Base SAS (Expert)
About Base SAS
SAS is the leader in business analytics software and services.
Base SAS provides a scalable, integrated software environment specially designed for data access, transformation and reporting. It includes a fourth-generation programming language; ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics and report writing; and a powerful macro facility that reduces programming time and maintenance headaches.
This foundation course focuses on the following key areas:
- Reading raw data files and SAS data sets
- Investigating and summarizing data by generating frequency tables and descriptive statistics
- Creating SAS variables and recoding data values, sub setting data
- Combining multiple SAS files
- Creating listing, summary, HTML, and graph reports
What we offer
Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Classroom training with flexible timing
Unlimited access to exclusive Study Materials on Cloud
Base SAS Certification – We guide the students to appear for Global Certification program.
Chapter-1: Introduction to Analytics and Role of SAS
- Introduction to Analytics
- SAS in Data Manipulation
- SAS in Data Presentation
- SAS in advanced analytics
- SAS Certification
Chapter-2: Introduction to SAS
- SAS software latest version ,installation and system requirements
- Introduction to SAS, GUI
- Different components of SAS language like PROC SQL
- SAS programming windowing Envirournment
- Concept of SAS Data Libraries and Creating Libraries
- Variable and Attributes – (Name, Type, Length, Format, In format, Label)
- Importing Data and Entering data manually
Chapter-3: Understanding Datasets
- Descriptor Portion of a Dataset
- Data Portion of a SAS Dataset
- Variable Names and Values
- SAS Data Libraries
- SAS Terminologies
Chapter-4: Accessing Data by Using Data Step
Data Step processing
- Data step execution
- Compilation and execution phase
- Looking behind the scene
- Input buffer and concept of PDV
Chapter-5: Importing Raw Data files
- Column Input ,Formatted input and List Input
- 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
Chapter-6: Importing and Exporting Data (Fixed Format / Delimited)
- Import Wizard
- Proc Import / .txt,.csv,xlsx…..so on
- Proc Export / Exporting Data from SAS
- 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
Chapter-7: Data Understanding, Managing & Manipulation
Understanding and Exporing Data
- Introduction to basic Procedures – Proc Contents, Proc Print
- 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
- SAS Labels, Commenting
- SAS System Options (OBS, FSTOBS, NOOBS etc…)
- 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
Chapter-8: Combining Datasets (Appending and Merging)
- One To One Merging
- Match Merging
- IN = Controlling SAS merge and Indicator
Chapter-9: Various SAS Functions for Data Manipulation
- Functions for Arithmatic operations
- Functions for Date and Time
- Functions for Text Manipulation
- Functions for Nested Functions
Chapter-10: Data Analysis and Reporting by Using SAS Procedures
Basic Statistics using SAS
- Types of Data
- Summarizing Data in Tables
- Frequency Distribution
- Grouped Frequency Distribution
- Cumulative Frequency Distribution
- Summarizing Data in diagrams using
Barplot,Histogram,Boxplot,Stem and Leaf Diagram,Dotplot/Lineplot
- Introduction to Descriptive Statistics and Measure of Dispersion
Statistical Proficiecncy using SAS
- Correlation and Regression
- Types of linear Correlation
- Correlation Coefficient
- Line of Best Fit
- Regression Line
Summary and Statistical Reports
- Proc Format to create user defined formats
- Proc Means
- Proc Summary
- Proc tabulate
- Proc report
- Concept of the Output Delivery System for generating HTML,PDF and RTF Files
Chart and Graphical Reports
- ProcGchart for Producing Bar and pie Chart(3D)
- PROC GPLOT for Producing Plots
Chapter-11: Advance Statistical Proficiency with SAS
- Introduction to Clustering
- Hierarchical Clustering
- Non-Hierarchical Clustering (K means Clustering)
- Simple and Multiple Linear Regression
- SAS Logistic Regression
Chapter-12: Advanced SAS Procedures
- Proc Surveyselect
- Proc Transpose
- Proc Rank
- Proc Corr
- Proc Univariate
- PROC CLUSTER
- PROC FASTCLUS
- PROC REG
- PROC LOGISTIC
We have various case studies based on different industries. You can choose the case study as applicable for you.
Case Study 1: Regression Analysis
How to assess if you are paying correct price or not while buying a property?
Price is very important function for any business. Correct price can create a real gap between profit and loss. In this case study we will take an example of property pricing to gain a deeper understanding of regression analysis.
Step – 1: Data Preparation
A. Checking the outlier
B. Checking Missing Values and how to treat them.
C. Basic bivariate and univariate analysis i.e. checking correlations, how the variables are distributed.
Step – 2: Principle Component Analysis
Step – 3: Traditional Regression Analysis with variable selection
Case Study 2: Marketing Analytics
Being a key decision and strategy maker on an online retail store that specializes in apparel and clothing, how by establishing analytics practice opportunity to improve PnL could be figured out. Background of behavioural analytics – How human brains follow involuntary pattern (behave like other similar people around them) and the detection of the pattern is preciously the idea behind marketing analytics.
Step – 1: EDA – Exploratory Data Analysis
A. Exploring different patterns i.e. distribution of the customers across the number of product categories purchased by each customer.
B. Why the customers buying different product categories
C. Categorization of customers based on the # of product category they purchased.
D. Which category is contributing highest sales?
Step – 2: Association Analysis
E. Support/Confidence/Lift – Apriori concept
F. Market Basket Analysis
Step – 3: Customer Segmentation
Case Study 3: Score Card ModelLing
Given the on-going turmoil on credit markets, a critical re-assessment of credit risk modelling approaches is more than ever needed. This modelling approach generates some probability of default score for each customer on basis of some collection of independent variables (it may differ as per business requirements). After that it is usable for predictive modelling, MIS reporting etc.
Step – 1: EDA – Exploratory Data Analysis
A. Data import and basic data sanity check.
B. Exploring different patterns i.e. distribution of data
C. Variables (categorical & numerical) selection approaches.
D. Training and validation data creation.
Step – 2: Model Preparation
E. Creating indicator variables
F. Apply step wise regression
Step – 3: validation of model
G. Check for multi Collinearity (using correlation matrix, VIF)
H. Generate Score using logistic regression.
I. KS calculation
J. Coefficient validation, coefficient stability and score stability.
Case Study 4: Web Scrapping & Text Analysis
The rapid growth of the World Wide Web over the past two decades tremendously changed the way we share, collect, and publish data. Firms, public institutions, and private users provide every imaginable type of information and new channels of communication generate vast amounts of data on human behavior. Web scrapping is a process to extract data from websites and applying some text analysis algorithms to analyze these data. Twitter analysis, google data analysis etc.
Step – 1: Setup connection
A. Create a key against developer account.
B. Run API request to fetch data.
Step – 2: Data Extraction
C. Save API requested data into excel/csv.
D. Data analysis and sanity check (dealing with missing data)
Step – 3: Text mining
E. Apply diff-2 algorithms like: sentiment analysis.
In-house Faculty (R & SAS)
Other than that she has worked on various other data analysis projects like, Data Analysis on US Economic Indices, Twitter Sentimental Analysis, GDP rates etc.
Simultaneously with project work, she provides training on Big Data analytics using Hadoop and R, Base SAS & Advanced SAS. She has already educated over hundred high profile MNC professionals on Data Analytics. She is the most junior but most appreciated faculty of our team.
In-house Faculty/Consultant (SAS, Python)
In-house Faculty (R & SAS)
In-house Faculty/Consultant (SAS, Python,R,SPSS)