Base SAS

Course Features

Course Details

Course Objectives
After the completion of the Iteanz Base SAS course, you should be able to:
1. Understand how SAS works in the back-end. This is supremely important for writing good code
2. Explore data with SAS procedures
3. Apply various data mining techniques
4. Perform Data Exploratory Analysis
5. Learn where to use functions like PUT, INTNX, SUBSTR, INPUT, SUM, among others
6. Apply PROC UNIVARIATE, PROC CORR etc. to perform statistical analyses
7. Implement the concepts as-is in data analytics work
8. Work on a industry-relevant case study, implementing concepts of SAS to build a 360 degree view of the customer, required for deriving actionable insights

Who should go for this Course? 
This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for:
1. Analytics professionals who are keen to migrate to advanced analytics
2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist
3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics
4. Testing professionals to move towards creative aspects of data analytics
5. Mainframe professionals
6. Software developers and architects
7. Graduates aiming to build a career in Big Data as a foundational step

Why Learn Base SAS? 
The MyTectra Base SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts.
SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively.

What are the pre-requisites for this Course? 
Though not a pre-requisite, it would be highly beneficial if learners have reasonable level of proficiency in any data handling tool like MS Excel.

Which Case-Studies will be a part of the Course? 
Towards end of the course, you will get an opportunity to work on a live case study from the banking domain which happens to be one of the most data intensive industries.
Industry: Banking
Data: Customer-level, Accounts-level and Transaction level
Problem-statement: To build a Customer Analytics Record.
A unique feature of this project is that though it will be mimicked for a bank, it has much wider application across almost all the B2C (and even a few B2B) industries.

1. Introduction and Accessing data
Learning Objectives - This module will introduce you to SAS as a language. It will talk about the minimum set of rules SAS works with, about how to access data in SAS, and more.
Topics - Introduction: History, SAS Windows and it’s contents, SAS Studio on SAS University edition, The SAS language, SAS data sets Parts of a SAS program, Modes for submitting a SAS program, How SAS language works, DATA-STEP’s built-in loop, Program data vector (PDV), Getting your data into SAS, Temporary vs. permanent SAS data sets, Working with data, Creating and redefining variables.
2. Working with Data
Learning Objectives - Once we have created or accessed data in SAS, learn how to then how to work on/with it are the learnings In this module, we will understand SAS functions (there are more than 400 available, so we will focus on a selected few), and some specialized SAS options.
Topics - Creating and redefining variables revisited, SAS functions: NUMERIC, CHARACTER, Working with SAS dates, DATES, SAS formats, SAS informats, PUT & INPUT, Conditional variable creation.
3. Working with Data : Loops and Advanced topics
Learning Objectives - This module has vast literature. This module will talk about how to work on iterative steps/processes with the help of Loops. It will then talk about some advanced topics on working with data.
Topics - Working with data: LOOPS, DO statement (Loop), DO-UNTIL statement, DO-WHILE statement, Working with data: Advanced topics, Retain & Sum statements, Uni-dimensional ARRAYS, PROC SORT – an introduction, SAS Automatic variables
4. Combining Datasets
Learning Objectives - Real-life work scenarios will require an analyst to work with multiple data sets, requiring integration, vertically or horizontally.This module will help you understand how to combine 2 or more SAS datasets.
Topics - Appending datasets, Using SAS dataset option IN, Appending datasets using IN, Merging datasets, Merging scenarios through Venn diagram, Merging codes for 4 scenarios.
5. SAS Procedures
Learning Objectives - SAS houses in-built procedures or SAS programs helping the user save time in writing customized codes for frequently used programs. This module will formally introduce and familiarize the user through some of the most frequently used PROCs in the industry.
6. Basic stats using SAS
Learning Objectives - SAS is a data management as well as an advanced statistical tool. This module familiarizes learners with elementary statistical procedures around SAS.
Topics - Introductory Statistical PROCs, PROC CORR, PROC UNIVARIATE, PROC FREQ, Summarizing data using PROC MEANS, Merging summary statistics to Parent data.
7. Debugging and Introduction to Advanced SAS
Learning Objectives - If you cannot debug a code then you do not really know coding. So, this module will focus on some of the common mistakes made by SAS programmers. The module also provides guidelines and best practices for avoiding bugs, or even debugging them.
Topics - Debugging, Writing programs that work, Fixing programs that do not work, Introduction to Advanced SAS - Structured Query Language, Macros.
8. Recap and Case study
Learning Objectives - Learning a programming language without its extensive usage will not be effective. Hence, this module will recap the concepts learnt till module 6, and then cover the case study using toy-datasets mimicking the real-life industry challenges in details.
Topics - Concepts quickly revisited, Accessing data, Working with data, Variable creation, Dataset options, Sorting & Combining data , 3 Best practices when approaching a new dataset, Case study on BFSI data:, Problem statement: Building Customer Analytics Record
This course does not have any sections.

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