R PROGRAMMING COURSES

• Installing R and R Studio
• Creating Objects in R
• Creating Arrays
• Creating Data frames
• Use of Structure
• Dimensions
• Loading CSV files
• Foreign packages into R

• • Data Manipulation with R
  • Loading vectors in R
  • Combining to vectors in R
  • Cleaning Data with R
  • Swapping Data
  • Sorting Data,

• Converting unstructured to structured data
• usage of :
  • sub
  • Gsub
  • Regexpr
  • Gregexpr
  • Apply
  • lapply

• Usage of
  • Plot, lines, boxplot, stars,
  • barplot, pie, hist, rug, sunflowerplot,
• Various parameters of histograms
• tabplot, ggplot2, maptools
• extracting data from URLs

• Case study on Data Science project with R language

This course comes as a perfect package by allowing you to learn about the entire data lifecycle and all the methods to achieve the same. You would not just learn about the data analytics techniques and tools but also learn how to apply them to raw data and obtain business insights.

R programming is a language and environment used for statistical computing and graphics. It is widely used by data analysts and statisticians for data analysis and visualization.

The basic data types in R include numeric (numbers), character (text), logical (TRUE or FALSE), and complex (complex numbers).

The purpose of learning R programming is to gain proficiency in a language and environment specifically designed for statistical computing and data analysis. It equips individuals with the skills to manipulate, visualize, and analyze data effectively, enabling them to make data-driven decisions and contribute to fields such as data science, research, and academia.

The tools of R programming include:

• RStudio: A popular integrated development environment (IDE) for R programming that provides a user-friendly interface, code editor, and various features for efficient coding, debugging, and data visualization.
• CRAN (Comprehensive R Archive Network): A repository that hosts thousands of R packages, which are collections of functions and datasets contributed by the R community. CRAN allows users to easily download and install packages to extend R's functionality.
• R Markdown: A versatile tool for creating dynamic documents that combine code, visualizations, and narrative text. R Markdown allows users to seamlessly blend code and text, facilitating the creation of reproducible reports, presentations, and websites.

Data mining finds applications in various fields such as business intelligence, marketing, finance, healthcare, fraud detection, and customer relationship management (CRM).

The main data mining techniques include classification, clustering, regression, association rule mining, and anomaly detection.

The skills of R programming include:

1. Data manipulation: The ability to import, clean, transform, and manipulate data using R's built-in functions and packages like dplyr and tidyr.
2. Statistical analysis: Proficiency in conducting statistical analyses, such as hypothesis testing, regression analysis, ANOVA, and descriptive statistics, using R's statistical packages like stats and lme4.
3. Data visualization: Creating visually appealing and informative plots, charts, and graphs using R's visualization packages like ggplot2 and plotly.
4. Programming fundamentals: Understanding and applying core programming concepts like loops, conditionals, functions, and object-oriented programming principles in R.

The data mining process typically involves problem definition, data collection, data preprocessing, modeling, evaluation, and interpretation of results.

The purpose of R programming is to provide a flexible and powerful tool for statistical analysis, data visualization, and data manipulation, making it a popular choice among researchers, data scientists, and statisticians.

Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.

No, Most of software are free and open source. The guidelines to setup software is a part of course.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.

The course spans over six months, comprising 400 learning hours, including 120 hours of live online training. The comprehensive program also entails 20 capstone projects and one client project.

There are no specific prerequisites for enrolling in the R programming course at DataMites. However, having a basic understanding of programming concepts and familiarity with statistical concepts would be beneficial.

The curriculum of the R programming course at DataMites covers topics such as introduction to R programming, data types and structures, data manipulation, data visualization, statistical modeling, machine learning with R, and advanced topics like text mining and time series analysis.

Yes, the instructors at DataMites are experienced professionals who have expertise in R programming and data analysis. They have hands-on experience in working on real-world projects and are skilled at teaching the concepts effectively.

DataMites is known for its experienced instructors, comprehensive curriculum, and hands-on projects, providing a strong foundation in R programming for data analysis.

DataMites offers career support and job placement assistance, along with flexible learning options, making it a reliable choice for individuals seeking to enhance their R programming skills and pursue a career in data analysis.