Data Science is an arena of reconstructing unstructured data to a structured model and confer it into knowledge. There are several tools and programming languages that are used in this process, and R is one such effective and required programming language for this purpose. Data Science R Training is one such training program that will help you to learn R Programming for Data Science from basic to advance level.
This training program will let you learn how to use variables, matrix, functions and other models of R programming required for Data Science. At advance level, you learn to use R programming language in collaboration of Machine learning algorithm for Data Science.
Prerequisites
This course has no specific prerequisites.
Fundamental knowledge in any high-level programming language (preferably R) is ideal but not required.
Basic knowledge of statistics would be considered as an added advantage.
Basic knowledge of computer hardware and software is ideal but not required.
What will you gain after this course
Through this course, you can stay on top of others in the talent race.
You will be recognized as a professional Data Scientist.
You will be recognized as an R Programmer, as well.
With the help of this course, you will be recognized as a Business intelligence (BI) expert.
Jobs you can get with a Data Science R Certification
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Who is this certification for?
Mostly the computer professionals are the key target audience of this training program. But the newbie’s may also participate in this training, as well.
Small Groups
With small groups of students, our instructors can work closely with each student.
Flexible Class Schedules
Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.
Experienced Instructors
Our instructors follow a modified are personalized approach to engage students during class
Hi-Tech Lab Facilities
Our students can access our lab facilities anytime for practical experience during and after studies.
barplot, a histogram and a density plot, and multivariate analysis
bar plots for categorical variables with geom_bar(), and theme() plotly visualization, geom_freqpoly() frequency plots, multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
Geographic visualization with ggmap() and building web applications with shinyR
barplot, a histogram and a density plot, and multivariate analysis
bar plots for categorical variables with geom_bar(), and theme() plotly visualization, geom_freqpoly() frequency plots, multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
Geographic visualization with ggmap() and building web applications with shinyR
Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
Introduction to Machine Learning
Introduction to linear regression, predictive modelling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
Predicting results and finding the p-value and an introduction to logistic regression
Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
Understanding the summary results with a null hypothesis, F-statistic, and building linear models with multiple independent variables
Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false-positive rate, and threshold evaluation with ROCR
Finding out the right threshold by building the ROC plot, cross-validation, multivariate logistic regression, and building logistic models with multiple independent variables
What is the classification? Different classification techniques
Introduction to decision trees
Algorithm for decision tree induction and building a decision tree in R
Confusion matrix and regression trees vs classification trees
Introduction to bagging
Random forest and implementing it in R
What is Naive Bayes? Computing probabilities
Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
what is k-means clustering? What is canopy clustering?
What is hierarchical clustering?
Introduction to unsupervised learning
Feature extraction, clustering algorithms, and the k-means clustering algorithm
Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means and finding out the right number of clusters using a scree plot it in R
Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
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Certification
Data Science R