Instructorstat mat
TypeOnline Course
DateSep 20, 2017
Student Enrolled1


The objective of this course is to master study of data science to become a successful data scientist. The course aims to equip the data scientist to successfully carry out data analysis to include tools for carrying out massive data management , statistical modelling and provide algorithm for data mining such as clustering and associate rule mining to name a few. The course primarily covers the complete range of SAS & R and machine language learning techniques as defined in the Data Science study.

Scope of the programme

After undertaking the course, one aims to achieve the proficiency in the following:

  • Understand the basic role played by the Data scientist in analyzing the Data Analysis Life cycle.
  •  Analyze Big data by the use of SAS and R statistically.
  • Learn Predictive Analytics, Machine Learning & Data mining Techniques
  • Insight in to various Machine Learning Techniques and their implementation using R.
  • Handling tools and techniques involved in sampling, filtering and data transformation

Who Should Enroll

The course is a blend of two major open source tools available viz.SAS and R language. The course is ideal for you if you are:

  • A Professional working on Database management and streaming of Big Data.
  • IT or Management student who are passionate about problem solving methodologies.
  • Professionals who are expert in their domain and strive to learn technology for business and technology integration.
Section 1SAS Programming
Lecture 2Getting Data in SAS-30 Mnts
Lecture 3Formats and Functions
Lecture 4Subsetting and Loops
Lecture 5PROC SQL
Lecture 6Macros
Section 2R Programming
Lecture 7Introduction
Lecture 8Getting Data from Different Sources
Lecture 9Functions
Lecture 10Data Transformation
Lecture 11Restructuring
Lecture 12Looping
Lecture 13Graphics
Section 3Analytics with SAS+R
Lecture 14Basic Statistics
Lecture 15Hyopthesis Testing
Lecture 16T-Test
Lecture 17ANOVA
Lecture 18Linear Regression
Lecture 19Logistic Regression
Lecture 20Cluster Analysis
Lecture 21Time Series
Lecture 22Random Forest
Lecture 23Sentimental Analysis