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R Programming Training in Jaipur

R Programming Training In Jaipur

Technoglobe Jaipur’s No.1 institute for training and internship is providing training in R Programming. Technoglobe has been shining in the field of technology since more than one and a half decade. Our remarkable achievement includes affiliation with Rajasthan Technical University and we also became the authorized partners of Microsoft & HP.

Our modern infrastructure and learning friendly environment helps the students to have in depth knowledge. Through our internationally standardized curriculum students learn that Cloud computing is an information technology (IT) paradigm that enables ubiquitous access to shared pools of configurable system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Our trainers are highly experienced and certified in the field of Information Technology.

Technoglobe provides students to get Internationally Certified by global companies like Microsoft & HP. Technoglobe trains students that how R Programming simplifies accessibilities, provides virtual storage space, addresses backup issues. At Technoglobe students get practical exposure about their learning through live and real time projects. By this exposure students learn that how Cloud computing provides security against unauthorized access and loss of data.

Technoglobe provides placements assistance to its students in R Programming in top reputed companies across the country. We are recruiting partners of more than 200 companies in India. We provide 100% placement assistance to our students.

R Programming training in Jaipur

R Programming Course Training in Jaipur

R Programming Training will help you to find good job or create chance for your promotion. We have plenty of experienced professional instructors who will teach you at best level with live project that will help you to implement new stuffs. We designed this R Programming course according to current demand of software industry.

R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science.

R Programming Course Training in Jaipur- Technoglobe is one of the best R Programming training institute in Jaipur with 100% Placement Support. We provides real-time and placement focused R Programming training in Jaipur. We have a track record of more than 1000 placements.

Technoglobe, 16 years old IT Training company provides high-quality R Programming training to students as well as working professionals to enhance their technical skills in R Programming. Candidates are provided in depth theoretical & practical knowledge in R Programming along with working on Major Project in R Programming Technology.

R Programming Course Training in Jaipur- Technoglobe is a Leading Training Centre in Jaipur that provides R Programming training courses of different modules with assured Placements. We are into 17 years experience in these trainings. Call us 9928556083

R Programming Course

    R with Real Time Examples

  • Types of variables
  • Using Variables
  • Logical Variables and Operators
  • The "While" Loop
  • Using the console
  • The "For" Loop
  • The "If" statement
  • What is a Vector?
  • Let's create some vectors
  • Using the [] brackets
  • Vectorized operations
  • The power of vectorized operations
  • Functions in R
  • Packages in R
  • Matrices
  • Building Your First Matrix
  • Naming Dimensions
  • Colnames() and Rownames()
  • Matrix Operations
  • Visualizing With Matplot()
  • Subsetting
  • Visualizing Subsets
  • Creating Your First Function
  • Importing data into R
  • Exploring your dataset
  • Using the $ sign
  • Basic operations with a Data Frame
  • Filtering a Data Frame
  • Introduction to qplot
  • Building Dataframes
  • Merging Data Frames
  • Loop Functions
  • lapply()
  • sapply()
  • apply()
  • tapply()
  • mapply()
  • Regular Expressions
  • Saving r objects
  • dplyr package
  • Shiny Apps

