
Students will be able to generate descriptive statistics, frequency distributions, and graphical visualizations to summarize and explore datasets effectively.

Students will demonstrate competence in creating informative and visually appealing graphical representations of data using SAS graphical procedures.
Students will engage in collaborative learning activities, demonstrate professionalism in their work, and adhere to ethical standards in data analysis and reporting.

|| What will I learn?

Students will gain a solid understanding of the SAS programming language, including syntax, data structures, and procedural logic.
Students will be able to generate descriptive statistics, frequency distributions, and graphical visualizations to summarize and explore datasets effectively.
Students will demonstrate competence in creating informative and visually appealing graphical representations of data using SAS graphical procedures.
Students will engage in collaborative learning activities, demonstrate professionalism in their work, and adhere to ethical standards in data analysis and reporting.

|| Requirements

Basic understanding of statistics and probability

Familiarity with programming concepts (preferably in any language)

|| Requirements

Basic understanding of statistics and probability
Familiarity with programming concepts (preferably in any language)

The "Data Analytics Using SAS" course offers a comprehensive exploration of SAS programming for data analytics. The course begins with an introduction to SAS, covering its components and fundamental role in data analysis. Participants will learn to navigate the SAS interface, write basic SAS programs, and understand the structure of SAS data sets. Subsequent modules delve into data manipulation and management, covering techniques for importing data from various sources, cleaning and transforming data, and exploring data through descriptive statistics and visualization. Statistical analysis is a core component of the course, with topics including hypothesis testing, regression analysis, and advanced statistical methods like survival analysis and multivariate analysis. Participants will also learn macro programming for automating tasks and SQL for data manipulation. Efficiency and performance tuning techniques are covered to optimize SAS code, and participants will learn to generate reports and customize output using SAS procedures and the Output Delivery System (ODS). The course includes case studies and projects to apply learned concepts to real-world data problems, ensuring participants gain practical skills in SAS programming and data analysis.

Introduction to SAS:
Overview of SAS software and its components
Understanding the SAS programming environment (SAS Studio, SAS Enterprise Guide)
Basics of SAS syntax and structure

Data Step Programming:
Reading and importing data into SAS datasets
Working with SAS datasets (creating, modifying, appending, merging)
Data manipulation techniques (sorting, subsetting, conditional statements)

SAS Functions and Formats:
Overview of SAS functions (numeric, character, date and time)
Working with SAS formats (DATE, DOLLAR, PERCENT, etc.)
Customizing formats and informats

Data Cleaning and Validation:
Identifying and handling missing values
Data validation techniques (range checks, consistency checks)
Handling errors and invalid data

Basic Statistical Analysis:
Descriptive statistics (mean, median, mode, standard deviation, etc.)
Frequency analysis (PROC FREQ)
Summary statistics (PROC MEANS)

SAS Procedures (PROCs):
Introduction to SAS procedures and their use
Common PROCs for data analysis (PROC PRINT, PROC SORT, PROC SUMMARY)
Generating reports with PROC REPORT and PROC TABULATE

Introduction to SAS Macro Language:
Overview of SAS macro language and macro variables
Creating and using macros
Automating repetitive tasks with macros

Data Visualization with SAS:
Creating basic graphs and charts (PROC GPLOT, PROC GCHART)
Customizing graphs (titles, labels, legends)
Introduction to ODS (Output Delivery System) for generating reports

Advanced Data Manipulation Techniques:
Combining datasets (SET, MERGE)
Working with character data (functions, concatenation)
Advanced data manipulation techniques (arrays, do-loops)

Debugging and Error Handling:
Identifying and fixing common programming errors
Using SAS log and output to debug programs
Error handling techniques

Introduction to SQL in SAS:
Basics of SQL (SELECT, FROM, WHERE)
Using SQL to manipulate and query SAS datasets
Combining SAS and SQL for data analysis

Real-world Case Studies and Projects:
Applying SAS programming skills to solve real-world data analysis problems
Hands-on projects to reinforce learning and practical skills

Advanced Data Manipulation Techniques:
Advanced conditional processing (IF-THEN/ELSE statements, WHERE statements)
Subsetting data with advanced techniques (DO loops, arrays, SQL)
Advanced sorting and merging techniques (complex sorting, one-to-many merging)

SAS Macro Language Mastery:
Macro variables and macro functions
Advanced macro programming techniques (macro loops, macro arrays)
Macro debugging and error handling

