Data Science with R Programming Training in Toronto

R functions, code compilation, and data in well-defined format called R Packages, R Package structure, package metadata and testing, CRAN (Comprehensive R Archive Network), vector creation and variables values assignment

R functionality, Rep function, generating repeats, sorting and generating factor levels, transpose and stack function

Introduction to matrix and vector in R, understanding various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists and understanding how to read data from external files

Generate plots in R, graphs, bar plots, line plots, histograms and components of a pie chart

Understanding analysis of variance (ANOVA) statistical technique, working with pie charts and histograms, and deploying ANOVA with R, one-way ANOVA, and two-way ANOVA

K-Means clustering for cluster and affinity analysis, cluster algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining, and analysis and learning co-occurrence relationships

Introduction to Association Rule Mining, various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, algorithm and rules of Association Rule Mining, and understanding single cardinality

Understanding what is simple linear regression, various equations of line, slope, Y-intercept regression line, deploying analysis using regression, the least square criterion, interpreting the results and standard error to estimate and measure of variation.

Scatter plots, two-variable relationship, simple regression analysis, and line of best fit

Deep understanding of measure of variation, the concept of co-efficient of determination, F-test, the test statistic with an F-distribution, advanced regression in R and prediction linear regression.

Logistic regression mean and logistic regression in R

Advanced logistic regression, understanding how to predict using logistic regression, ensuring if the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system and ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

Detailed understanding of ROC, area under ROC curve, converting the variable, data set partitioning, understanding how to check for multicollinearity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix and deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates

Data analysis with R, understanding the Wald test, MC Fadden’s pseudo R-squared, the significance of the area under ROC curve, Kolmogorov–Smirnov chart which is a non-parametric test of one-dimensional probability distribution.

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data and visualization of the performance of the algorithm using the confusion matrix

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop integrated programming environment and R programming for MapReduce jobs and Hadoop execution.

Logistic Regression Case Study

In this case study, you will get a detailed understanding of the advertisement spends of a company that will help drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns and uncover insights and more, all through the power of R programming. Due to this, the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on various parameters. You will deploy multiple regression and note down the MPG for the car make, model, speed, load conditions, etc. It includes the model building, model diagnostic and checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) Case Study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real-world data, check the ROC curve and more.