Statistics with R

  • Data collection
  • Data collection - Questionaire Designing
  • Data collection - Observation
  • Data collection - Case Study Method
  • Qualitative Data Vs Quantitative Data
  • Data Patterns
  • Deciles Statistics
  • Venn Diagram
  • Central limit theorem
  • Chebyshev's Theorem
  • Kurtosis
  • Normal Distribution
  • Laplace Distribution
  • Log Gamma Distribution
  • Rayleigh Distribution
  • Exponential distribution
  • Multinomial Distribution
  • Binomial Distribution
  • Beta Distribution
  • F distribution
  • Negative Binomial Distribution
  • Gamma Distribution
  • Chi-squared Distribution
  • Geometric Mean
  • Harmonic Mean
  • Outlier Function
  • Stem and Leaf Plot
  • Poisson Distribution
  • Cumulative Poisson Distribution
  • Inverse Gamma Distribution
  • Continuous Uniform Distribution
  • Hypergeometric Distribution
  • Harmonic Number
  • Gumbel Distribution
  • Comparing plots
  • Power Calculator
  • Process Sigma
  • Harmonic Resonance Frequency
  • Standard normal table
  • Pooled Variance (r)
  • Mean Deviation
  • Means Difference
  • code(+theory) for Descriptive:
  • entral tendency:
  • Arithmetic Mean
  • Arithmetic Median
  • Arithmetic Mode
  • Arithmetic Range
  • Range Rule of Thumb
  • Adjusted R-Squared
  • Standard Deviation
  • Relative Standard Deviation
  • Analysis of Variance
  • Grand Mean
  • Boxplots
  • Quartile Deviation
  • Frequency Distribution
  • Bar Graph
  • Dot Plot
  • Scatterplots
  • Correlation Co-efficient
  • Pie Chart
  • Histograms
  • Cumulative Frequency
  • Cumulative plots
  • Skewness
  • Goodness of Fit
  • Transformations
  • Trimmed Mean
  • Reliability Coefficient
  • Linear regression
  • Logistic Regression
  • Quadratic Regression
  • Regression Intercept Confidence Interval
  • Residual sum of squares
  • Equation Sum of Square
  • Standard Error ( SE )
  • Root Mean Square
  • Cohen's kappa coefficient
  • Ti 83 Exponential Regression
  • Shannon Wiener Diversity Index
  • Machine Learning with Real Time Examples

