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Program overview
Online
On-site
Hybrid
Data Science with R Programming
Build a strong foundation in data science using R by mastering data analysis, visualization, and statistical modeling techniques. Learn how to build predictive models, apply machine learning, and communicate data driven insights effectively to support informed business decisions.
Duration:
4 Days
Level:
Advanced
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1500+ users onboarded
Who will Benefit from this Training?
Training Objectives
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
Download Curriculum
- Introduction to R Programming
- Work with R syntax, types, operators, and environments
- Use projects and packages for reproducible analysis
- Write functions for reusable data tasks
- Hands-on: Set up an analysis project structure
- Data Science Fundamentals
- Connect R workflows to data science outcomes
- Apply descriptive statistics for first insights
- Choose visualizations that clarify patterns
- Frame analysis questions before coding
- Data Wrangling
- Import and export common data formats in R
- Clean, transform, and reshape tabular data
- Join and aggregate datasets for analysis
- Build a repeatable wrangling pipeline
- Data Visualization with R
- Create publication-ready charts for analysis
- Customize aesthetics for clarity and comparison
- Combine multiple plots for storytelling
- Export visuals for reports and presentations
- Machine Learning with R
- Apply supervised and unsupervised methods in R
- Train regression and classification baselines
- Evaluate models with appropriate metrics
- Compare algorithms for a practical use case
- Time Series Analysis
- Prepare time-ordered data in R
- Apply moving averages, smoothing, and ARIMA-style patterns
- Validate forecasts with time-aware holdouts
- Interpret and communicate forecast results
- Text Analytics
- Preprocess text for analysis in R
- Classify and cluster text documents
- Extract themes from unstructured content
- Connect text insights to business questions
- Big Data Analytics with R
- Identify limits of in-memory R workflows
- Work with larger datasets using scalable patterns
- Integrate R with big-data tooling where needed
- Choose practical architectures for R at scale
- Data Science Project
- Scope a complete data science project lifecycle
- Combine wrangling, modeling, and visualization
- Document assumptions, metrics, and decisions
- Present findings with clear recommendations
- Advanced Topics
- Explore advanced R packages for specialized analysis
- Improve performance and code quality practices
- Package reusable analysis components
- Plan next-step learning paths for the team
Hands-on Experience with Tools
Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
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100% Live
on-site/online training
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Hands-on
Labs and capstone projects
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Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
1
Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
3
Why this course
- Faster data-driven decisions: Equip teams to analyze data in R and act on evidence instead of intuition.
- Strong statistical workflows: Use R’s statistical ecosystem for modeling, testing, and visualization.
- Reliable data wrangling: Clean, transform, and prepare large datasets with reproducible R pipelines.
- Clear stakeholder communication: Produce high-quality graphics and reports that explain insights effectively.
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.
Who will benefit
- Business professionals
- Data analysts
- Data scientists
- Anyone interested in learning about data analysis
- Managers who need to make data-driven decisions
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Frequently Asked Questions
2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.