Advanced Statistical Analysis:
Advanced statistical procedures (PROC UNIVARIATE, PROC REG, PROC LOGISTIC)
Multivariate analysis techniques (factor analysis, cluster analysis)
Time series analysis (PROC TIMESERIES, PROC ARIMA)

Efficiency and Performance Optimization:
Improving program efficiency (techniques for reducing memory usage and CPU time)
Performance optimization tips and tricks (indexing, compression)
Performance monitoring and benchmarking

Advanced Reporting and Output Delivery:
Customizing and enhancing reports (advanced formatting, conditional highlighting)
Creating interactive reports (ODS HTML, ODS PDF, ODS EXCEL)
Output delivery customization with ODS destinations

Advanced SQL in SAS:
Advanced SQL techniques (subqueries, joins, set operators)
Performance optimization in SQL queries
Combining SAS and SQL for advanced data manipulation and analysis

Data Quality and Validation:
Data validation techniques for advanced data structures
Handling complex data quality issues (data profiling, error detection)
Advanced data cleansing and transformation techniques

SAS Integration with External Systems:
Integrating SAS with databases (PROC SQL, LIBNAME)
Accessing and processing external data sources (Excel, CSV, XML)
Using SAS with other programming languages (Python, R)

Advanced Topics in SAS Programming:
Handling large datasets (SAS data set options, compression techniques)
Parallel processing with SAS Grid Computing
Integrating SAS with cloud platforms (SAS Viya)

Real-world Case Studies and Projects:
Applying advanced SAS programming skills to solve complex real-world problems
Hands-on projects and exercises to reinforce learning and practical skills

Introduction to Data Analytics and SAS:
Overview of data analytics concepts and methodologies
Introduction to SAS software and its capabilities for data analysis
Understanding the SAS programming environment (SAS Studio, SAS Enterprise Guide)

Data Preparation and Management:
Data import/export techniques (reading and writing data from/to external sources)
Data cleaning and preprocessing (handling missing values, outliers, and inconsistencies)
Data transformation techniques (variable creation, recoding, and restructuring)

Exploratory Data Analysis (EDA):
Descriptive statistics (summary statistics, frequency distributions, measures of central tendency and dispersion)
Data visualization techniques (scatter plots, histograms, box plots, heatmaps)
Exploring relationships between variables (correlation analysis, cross-tabulations)

Statistical Analysis with SAS:
Hypothesis testing (t-tests, ANOVA, chi-square tests)
Regression analysis (simple linear regression, multiple regression)
Non-parametric methods (Wilcoxon signed-rank test, Kruskal-Wallis test)

Predictive Modeling:
Introduction to predictive analytics concepts
Building predictive models using SAS (logistic regression, decision trees, neural networks)
Model assessment and validation techniques (ROC curves, confusion matrices, cross-validation)

Time Series Analysis:
Understanding time series data and its characteristics
Time series forecasting techniques (moving averages, exponential smoothing, ARIMA models)
Analyzing seasonality and trends in time series data

Advanced Analytics Techniques:
Cluster analysis (k-means clustering, hierarchical clustering)
Association rule mining (market basket analysis)
Text mining and sentiment analysis with SAS Text Miner

Big Data Analytics with SAS:
Introduction to SAS Viya and SAS Cloud Analytic Services (CAS)
Working with large-scale datasets using SAS Viya
Distributed computing and parallel processing with SAS Viya

Data Visualization and Reporting:
Creating interactive dashboards and reports with SAS Visual Analytics
Customizing visualizations and reports for effective communication of insights
Best practices for data visualization and storytelling with data

Case Studies and Practical Applications:
Applying SAS analytics skills to real-world datasets and scenarios
Hands-on projects and exercises to reinforce learning and practical skills

Ethical and Legal Considerations:
Ethical issues in data analytics (privacy, bias, transparency)
Legal regulations and compliance (GDPR, CCPA, HIPAA)

Introduction to Clinical Trials:
Overview of the clinical trial process (phases, design, regulations)
Role of SAS in clinical research and pharmaceutical industries
Understanding the importance of data integrity and regulatory compliance

Data Management in Clinical Trials:
Data collection methods (Case Report Forms, electronic data capture)
Data standards (CDISC standards - SDTM, ADaM)
Data cleaning and validation techniques specific to clinical data