    • Building Linear Regressors
    • Interpreting Regression Results and Interactions Terms
    • Performing Residual Analysis & Extracting Extreme Observations Cook's Distance
    • Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
    • Validating Model Performance on New Data with k-Fold Cross Validation
    • Building Non-Linear Regressors with Splines and GAMs
    • Building Logistic Regressors, Evaluation Metrics, and ROC Curve
    • Understanding the Concept and Building Naive Bayes Classifier
    • Building k-Nearest Neighbors Classifier
    • Building Tree Based Models Using RPart, cTree, and C5.0
    • Building Predictive Models with the caret Package
  • Selecting Important Features with RFE, varImp, and Boruta
  • Building Classifiers with Support Vector Machines
  • Understanding Bagging and Building Random Forest Classifier
  • Implementing Stochastic Gradient Boosting with GBM
  • Regularization with Ridge, Lasso, and Elasticnet
  • Building Classifiers and Regressors with XGBoost
  • Dimensionality Reduction with Principal Component Analysis
  • Clustering with k-means and Principal Components
  • Determining Optimum Number of Clusters
  • Understanding and Implementing Hierarchical Clustering
  • Clustering with Affinity Propagation
  • Building Recommendation Engines
  • Understanding the Components of a Time Series, and the xts Package
  • Stationarity, De-Trend, and De-Seasonalize
  • Understanding the Significance of Lags, ACF, PACF, and CCF
  • Forecasting with Moving Average and Exponential Smoothing
  • Forecasting with Double Exponential and Holt Winters
  • Forecasting with ARIMA Modelling
  • Scraping Web Pages and Processing Texts
  • Corpus, TDM, TF-IDF, and Word Cloud
  • Cosine Similarity and Latent Semantic Analysis
  • Extracting Topics with Latent Dirichlet Allocation
  • Sentiment Scoring with tidytext and Syuzhet
  • Classifying Texts with RTextTools
  • Building a Basic ggplot2 and Customizing the Aesthetics and Themes
  • Manipulating Legend, AddingText, and Annotation
  • Drawing Multiple Plots with Faceting and Changing Layouts
  • Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
  • ggplot2 Extensions and ggplotly
  • Implementing Best Practices to Speed Up R Code Preview
  • Implementing Parallel Computing with doParallel and foreach
  • Writing Readable and Fast R Code with Pipes and DPlyR
  • Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
  • Interface C++ in R with RCpp
  • Understanding the Structure of an R Package
  • Build, Document, and Host an R Package on GitHub
  • Performing Important Checks Before Submitting to CRAN
  • Submitting an R Package to CRAN
  • R Machine Learning solutions
  • Downloading and Installing R
  • Downloading and Installing RStudio
  • Installing and Loading Packages
  • Reading and Writing Data
  • Using R to Manipulate Data
  • Applying Basic Statistics
  • Visualizing Data
  • Getting a Dataset for Machine Learning
  • Reading a Titanic Dataset from a CSV File Preview
  • Converting Types on Character Variables
  • Detecting Missing Values
  • Imputing Missing Values
  • Exploring and Visualizing Data
  • Predicting Passenger Survival with a Decision Tree
  • Validating the Power of Prediction with a Confusion Matrix
  • Assessing performance with the ROC curve
  • Understanding Data Sampling in R
  • Operating a Probability Distribution in R
  • Working with Univariate Descriptive Statistics in R
  • Performing Correlations and Multivariate Analysis
  • Operating Linear Regression and Multivariate Analysis
  • Conducting an Exact Binomial Test
  • Performing Student's t-test
  • Performing the Kolmogorov-Smirnov Test
  • Understanding the Wilcoxon Rank Sum and Signed Rank Test
  • Working with Pearson's Chi-Squared Test
  • Conducting a One-Way ANOVA
  • Performing a Two-Way ANOVA
  • Fitting a Linear Regression Model with lm
  • Summarizing Linear Model Fits
  • Using Linear Regression to Predict Unknown Values
  • Generating a Diagnostic Plot of a Fitted Model
  • Fitting a Polynomial Regression Model with lm
  • Fitting a Robust Linear Regression Model with rlm
  • Studying a case of linear regression on SLID data
  • Reducing Dimensions with SVD
  • Applying the Poisson model for Generalized Linear Regression
  • Applying the Binomial Model for Generalized Linear Regression
  • Fitting a Generalized Additive Model to Data
  • Visualizing a Generalized Additive Model
  • Diagnosing a Generalized Additive Model
  • Preparing the Training and Testing Datasets
  • Building a Classification Model with Recursive Partitioning Trees
  • Visualizing a Recursive Partitioning Tree
  • Measuring the Prediction Performance of a Recursive Partitioning Tree
  • Pruning a Recursive Partitioning Tree
  • Building a Classification Model with a Conditional Inference Tree
  • Visualizing a Conditional Inference Tree
  • Measuring the Prediction Performance of a Conditional Inference Tree
  • Classifying Data with the K-Nearest Neighbor Classifier
  • Classifying Data with Logistic Regression
  • Classifying data with the Naïve Bayes Classifier
  • Classifying Data with a Support Vector Machine
