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.
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
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 1: Analytics with SAS+R
- Lecture 1: Introduction-20 Mnts
- Lecture 2: Getting Data in SAS-30 Mnts
- Lecture 3: Formats and Functions
- Lecture 4: Subsetting and Loops
- Lecture 5: PROC SQL
- Lecture 6: MacrosRegression
Section 2: Analytics with SAS+R
- Lecture 7: Introduction
- Lecture 8: Getting Data from Different Sources
- Lecture 9: Functions
- Lecture 10: Data Transformation
- Lecture 11: Restructuring
- Lecture 12: Looping
- Lecture 13: Graphics
Section 3: Analytics with SAS+R
- Lecture 14: Basic Statistics
- Lecture 15: Hyopthesis Testing
- Lecture 16: T-Test
- Lecture 17: ANOVA
- Lecture 18: Linear Regression
- Lecture 19: Logistic Regression
- Lecture 20: Cluster Analysis
- Lecture 21: Time Series
- Lecture 22: Random Forest
- Lecture 23: Sentimental Analysis