.webp)
Program overview
All Trainings > See More +
Data Science with R programming
Professional Training in Data Science with R Programming
Build a data-driven culture and analyse insights for risk-averse decision-making with R programming
Program Overview
R is a widely used open-source programming language in data science. It offers a rich library of statistical and graphical techniques, making it a powerful tool for data analysis and visualization. It combines statistical analysis, machine learning, and data visualisation to extract insights and knowledge from complex data sets.
With the increasing demand for data-driven decision-making, Data Science with R Programming has become a popular field of study.
Uptut specialises in delivering customised and comprehensive training programs that cater to your specific needs and requirements. Data Science with R Programming professional training is designed to provide hands-on training to your team in statistical analysis, data visualization, and machine learning using the R programming language.
Our experienced trainers have solved industry-level problems in Data Science with R Programming and are skilled at imparting this knowledge effectively to professionals across levels. We strive to ensure theat our training programs are engaging, experiential and provide practical solutions that can be implemented in real-world scenarios.
Personalised training curriculum
100% Live Sessions
Lifetime access to recordings and material
Hands-on labs and assignments included
Consulting Support
Delivered by industry experts
Why Data Science with R Programming for Your Business?
- Gain valuable insights and make data-driven decisions for improved efficiency, productivity, and profitability
- Empower your team to utilize data and make informed decisions.
- Handle large datasets, manipulate data easily, and produce high-quality graphics and visualizations.
Make Your Team Proficient
Training Objectives
- Gain a comprehensive understanding of data science concepts and techniques.
- Learn how to use R programming language for data science tasks, including data cleaning, manipulation, and visualization.
- Get trained in statistical modeling and machine learning techniques using R.
- Get equipped with skills to build predictive models and perform data-driven decision-making.
- Understand how to use R packages and tools for data analysis and visualization.
- Learn how to communicate data science insights and findings to stakeholders effectively.
Hands-on Experience with Tools
No items found.
Core Training Modules
- Introduction to R Programming
- Basics of the R Programming language, Variables, data types, and operators, R programming environment and basic commands.
- Data Science Fundamentals
- Introduction to data science and Its Importance, Descriptive statistics and data visualization techniques, Probability distributions and hypothesis testing.
- Data Wrangling
- Data importing and exporting using R, Data cleaning and manipulation, Data transformation and reshaping.
- Machine Learning with R
- Understanding the fundamentals of machine learning, Supervised and unsupervised learning, Regression analysis and classification algorithms.
- Data Science with Big Data
- Introduction to big data and its challenges, Working with large datasets using R, Distributed computing with R.
- Data Science Project
- Developing a complete data science project from scratch, Understanding the project lifecycle and project management techniques, Project presentation and documentation.
- Time Series Analysis
- Introduction to Time Series Analysis, Moving Averages and Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA) models, Seasonality and Decomposition, Time Series Forecasting using R.
- Text Analytics
- Introduction to Text Analytics, Text Pre-processing Techniques, Text Classification and Clustering, Sentiment Analysis, Text Mining using R.
- Machine Learning with R
- Introduction to Machine Learning, Supervised and Unsupervised Learning, Classification and Regression Algorithms, Model Evaluation Techniques, Feature Selection and Dimensionality Reduction, Model Tuning and Optimization.
- Data Visualization with R
- Introduction to Data Visualization, Data Visualization Principles and Best Practices, Basic and Advanced Charts using ggplot2, Interactive Visualizations with Shiny, Dashboarding with R Markdown.
- Big Data Analytics with R
- Introduction to Big Data Analytics, Hadoop Ecosystem and Distributed Computing, Apache Spark and SparkR, Distributed Data Processing with dplyr, Large Scale Machine Learning with sparklyr.
- Advanced Topics
- Web Scrapping, R Packages and Libraries for Data Science, Parallel Computing and Performance Tuning, R Integration with Other Tools and Platforms, R Markdown and Reproducible Research, Best Practices for Data Science Projects.
How does personalized training work?
01
Analyzing skill gap and assessing business requirements
02
Customising curriculum with core and additional modules to fit objectives
03
Supporting learning implementation with consulting
Who will benefit from this training?
- Business professionals
- Data analysts
- Data scientists
- Anyone interested in learning about data analysis
- Managers who need to make data-driven decisions
Is this training right for your team?
Frequently Asked Questions- Answered!
1. What are the pre-requisites for this training?
2. Will my team get any practical experience with this training?
3. What is your mode of delivery - online or on-sight?
4. Will trainees get certified?
5. What do we do if we need further support after the training?
Upskill My Team
- Personalised training curriculum
- 100% Live Sessions
- Lifetime access to recordings and material
- Hands-on labs and assignments included
- Consulting Support
- Delivered by industry experts
Accelerate Business Returns with In-Demand Technology and High-Performing Teams
Discover the power of digital transformation with train-to-deliver programs from Uptut's expert trainers and consultants trusted by 50,000+ professionals across the world's leading tech innovators.
.png)
.png)
.png)
.png)
.png)
.png)
.svg)
.svg)
.svg)
.svg)
.svg)
.svg)