  • Choosing the Cost of an SVM
  • Visualizing an SVM Fit
  • Predicting Labels Based on a Model Trained by an SVM
  • Tuning an SVM
  • Training a Neural Network with neuralnet
  • Visualizing a Neural Network Trained by neuralnet
  • Predicting Labels based on a Model Trained by neuralnet
  • Training a Neural Network with nnet
  • Predicting labels based on a model trained by nnet
  • Estimating Model Performance with k-fold Cross Validation
  • Performing Cross Validation with the e1071 Package
  • Performing Cross Validation with the caret Package
  • Ranking the Variable Importance with the caret Package
  • Ranking the Variable Importance with the rminer Package
  • Finding Highly Correlated Features with the caret Package
  • Selecting Features Using the Caret Package
  • Measuring the Performance of the Regression Model
  • Measuring Prediction Performance with a Confusion Matrix
  • Measuring Prediction Performance Using ROCR
  • Comparing an ROC Curve Using the Caret Package
  • Measuring Performance Differences between Models with the caret Package
  • Classifying Data with the Bagging Method
  • Performing Cross Validation with the Bagging Method
  • Classifying Data with the Boosting Method
  • Performing Cross Validation with the Boosting Method
  • Classifying Data with Gradient Boosting
  • Calculating the Margins of a Classifier
  • Calculating the Error Evolution of the Ensemble Method
  • Classifying Data with Random Forest
  • Estimating the Prediction Errors of Different Classifiers
  • Clustering Data with Hierarchical Clustering
  • Cutting Trees into Clusters
  • Clustering Data with the k-Means Method
  • Drawing a Bivariate Cluster Plot
  • Comparing Clustering Methods
  • Extracting Silhouette Information from Clustering
  • Obtaining the Optimum Number of Clusters for k-Means
  • Clustering Data with the Density-Based Method
  • Clustering Data with the Model-Based Method
  • Visualizing a Dissimilarity Matrix
  • Validating Clusters Externally
  • Transforming Data into Transactions
  • Displaying Transactions and Associations
  • Mining Associations with the Apriori Rule
  • Pruning Redundant Rules
  • Visualizing Association Rules
  • Mining Frequent Itemsets with Eclat
  • Creating Transactions with Temporal Information
  • Mining Frequent Sequential Patterns with cSPADE
  • Performing Feature Selection with FSelector
  • Performing Dimension Reduction with PCA
  • Determining the Number of Principal Components Using the Scree Test
  • Determining the Number of Principal Components Using the Kaiser Method
  • Visualizing Multivariate Data Using biplot
  • Performing Dimension Reduction with MDS
  • Reducing Dimensions with SVD
  • Compressing Images with SVD
  • Performing Nonlinear Dimension Reduction with ISOMAP
  • Performing Nonlinear Dimension Reduction with Local Linear Embedding
  • Preparing the RHadoop Environment
  • Installing rmr2
  • Installing rhdfs
  • Operating HDFS with rhdfs
  • Implementing a Word Count Problem with RHadoop
  • Comparing the Performance between an R MapReduce Program & a Standard R Program
  • Testing and Debugging the rmr2 Program
  • Installing plyrmr
  • Manipulating Data with plyrmr
  • Conducting Machine Learning with RHadoop
  • Configuring RHadoop Clusters on Amazon EMR
  • Deep Learning with R
  • Introduction to Multi-hidden-layer Architectures
  • Fundamental Concepts in Deep Learning
  • Introduction to Artificial Neural Networks
  • Classification with Two-Layers Artificial Neural Networks
  • Probabilistic Predictions with Two-Layer ANNs
  • Introduction to Multi-hidden-layer Architectures
  • Tuning ANNs Hyper-Parameters and Best Practices
  • Neural Network Architectures
  • Neural Network Architectures Continued
  • The LearningProcess
  • Optimization Algorithms and Stochastic Gradient Descent
  • Backpropagation
  • Hyper-Parameters Optimization
  • Introduction to Convolutional Neural Networks
  • Introduction to Convolutional Neural Networks Continued
  • CNNs in R
  • Classifying Real-World Images with Pre-Trained Models
  • Introduction to Recurrent Neural Networks
  • Introduction to Long Short-Term Memory
  • RNNs in R
  • Use-Case – Learning How to Spell English Words from Scratch
  • Introduction to Unsupervised and Reinforcement Learning
  • Autoencoders
  • Restricted Boltzmann Machines and Deep Belief Networks
  • Reinforcement Learning with ANNs
  • Use-Case – Anomaly Detection through Denoising Autoencoders
  • Deep Learning for Computer Vision
  • Deep Learning for Natural Language Processing
  • Deep Learning for Audio Signal Processing
  • Deep Learning for Complex Multimodal Tasks
  • Other Important Applications of Deep Learning
  • Debugging Deep Learning Systems
  • GPU and MGPU Computing for Deep Learning
  • A Complete Comparison of Every DL Packages in R
  • Research Directions and Open Questions
  • Big Data & Hadoop with Real Time Examples

    • Introduction to Big Data
    • Getting started with Hadoop
    • Hive Concepts
    • Pig Concepts
    • Use case on log collections & analysis
    • Use Case on Ecommerece

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