SAS Programming for Clinical Trials:
Introduction to SAS programming in the context of clinical trials
SAS datasets specific to clinical trials (SDTM, ADaM)
Creating and managing clinical trial datasets using SAS

Clinical Data Analysis:
Descriptive statistics for clinical data (summary statistics, frequency tables)
Statistical analysis for clinical trials (t-tests, ANOVA, survival analysis)
Handling time-to-event data (Kaplan-Meier curves, Cox proportional hazards model)

Safety and Efficacy Analysis:
Adverse event analysis (AE/SAE reporting)
Efficacy endpoint analysis (response rates, time-to-event endpoints)
Safety and efficacy tables, listings, and figures

Clinical Trial Reporting:
Generating clinical study reports (CSRs) using SAS
Creation of analysis datasets and outputs for regulatory submissions
Automation of reporting processes using SAS macros and procedures

Advanced Topics in Clinical Data Analysis:
Advanced statistical techniques for clinical trials (mixed models, non-parametric methods)
Handling missing data in clinical trials
Meta-analysis of clinical trial data
Regulatory Compliance and Good Clinical Practice (GCP):
Understanding regulatory requirements (FDA, EMA, ICH guidelines)
Ensuring compliance with GCP principles
Documentation and audit trails in clinical trials

CDISC Standards Implementation:
Overview of CDISC standards (SDTM, ADaM, CDASH)
Converting raw data to SDTM and ADaM datasets using SAS
Validation and quality control of CDISC datasets

Case Studies and Hands-on Projects:
Applying SAS skills to analyze and report on real-world clinical trial data
Hands-on exercises and projects to reinforce learning and practical skills

Industry Trends and Emerging Technologies:
Emerging trends in clinical research and data analysis
Integration of SAS with other technologies (R, Python, cloud computing)

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|| Scope of Data Analytics Using SAS in India

The scope of the "Data Analytics Using SAS " course in India is promising, considering the increasing demand for skilled data analysts and SAS programmers across various industries. Here's why:

Growing Demand for Data Analytics: With the proliferation of data in every sector, there's a rising demand for professionals who can effectively analyze and derive insights from this data. Industries such as banking, finance, healthcare, retail, and e-commerce are actively seeking skilled data analysts.
Preferred Tool in Industry: SAS (Statistical Analysis System) is widely used in industries globally, including India, for its robust capabilities in data management, analytics, and reporting. Many organizations, especially in sectors like banking, insurance, and pharmaceuticals, rely on SAS for their analytical needs.
Government and Healthcare Sectors: In India, government initiatives such as Smart Cities, Digital India, and healthcare reforms generate vast amounts of data. SAS skills are valuable in analyzing this data to make informed decisions and policy recommendations.
Career Opportunities: Completion of the course opens up various career opportunities in roles such as Data Analyst, Business Analyst, Statistical Analyst, SAS Programmer, Research Analyst, and Data Scientist. These roles are in demand not only in IT companies but also in consulting firms, research organizations, and government agencies.
Salary Potential: Skilled professionals with expertise in SAS programming and analytics command attractive salaries in the Indian job market. Salaries vary depending on factors such as experience, location, and industry, but they generally reflect the high demand for these skills.
Continuous Learning and Development: The field of data analytics is dynamic, with new techniques and technologies emerging regularly. Continuous learning and upskilling are essential to stay relevant in this field. Completing the "Analytics Using SAS Programming" course equips individuals with a strong foundation to pursue further specialization and career advancement in data analytics.

Overall, the "Analytics Using SAS Programming" course offers promising career prospects in India, given the country's growing focus on leveraging data for strategic decision-making across industries.

|| Data Analytics using SAS Career option & opportunities in India

In India, pursuing a career in analytics offers a wide range of options and abundant opportunities across various industries. Here's a glimpse into some of the prominent career options and opportunities in the field of analytics:

