Data Analytics Programme is especially designed for B. Pharm, M. Pharm & Pharm D pursuing students. In this, we will cover all 7 important data analytics modules for 4 semesters on both SAS and Python platform to stand firmly in the analytic domain fields.
Sankhyana Consultancy Services is a premium and SAS Authorized Training Partner, providing in- house and live- web training on SAS and Data Management tools. With more than 3000+ Students and professionals trained till 2019, we are on a mission to enable aspirants and help bridge the Corporate demand and supply gap.
Advance SAS-This course is for SAS programmers who prepare data for analysis. The comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation. It focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with and without macro code are processed. It also covers how to process SAS data using Structured Query Language (SQL).
Clinical SAS-In this module, we cover topics like CDISC, SDTM, ADaM, Meta Data etc. which helps in strategic analyses, such as cross-study and advanced safety analysis.
SAS/Stats-This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum
Data Science with Python-This hands-on Python programming course shows how to rapidly develop and maintain effective Python programs. The course includes thorough coverage of Python syntax, built in data types and control constructs. The course takes a practical approach to creating and organizing Python programs using functions, packages, modules and classes as part of Python's object-oriented paradigm. Attendees will use regular expressions to rapidly process data captured from users and from the file system.