HCTPL

R Programming

“R programming training courses are designed to help individuals learn and master the R programming language, which is widely used for statistical computing, data analysis, data visualization, and machine learning. Here are key aspects of R programming training.”

Participants will be introduced to the basics of R programming, including its history, features, and applications. They will learn how to install R and RStudio, the popular integrated development environment (IDE) for R, and navigate the RStudio interface.

Participants will learn about different data types in R, such as numeric, character, logical, and factors. They will understand various data structures, including vectors, matrices, arrays, lists, and data frames, and how to manipulate and analyze data using these structures.

Participants will learn how to import, clean, and preprocess data in R. They will explore techniques for data manipulation, transformation, and aggregation using functions from the dplyr and tidyr packages.

   Participants will delve into data visualization techniques in R using packages such as ggplot2, plotly, and lattice. They will learn how to create static and interactive plots, histograms, bar charts, scatter plots, box plots, and more to effectively communicate insights from data.

 

Participants will learn how to perform statistical analysis in R, including descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and correlation analysis. They will understand how to interpret statistical results and draw meaningful conclusions.

Participants will explore machine learning algorithms and techniques in R using packages such as caret, randomForest, and xgboost. They will learn how to build predictive models for classification, regression, and clustering tasks and evaluate model performance using cross-validation and other techniques.

   Participants will learn how to analyze time series data in R, including techniques for time series decomposition, forecasting, and anomaly detection. They will understand popular time series models such as ARIMA (AutoRegressive Integrated Moving Average) and Exponential Smoothing.

   Participants will be introduced to text mining and NLP techniques in R using packages such as tm, tidytext, and quanteda. They will learn how to preprocess text data, perform sentiment analysis, topic modeling, and text classification tasks.

 

   Participants will learn how to extract data from websites using web scraping techniques in R. They will understand how to parse HTML/XML documents, scrape data from web pages, and interact with web APIs to retrieve structured data.

 

    Depending on the course level, participants may explore advanced topics such as spatial data analysis, Bayesian statistics, geospatial analysis, and parallel computing in R.

 

By the end of the R programming training course, participants will have the knowledge and skills to effectively use R for data analysis, statistical modeling, machine learning, and more, making them valuable assets in the field of data science and analytics.

    Participants will learn about the Standard Template Library (STL) in C++, including containers (vector, list, deque, map, set), algorithms (sorting, searching, manipulation), and iterators. They will understand how to use STL containers and algorithms to write efficient code.

    Participants will learn how to handle exceptions in C++ using try-catch blocks, throw, and catch keywords. They will understand exception propagation, catching multiple exceptions, and best practices for exception handling in C++ programs.

 

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Refund Policy

At Hari Cornucopia Tech Private Limited, we prioritize customer satisfaction. Therefore, we offer a refund policy to ensure that participants have peace of mind when enrolling in our courses. If a participant is dissatisfied after the first class, they are eligible for a refund. However, once they attend the second class, the refund policy becomes void.