Data Analyst: Data analysts are responsible for collecting, processing, and analyzing data to derive insights and inform business decisions. They work with large datasets using tools like SAS, SQL, Python, and R. Industries such as e-commerce, finance, healthcare, and telecommunications actively seek data analysts.
Business Analyst: Business analysts bridge the gap between IT and business objectives by interpreting data trends and patterns to provide actionable insights. They collaborate with stakeholders to identify business needs, develop strategies, and optimize processes.
Data Scientist: Data scientists possess advanced skills in statistical analysis, machine learning, and programming to extract insights from complex datasets. They build predictive models, develop algorithms, and implement data-driven solutions to solve business challenges. Data scientists are in high demand in sectors like technology, finance, and healthcare.
SAS Programmer: SAS programmers specialize in using SAS software for data management, statistical analysis, and reporting. They write and debug SAS code, create custom macros, and develop efficient solutions for data processing and analysis. Industries such as banking, pharmaceuticals, and market research rely on SAS programmers for their analytical needs.
Business Intelligence (BI) Developer: BI developers design and maintain data warehousing solutions, dashboards, and reporting tools to support decision-making processes. They extract data from various sources, perform ETL (extract, transform, load) operations, and visualize insights using BI platforms like Tableau, Power BI, and QlikView.
Marketing Analyst: Marketing analysts leverage data to optimize marketing campaigns, customer segmentation, and product strategies. They analyze consumer behavior, market trends, and campaign performance to identify opportunities for growth and improve ROI.
Risk Analyst: Risk analysts assess and mitigate financial, operational, and strategic risks faced by organizations. They use statistical models and predictive analytics to evaluate credit risk, market risk, and operational risk, helping companies make informed decisions to minimize losses.
Consultant: Analytics consultants work with clients across industries to solve complex business problems and drive strategic initiatives. They provide expertise in data analysis, predictive modeling, and decision support, helping organizations improve efficiency, reduce costs, and gain competitive advantage.

Opportunities for analytics professionals in India are vast and diverse, with industries increasingly recognizing the importance of data-driven decision-making in today's competitive landscape. As organizations continue to invest in analytics capabilities, the demand for skilled professionals with expertise in data analysis, statistical modeling, and programming is expected to grow significantly. Additionally, advancements in technology, such as big data, artificial intelligence, and machine learning, are opening up new avenues for innovation and career advancement in the field of analytics.

|| Skill to Master & Salary

Job Roles Analytics using SAS Programming skills to master , data analytics using sas ,data step programming ,proc sql ,functions and operators ,macro programming

|| Salary for Data Analytics using SAS in India

Sure, here's a breakdown of average salaries for SAS analytics professionals based on experience levels:

Entry-Level: Entry-level positions for individuals with SAS analytics skills typically include roles such as Data Analyst, Junior Data Scientist, or Analytics Associate. The average salary for entry-level positions can range from 4-8 lakhs per year.
Mid-Level: Mid-level positions may include titles such as Data Scientist, Analytics Consultant, or Business Intelligence Analyst. With a few years of experience, professionals in these roles can expect average salaries ranging from 8-15 per year.
Senior-Level: Senior-level roles in SAS analytics often include positions such as Senior Data Scientist, Analytics Manager, or Director of Analytics. Salaries for senior-level professionals can vary greatly depending on factors such as years of experience, managerial responsibilities, and the size of the organization. On average, senior-level salaries can range from 20++ lakhs per year or more.

It's important to note that these salary ranges are approximate and can vary based on factors such as geographic location, industry, company size, and individual qualifications. Additionally, salaries may continue to evolve over time, so it's a good idea to stay updated with current job market trends and salary data.

|| Data Analytics using SAS holds a Prominent Position in Indian Job Market

In India, the demand for SAS analytics professionals spans across diverse industries, offering ample placement opportunities for skilled individuals. Within the realm of IT services, both multinational corporations and domestic firms maintain robust analytics practices, providing roles such as Data Analyst, Business Analyst, and SAS Developer. Consulting firms offer further avenues, deploying SAS experts on projects across sectors, from banking and finance to healthcare and retail. The banking and financial services sector, in particular, relies heavily on analytics for risk assessment, credit analysis, and fraud detection, creating roles like Risk Analyst and Financial Analyst. Similarly, healthcare and pharmaceutical companies increasingly seek SAS talent for clinical trials analysis and healthcare operations management. E-commerce and retail sectors leverage SAS for consumer behavior analysis and supply chain optimization, offering positions like Market Analyst and Supply Chain Analyst. Even government agencies and public sector organizations are embracing analytics for better governance and service delivery, opening up opportunities in social welfare analytics and urban planning. Overall, India's dynamic job market provides a wealth of opportunities for SAS analytics professionals across various industries and domains.

|| Empowering Your Career Transition From Learning To Leading

Divyanshi Suryawanshi

5 View LinkedIn

Divyanshi Suryawanshi, a proud BIT graduate, is excelling as a Data Analyst at Tacttree LLP. Her expertise in data analysis and problem-solving, built through BIT's strong focus on technical and software skills, showcases how the institute prepares students for success in data-driven industries beyond traditional engineering roles.

