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Applied Statistics with R Course

Applied Statistics with R paves the way for developing new methods for interactive data analysis. R is getting popular in the market as it is serving as a better tool for statistical analysis. Using R, you can perform software development and Data Analysis. R is highly flexible as it is dependent on former computer language. Join the course to learn and analyze statistics in the context of real-world problems.

Course syllabus
Price: $400 - $500
  • Introduction to R
  • Statistics and Graphical Data Display
  • Programming in R
  • R Operations and Concepts

About Applied Statistics With R

  • Applied Statistics with R allows the students to get started on their applied statistical problems.

    A statistical program which is available on the internet is referred as R and provides an environment for performing statistical computing and producing graphics. The letter "R" refers to two things programming language and software environment for statistical computing, which is free and open-source.

    The advantage of R is the support for Graphical User Interface (GUI) that makes interactions easier thought it is a programming language. R also allows you to copy and paste text from other applications. R web page provides a library of basic commands. You can copy and paste using these commands into R for performing a variety of statistical analysis.

    Applied Statistics with R paves the way for developing new methods for interactive data analysis.

Applied Statistics With R Course Content

  • Why Applied Statics with R?

    R is a very easy programming language and allows to create a high-quality analysis. R is very flexible, and the vast ecosystem is the strongest feature in R. R supports excellent graphics and charting capabilities. It has grown rapidly and supports an extensive collection of packages.

    The instructions for obtaining R largely depend on the user's hardware and operating system. There are base packages that come with R automatically and contributed packages that you need to download for installation.

  • Why should I learn R?

    R is the programming language which is necessary for development in the statistical analysis and machine learning spaces. The popularity of R is growing in the market as the machines are becoming important as data generators.
  • What will I learn?

    • Learn powerful tools for handling, analyzing and displaying your data
    • Learn to analyze data by writing functions and scripts
    • Understand the concept of a model
    • Gain knowledge on Regression
    • Learn to analyze statistics in the context of real-world problems
    • Make predictions using your regression models
    • Produce excellent graphics
  • Requirements

    This class teaches you to install the necessary software.

    Desktop computer with Windows, Mac or Linux OS

    Basic working knowledge of statistics, regression analysis, and the linear model

  • Audience

    Software developers willing to change their career path to Data Science

    Software Engineers willing to change their career path to Data Science

    Data Analysts from any industry who are ready to enter Tech industry but with basic coding and scripting knowledge

Applied Statistics With R Course Syllabus

  • Introduction to R

    This chapter introduces you to R, the programming language which is a free and open source for statistical computing and graphics. This session will teach you to download R, the R studio script and setup the R environment required for learning. R supports several basic data structures. A data structure is either homogeneous in which all elements are of the same data type or heterogeneous in which elements can be of more than one data type. Numeric, Integer, Complex and Logical are the data types available in R.

    Class 1.1:

    • What is R?
    • R Language features
    • Setting up R environment
    • Using R interactively


    Class 1.2:

    • R objects and functions
    • Data structures
    • Data input and output
    • Missing data
  • Statistics and Graphical data display

    The R environment supports graphical facility for displaying a large variety of statistical graphs and build new types of graph. You can use these graphical facilities in both interactive and batch modes. This lesson teaches you about the Plotting commands in R which are of three types namely High-level plotting functions to create new plot on the graphic devices, low-level plotting function to include more information to an already existing plot and Interactive graphics to interactively add or extract information from an existing plot.

    Class 2.1:

    • Graphical exposition
    • Summary statistics
    • Descriptive for tables
    • Plotting methods
  • Operations and Concepts

    This session explains about the R commands and operations. Objects are the entities on which R operates. Multiple collections of data entries are called an Array and R allows simple and easy methods to create and handle multiple arrays. The named data structures used in R is the vector, and it is an entity that consists of ordered collection of numbers.

    Class 3.1:

    • Arithmetic operations
    • Objects and attributes
    • Arrays and matrices
    • Vectors
  • Programming in R

    This chapter teaches you how to create objects of mode function. R functions get stored in a special internal form for using them in expressions. R is a powerful and convenient language that allows you to learn and write useful functions which result in a comfortable and productive development. Many functions like mean(), var() and postscript() are available in R themselves, and hence they do not differ from user written functions.

    • 4.1. Writing your functions
    • 4.2. Basic programming: conditional execution and loops
    • 4.3. Programming with functions
    • 4.4. Input and output control of functions
  • Null hypothesis significance testing

    This section explains the theory of power calculations and sample size choice. Based on the null hypothesis value, we conclude decisions about the acceptance and rejection regions. This decision allows you to identify the probability of test statistic which falls into rejection region. The tests described here allows you to find out the degrees of freedom of the distribution in the null hypothesis

    • 5.1. Student's t- and other parametric tests
    • 5.2. Nonparametric hypothesis tests
    • 5.3. Association and correlation
    • 5.4. Power calculations and sample size determination
  • The linear model

    The concept of models is applicable for explaining relationships and predict observations. Small and interpretable models help in explaining and models which make most minor errors possible without overfitting helps for predict. Both the feature mentioned above is made possible by Linear models.

    • 6.1. Linear regression analysis
    • 6.2. Analysis of variance
    • 6.3. Analysis of covariance
    • 6.4. Logistic regression
    • 6.5. Inspection of residuals, checking model assumptions
  • Probability in R

    Probabilistic statements are derived based on the distribution. R supports many functions for determining the density, distribution and random values. A set of statistical tables is available in R which help to calculate the cumulative distribution function, the probability density function, and the quantile function.

    Class 7.1:

    • R as a set of statistical tables
    • Data distribution
    • One- and two-sample tests
  • Using R for Statistical analyses

    R provides an interlocking suite of facilities which make statistical models very simple. The most basic statically tests include comparing continuous data either between two groups or against a previously stipulated value. This lesson teaches you how to perform the t-test, and these t-tests assume that the data comes from normal distribution.

    Class 8.1:

    • One sample t-test
    • Two sample t-test
    • The paired t-test
  • Regression

    This chapter shows how to perform regression analysis. Regression analysis, in general, includes plots for model checking, confidence and prediction intervals display. This lesson also explains about the correlation in both parametric and non-parametric variants.

    Class 9.1:

    • Simple linear regression
    • Residuals and the fitted values
    • Regression parameters, mean response, and predictions
    • Correlation

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