Welcome to Ancla Academy of Analytics - Knowledge Bank of Healthcare & Life Sciences
Best Seller

Healthcare & Life Science R Programming Training

This course will help you master the basics of this open source language including frames, lists and data frames. With the understanding you get from this course you will be able to undertake your own data analysis. Leverage the power of R by enrolling for this course today to become a R ninja.

20th Feb Sat & Sun(5 Weeks) Timings : 08:30 PM - 11:30 PM (IST) Action
Mar 03th Sat & Sun(5 Weeks)
Weeked Batch
Timings : 08:30 PM - 11:30 PM (IST)
Mar 07th Sat & Sun(5 Weeks)
Weeked Batch
Timings : 08:30 PM - 11:30 PM (IST)
Mar 12th Sat & Sun(5 Weeks)
Weeked Batch
Timings : 08:30 PM - 11:30 PM (IST)

Course Price

35,000.00

Course Details

  • Introduction to Big Data and Data Science Fundamentals
  • Data Science Life Cycle
  • Data scientist Role and Responsibilities
  • Molecular Biology, Including Genomic & Proteomics
  • General Biochemistry
  • Human Physiology & Anatomy: Major Systems
  • General Pathology
  • General Microbiology
  • General Pharmacology
  • Public Health – Epidemiology
  • GxP Regulation
  • Drug Development including Biosimilars
  • Pharmacovigilance & Toxicology
  • Clinical Data Management including programming & Biostats
  • Pharmaceutical Brand Management
  • Hospital Systems & Management
  • Health Economics & Outcome Research
  • Health Policy: A Global Perspective
  • Introduction to Data
  • Types of Data: A Healthcare and Life Sciences Perspective
  • Characteristics of Big data
  • Data Pre-Processing
  • Data Classifications
  • Introduction to Statistics
  • Descriptive Statistics
  • Prescriptive Statistics
  • Measures of Central Tendency
  • Distribution
  • Hypothesis Testing
  • Analysis of Variance
  • Probability
  • Summary of statistics
  • Data transformations
  • Outlier detections and management
  • Charts and graphs
  • Two dimensional and multidimensional charts

“Design thinking is a human-centered approach to innovation that draws from the designer's toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.” -Tim Brows

  • What is R?
  • What is S?
  • The S Philosophy
  • Back to R
  • Basic Features of R
  • Design of the R System
  • Limitations of R
  • R Resources
  • Installation
  • Getting Started with R interface
  • Entering Input
  • Evaluation
  • R Objects
  • Numbers
  • Attributes
  • Creating Vectors
  • Mixing Objects
  • Explicit Coercion
  • Matrices
  • Lists
  • Factors
  • Missing Values
  • Data Frames
  • Names
  • Summary
  • Reading and Writing Data
  • Reading Data Files with read.table()
  • Reading in Larger Datasets with read.table
  • Calculating Memory Requirements for R Objects
  • Using the readr Package
  • File Connections
  • Reading Lines of a Text File
  • Reading From a URL Connection
  • Sub-setting a Vector
  • Sub-setting a Matrix
  • Sub-setting
  • Sub-setting Nested Elements of a List
  • Extracting Multiple Elements of a List
  • Partial Matching
  • Removing NA Values
  • Vectorized Matrix Operations
  • Dates in R
  • Times in R
  • Operations on Dates and Times
  • if-else
  • for Loops
  • Nested for loops
  • while Loops
  • repeat Loops
  • next, break
  • Functions
  • Functions in R
  • Your First Function
  • Argument Matching
  • Lazy Evaluation
  • The Argument
  • Arguments Coming After the Argument
  • A Diversion on Binding Values to Symbol
  • Scoping Rules
  • Lexical Scoping: Why Does It Matter?
  • Lexical vs. Dynamic Scoping
  • Application: Optimization
  • Plotting the Likelihood
  • Looping on the Command Line
  • lapply()
  • sapply()
  • split()
  • Splitting a Data Frame
  • Tapply
  • apply()
  • Col/Row Sums and Means
  • Other Ways to Apply
  • mapply()
  • CONTENTS
  • Vectorizing a Function
  • Something’s Wrong!
  • Figuring Out What’s Wrong
  • Debugging Tools in R
  • Using traceback()
  • Using debug()
  • Using recover()
  • Using system.time()
  • Timing Longer Expressions
  • The R Profiler
  • Using summaryRprof()
  • Generating Random Numbers
  • Setting the random number seed
  • Simulating a Linear Model
  • Random Sampling
  • Synopsis
  • Loading & Processing the Raw Data
  • Results

Course Certification