MASTER PROGRAM WITH DATA SCIENCE:

The Data Science with Python course has been designed to provide in-depth knowledge of the various libraries and packages that are required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The data science with python course is based on the live projects, demonstrations, assignments, and the case studies to provide a hands-on as well as practical experience to the aspirants.

Moreover, the course insights on PROC SQL and other statistical procedures such as: PROC MEANS, PROC FREQ, etc. along with the advanced analytics techniques to have a clear vision of decision tree, regression and clustering.

The training on Data Science with R provides the skills required to work with real data sets and provide an opportunity to use data to provide data-driven strategic and tactical recommendations. This training will provide some insights on techniques such as linear and logistic regression, ANOVA, Segmentation, Ensemble models, SVM and machine learning in big data. In addition to technical skills, the program also allows students to build effective leadership and communication skills to advance their career upon graduation.

Data Science with SAS is an advanced technology in the world of data analytics. The training on Data Science with SAS would help the candidates to achieve their career goals by making them understand what PROC MEANS, PROC FREQ, PROC UNIVARIATE and PROC CORP are along with data analytics concepts  such as: clustering, decision tree, and regression and their implementation in their respective organizations. Moreover, fresher graduates would get a chance to learn how to handle real time issues and resolve them with the help of advanced SAS tools.

Hadoop Data Analytics training course explains how to apply data analytics and business intelligence skills to Big Data. This Big Data Analytics training lays emphasis on the usage of Apache Pig, Hive, and Cloudera Impala. It will drive you through the process of developing distributed processing of large data sets across clusters of computers and administering Hadoop. The participants will learn how to handle heterogeneous data coming from different sources. This data may be structured, unstructured, communication records, log files, audio files, pictures, and videos.