Any scientific task without the knowledge of software is difficult to imagine and complete in the current scenario. R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built in functions to perform any specialized task. We intend to learn the basics of R software in this course. All industries involved in mathematical and statistical computations, programming and simulations and having R & D setup will use this R Programming course.

As we rightly know that R is a programming language and software environment used for statistical analysis, data modeling, graphical representation and reporting. The module activates the demonstration of how R is the best tool for software programmers, statisticians and data miners who looking forward towards excellence through this implementation in particular

Beginners Training on R Programming R is a programming language and related to software environment for statistical computing and graphics. R programming languages is mostly used in graphics, it also used mostly by statisticians for developing software’s. Through this course one will be learning about basic R functions, special numerical values, array and matrix, repository and packages, installing a package, how to calculate variance, co-variance, cumulative frequency, learn about statistics, probability and distribution, random examples, discrete example and many as such concept about R. Through this training you are going to learn the basics of R and how it can be used for data processing and data visualization to carry out exploratory analysis. Target Customers: Analytics professional Anyone interested in R Programming related Training Web developers People interested in statistics and data sciences Researchers Pre-Requisites: Passion to learn about R Programming Courses Computer ready to run R and RStudio Basic knowledge of statistics Computer with Internet Connection Statistics Essentials for Analytics – Beginners: Data and analytics is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this tutorial you are going to learn the basic statistical concepts that are important to data analytics and its application using R, SPSS and Minitab. The training will include the following; Module1: Introduction to basic elements of Statistics Module2: Measures of central tendency using R or Minitab Software Module3: Measure of Dispersion using R/Minitab Software Module4: Correlation and Simple linear regression using R programme Module5: Understanding of Normal Distribution using R programme Market Basket Analysis in R: Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. We are understanding the conceptual foundations of association analysis and perform market basket analyses. The training includes the following topics; 1. What is Market Basket analysis – Introduction – What Market Basket Analysis is not – Elements of MBA and key terminologies – Understanding Confidence and Support – Association rules – Examples of MBA – Applications 2. Case study – MBA for marketing campaign using R – Problem statement – Introduction to apriori algorithm in R – Deciding the support and confidence cutoffs – Executing MBA – Visualizing the results in R Data Visualization with R Shiny – Basic Tutorials: Data visualization is understanding the significance of data by placing it in a visual context. Patterns, trends that might go unnoticed in text-based data can be exposed and recognized easier with data visualization software. It basically involves presentation of data in a pictorial or graphical format. Through this training we are going to learn how to use R and Shiny to create fascinating data visualizations. The training will include the following; Introduction Web Development Shiny Resources Getting Started Structure of a Shiny App UI Server Reactive Programming Add-on Packages

Are you nervous or excited about learning how to code? Are you a beginner who wants to get better at learning R the right way? Would you like to learn how to make cool looking and insightful charts? If so, you are in the right place. Learning how to code in R is an excellent way to start . R is one of the top languages used by data scientists, data analysts, statisticians, etc. The best thing about it is its simplicity. R was introduced to me in the summer of 2008 as an intern at a marketing firm; since then, I have been a loyal user. Along with SAS, I use it daily to conduct data analysis and reporting. R is one of my top go-to tools. I start with the basics showing you how I learned it, and then I teach it at a pace comfortable for a beginner . We are living in exciting times, and the future looks bright for those skilled in programming . Industries are using data more and more to make crucial decisions. They need experienced analysts to help design data collection processes and to analyze it. Where do you fit in this picture now and tomorrow? Learning R sets you now and will sustain you for the future . R was designed mainly for statisticians or those who did not have a computer science background, hence its intuitiveness. R is a free and open-source programming language . It will not cost you anything to have R installed and running on your computer. R is open-source, meaning that contributors can improve its usability by creating packages. Packages contain functions to help users solve specific problems that R’s founders did not think of. It would be a pleasure to see you grow to become a contributor to R someday. Although R itself is mighty, it is not the best place to write R codes. We will write R codes (or scripts) in R studio. R studio is a powerful editor for R. You will learn all about it in this course. Here are some of the things you will learn in this course: 1. Download and install R and R studio 2. The different data structures, such as atomic vectors, lists, data frames, and tibbles. How to create and use them 3. How to import an excel or a CSV file into R 4. Create functions 5. How to execute chunks of code following an if-else logic 6. Lean R studio short cut keys to increase your efficiency and productivity 7. How to summarize data 8. How to transpose data from long format to wide format and backward 9. How to create powerful easy to read pipelines using purrr and dplyr packages 10. Introduction to base R plots 11. Ggplots 12. And more Thanks for taking the time to check out my course. I cannot wait to help you get started with R and R studio. If you have any questions, please message me or check out the free preview lecture to learn more.

