R for Data Analytics

Statistical computing is employed within a diverse range of industries. In recent years, an open source project, R, has emerged as the preeminent statistical computing platform. With its unsurpassed library of freely available packages, R is capable of addressing almost every statistical inference problem. In this course, you will learn the most commonly-used (roughly 100) functions and operators from the R Base Package, which serves as the fundamental tools for accessing data from multiple sources, manipulating different types of R objects, performing character manipulation, and...

Statistics allows us to collect, analyze, and interpret data. The R programming language is one of the most widely used tools for data analysis and statistical programming. Its easy-to-learn syntax, built-in statistical functions, and powerful graphing capabilities make it an ideal tool for learning and applying statistical concepts. In this course, you will master the most widely used statistical methods while also learning to design efficient and informative studies, perform statistical analyses using R, and critique the statistical methods used in published studies. The emphasis is on...

Data management and manipulation is an essential task for data scientists who deal with data on a day-to-day basis. In recent years, many R packages were developed to tackle a wide variety of data science challenges. The focus of this course is the tidyverse suite of packages, which contain a large variety of tools to efficiently manage complex and big data. The materials in this course are essential for developing robust and efficient R programs in the data science field. At the end of this course, students will be able to work independently to solve common data management tasks by...

One of the reasons the R language has become a popular for analytics is because of its powerful functionality in its plotting systems. One can easily generate a high-quality and publication-ready figure in R without having to put too much effort in adjusting the parameters of R graphic functions. In this course, students will learn how to generate different types of figures, such as histograms, bar plots, scatter plots, line plots, etc. In addition to exploring the different R plotting systems, we will focus on the ggplot2 plotting system, which contains the latest graphing techniques in...

Advanced Business Intelligence: Introduction to Predictive Analytics This course introduces the predictive modeling process and basics of predictive analytics for business applications, including hands-on introduction to data preparation, model identification and validation, model documentation, and interpretation of model results. Topics include: Explain predictive analytics key concepts and terms, benefits, and applications Understand the steps to creating and selecting a predictive model Comprehend the data mining process, including data collection or selection, data cleansing,...

Data Analytics Using Python In this course, you will learn the rich set of tools, libraries, and packages that comprise the highly popular and practical Python data analysis ecosystem. This course is primarily taught via screen sharing programming videos. Topics taught range from basic Python syntax all the way to more advanced topics like supervised and unsupervised machine learning techniques. Key topics: Installing Python/Jupyter/IPython on Windows and Mac Python Basics (variables, strings, simple math, conditional logic, for loops, lists, tuples, dictionaries, etc.) Using the Pandas...

Learn to Create Regulatory Compliance Deliverables for Pharmaceutical Industry: SDTMs, ADaMs, Tables, Lists and Graphs Participants will gain comprehensive knowledge on common R packages (Tidyverse, DPLYR and Piping) and the R programming language. Attendees will learn how to access, create and process R data frames in data management, reporting and analysis. In addition, this course shows best practices in how to select, filter, derive, append and join data frames using SQL code. Ideal for the pharma industry, examples of both SDTM and ADaM datasets will be created, which are important in...