## Basics of Data Science and Machine learning in SAS

**Course code: **DAS001

**Duration: **50 Hours

### About this course

This course is designed and delivered to a large extent with practice sessions. Data science goes beyond traditional analytics and statistical models and facilitates building predictive models. Participants will learn the concepts and motivation to apply data science and interdisciplinary programs. This also provides you the foundation skills to produce various types of predictive models for machine learning, the algorithms involved to build such models and how predictive modelling and machine learning are interrelated. The key aspect of this course is that, participants are exposed to sufficient amount of real time use cases, for each of the machine learning algorithms learnt, in order to analyse, conceptualize and build various types of predictive models, on their own.

### Audience

Students and Working Professionals who want to learn Data Science, Machine Learning & SAS. This is a foundation course in data science imparting necessary skills to begin working on machine learning projects.

### Prerequisites

- Understanding of basic mathematics and analytical thinking are required.

- Beginner level knowledge in programming with SAS software is required.

### Course Objectives

This course provides the foundation knowledge needed in analytics and data science:

- - Introduction to analytics and data science and their applications today

- - Using SAS software and its facilities

- - Data manipulation using SQL

- - Usage of inbuilt SAS macros

- - Basic statistics and matrices

- - Various Machine learning algorithms

- - Predictive models and Time series

- - Project works for applying each of the machine learning algorithms learnt.

### Course Curriculum

### Part 1. Analytics Overview

- Definition of Analytics
- Types of Analytics
- Analytics Problem Types
- Widely used tools and analytical techniques

### Part 2. SAS Introduction

- Introduction of GUI
- Library statement, understanding of PDV
- Import / Export of Data
- Variable Attributes
- Basic Procedures

### Part 3. Combining/Modifying Datasets

- Combining Data Sets Methods
- Concatenation
- Interleaving
- One to One Reading
- One to One Merging
- Data Manipulation steps and tools
- SAS Functions and Procedures for Data Manipulations

### Part 4. PROC SQL

- Introduction and Advantages
- Options and Syntax - Understanding of Select Statement
- Joins in SQL
- Merge vs. Join

### Part 5. SAS Macros

- Need for SAS Macros
- Macro Variables
- Automatic Macro Variables
- User-defined Macro Variables
- Macro Functions
- SYMBOLGEN System Option
- SQL Clauses for Macros
- The %Macro Statement

### Part 6. Basic Statistics

- Descriptive Statistics
- Inferential Statistics
- Hypothesis testing
- Non parametric tests

### Part 7. Basic Statistical Procedures

- PROC UNIVARIATE
- PROC MEANS
- PROC FREQ
- PROC CORR
- PROC REG
- PROC ANOVA

### Part 8. Data Exploration

- Data Preparation
- Data Type Conversion
- Missing Value Treatment
- Summarizing Data

### Part 9. Advanced Statistical Techniques

- Clustering Introduction
- Clustering Methodology
- Data Preparation
- K Means Clustering
- Cluster Profiling
- Decision Trees Introduction
- Creating Decision Trees
- Linear Regression Introduction
- Linear Regression in SAS
- Logistic Regression Introduction
- Logistic Regression in SAS

### Part 10. Working with Time series Data

- Reading Simple Time Series
- Dating time series and working with SAS date and date time values
- Sub setting data and selecting observations
- Storing time series data in SAS data sets
- Plotting, transforming, transposing, interpolating

### Part 11. Course review and evaluation

- Course review
- Review on project works.