Hi all this course is designed for elementary level students in R  who want to enjoy in learning R. This course is designed with simple examples for beginners and its created with my young research team who has worked on the material. you will get here : Introduction to R Installation Procedure of R Overview of R Data types in R Basic Data management in R Basic Flow control in R Basic Graphs in R Basic Statistics in R

What is this course about? This course helps student learn R syntax for Import / Enter / Viewing data and metadata in R Conduct Frequency Distribution Analysis / Univariate Analysis in R Create derived variables Merge / Append data sets Sort / Subset data sets Learn to substring variables Create cross tab analysis conduct Linear Regression analysis Practice case studies Terminology associated with the course R syntax Data Mining Analytics Machine Learning Material for the course 20 HD Videos Excel Data sets PDF of presentation R code How long the course should take? Approximately 4 hours to internalize the concepts How is the course structures Section 1 - explains how to get R, R Studio, Understand environment and data for workout Section 2 - explains the R syntax through examples Section 3 - explains some other syntax needed for working Section 4 - Practice Case Studies - apply your knowledge to solve business problems Why take this course? This course ensure quick learning in a simplified way. It explains the most important aspects of working on data and conduct analysis through example.

Practical Foundations of R Programming is the first course of a learning path that teaches critical foundation skills necessary to create quality code using the free and open-access R programming language. This course, and the courses that follow, are useful for both beginner and intermediate R programmers who want to understand the unique features of R and why "R works the way it does." I have been using, teaching, and writing applications in R for 6 years and have come to appreciate that R is a beautiful and elegant language that is especially well-suited for writing applications for data analytics, and for mathematical and statistical applications. Furthermore, R is superior in terms of inherent graphical data presentation capabilities that go hand-in-hand with exploring and understanding data relationships. Most introductory R courses, those that do not directly address sharpening one's R programming skills, first teach the important R data structures, then the basics of R functions, and generally the use of base R graphics capabilities. However, these introductory R courses are not targeted at the R programmer population, but rather at the general R user population. This course, Practical Foundations of R Programming , which contains all-unique material compared to my other Udemy R courses, addresses R data structures, R subsetting, and R functions, but from the focused perspective of someone who intends to write efficient higher-level applications using R. It is specifically intended to teach the most important foundation concepts and features of the R programming language which are necessary to understand to write efficient and effective applications in R. This course, which is exclusively "hands-on," demonstrates the construction and use of R code within the RStudio IDE, and focuses on the unique features of R that can make writing applications in R both a challenge and a delight. The course does not present a single power point slide and relies heavily on user exercises. In each of the three major sections of the course, (1) data structures, (2) subsetting, and (3) functions, there are multiple sets of within-section exercises, as well as a final end-of-section exercise set. Participants are encouraged to complete each set of exercises "on their own" before they view the videos that present the exercise solutions. All course videos, and all exercises, as well as their solutions, are presented within R scripts that are made accessible with the course materials. Anything and everything that you see me demonstrate and/or discuss in the 100+ course videos are available for you to download at the beginning of the course. The second course in this learning path, which should be available to you by the time you complete this first course, will delve more deeply into functional programming in R per se. The second course will have a similar format to this first course: all "hands-on" with extensive use of practical and relevant in-section, and end-of-section, exercises.

Nesse curso você vai aprender a desenvolver algoritmos para importar dados de ativos da bolsa de valores, criar seu portfólio ou carteira de ações, analisá-las e otimizar sua carteira para alcançar melhores resultados. Tudo isso utilizando uma linguagem poderosa e gratuita, o R. Esse curso foi desenvolvido para todos aqueles que desejam trabalhar no mundo das finanças e aprimorar os resultados de seus investimentos.