Dixita Vandra

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Dixita Vandra, a proud BIT graduate, is successfully working as a Customer Service Associate at The Home Depot. With strong communication skills and a professional approach nurtured at BIT, she exemplifies how the institute prepares students for global opportunities, combining technical knowledge with real-world readiness across diverse career paths.

Shivam Uttekar

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Shivam Uttekar, a proud BIT graduate, is excelling as a Data Analyst at Tacttree LLP. BIT's strong focus on technical training and software proficiency has enabled students like Shivam to succeed in data-centric roles, proving that the institute prepares graduates not just for mechanical careers but for dynamic opportunities across various industries.

Duhita Tandon

5 View LinkedIn

Duhita Tandon, a proud BIT graduate, is making great strides as a Data Analyst at Gavit e-Services. With a solid foundation in data handling, analysis, and software skills built at BIT, she is well-equipped for the data-driven industry. BIT continues to empower students with relevant technical expertise and practical skills beyond core mechanical engineering, ensuring career success in diverse fields.

Rajvi Suthar

5

Rajvi Suthar, excelling as a Data Analyst at Tata Consultancy Services (TCS), leverages unique tools such as Python for scripting, R for statistical analysis, and Alteryx for data blending. Her adept use of these cutting-edge tools contributes to efficient and advanced data analysis solutions.

|| Some prominent companies in India that use Data Analytics using SAS

Several companies in India offer courses in analytics to enhance the skills of their employees or to recruit new talent with the necessary expertise. Here are some notable companies that provide analytics courses in India:

Accenture: Accenture offers a range of training programs and certifications in analytics and data science through its Accenture Academy. These courses cover various topics such as predictive analytics, machine learning, and big data technologies.
Tata Consultancy Services (TCS): TCS provides comprehensive training programs in analytics and data science through its TCS iON Digital Learning Hub. These courses cater to individuals at different skill levels and cover topics such as data visualization, statistical analysis, and business analytics.
Infosys: Infosys offers training in analytics and data science through its Infosys Learning Platform. These courses focus on practical skills development and cover areas such as data mining, predictive modeling, and advanced analytics techniques.
Wipro: Wipro offers analytics training programs through its Wipro Academy of Software Excellence (WASE). These programs cover a wide range of topics including data analytics, machine learning, and artificial intelligence.
IBM: IBM provides training in analytics and data science through its IBM Skills Gateway. These courses are designed to build expertise in areas such as data visualization, data mining, and predictive analytics using IBM tools and technologies.
Genpact: Genpact offers analytics training programs through its Genpact Analytics Academy. These programs focus on developing skills in areas such as data analysis, statistical modeling, and business intelligence.
Mu Sigma: Mu Sigma offers training programs in analytics and decision sciences through its Mu Sigma University. These programs cover topics such as data analysis, machine learning, and optimization techniques.
Cognizant: Cognizant provides analytics training programs through its Cognizant Academy. These programs cover a wide range of topics including data analytics, predictive modeling, and business intelligence.
Capgemini: Capgemini offers analytics training programs through its Capgemini University. These programs focus on developing skills in areas such as data visualization, statistical analysis, and predictive modeling.
Deloitte: Deloitte provides analytics training programs through its Deloitte Analytics Institute. These programs cover topics such as data analysis, machine learning, and predictive modeling using Deloitte's proprietary methodologies.

These are just a few examples of companies in India that offer analytics courses either internally for their employees or externally for professionals seeking to upskill in this field. Additionally, there are numerous educational institutions, online platforms, and specialized training providers in India that offer analytics courses to cater to the growing demand for skilled professionals in the field of data analytics and data science.

|| Top Hiring Companies

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|| Job Roles and Salary

DATA Step Programming

PROC SQL

Function and Operators

Macro Programming

Advanced DATA Step Techniques

Efficient DATA Handling

Descriptive Statistics

Inferential Statistics

0-2 years

2-5 years

5+ years

|| Get Data Analytics Using SAS Certification

Three easy steps will unlock your Data Analytics Using SAS Certification:

Finish the online / offline course of Data Analytics Using SAS Programming Course and the Assignment
Take on and successfully complete a number of industry-based Projects
Pass the Data Analytics Using SAS Programming Certification exam

The certificate for this Data Analytics Using SAS Programming Certification will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile.