Data Science with R Training gives students a combined knowledge on both Data Science and R which will put them on the forefront in among other workforce. Our Data Science with R Training Institute has the most up-to-date syllabus and modern infrastructure, along with experienced trainers as well. Therefore, our Data Science with R Course will give students a holistic learning of Data Science with R, which will eventually give them a prolonged, high-paying career in Data Science with R as a Data Scientist and so on. So go ahead and explore more down below to get all the information you need about our Data Science with R Course with certification & placements.
Data Science with R Training
4.90
(1987)
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DURATION
2 Months
EMI
0% Interest
Mode
Live Online / Offline
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100% Placement
Assurance
Get IBM Certified
What this Course Includes?
- Technology Training
- Aptitude Training
- Learn to Code (Codeathon)
- Real Time Projects
- Learn to Crack Interviews
- Panel Mock Interview
- Unlimited Interviews
- Life Long Placement Support
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Course Schedules
Course Syllabus
Course Fees
or any other questions...
Breakdown of Data Science with R Training Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
June 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
Course Fee
June 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Course Fee
Save up to 20% in your Course Fee on our Job Seeker Course Series
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Syllabus for The Data Science with R Training Course
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Module: 1 R Introduction
1
- Overview of R Programming
- Downloading and installing
- Help of Function
- Viewing documentation
- General issues in R
- Package Management
Module: 2 Data Inputting in R
2
- Data Types
- Subsetting
- Writing data
- Reading from csv files
- Creating a vector and vector operation
- Initializing data frame
- Control structure
- Re-directing R Output
Module: 3 Data Visualization
3
- Creating bar chart and dot plot
- Creating histogram and box plot
- Plotting with base graphics
- Plotting and coloring in R
Module: 4 Basic Statistic
4
- Computing Basic Statistics
- Comparing means of two samples
- Testing a proportion
- Data Munging Basics
Module: 5 Functions and Programming in R
5
- Flow control: For loop
- If condition
- Debugging tools
Module: 6 Data manipulation in R
6
- List Management
- Data Transformation
- Merging Data Frames
- Outlier Detection
- Combining multiple vectors
Module: 7 R an Database
7
- Performing queries
- RODBC and DBI Package
- Advanced Data handling
- Combined and restructuring data frames
Statistical Modelling in R
8
- Logical Regression
- Hierarchical Clustering PCA for Dimensionality Reduction
Objectives of Learning Data Science with R Training Course
Download Syllabus
The Data Science with R Training will cover all the topics ranging from fundamental to advanced concepts, which will make it easy for students to grasp Data Science with R. The Data Science with R Course Curriculum is composed of some of the most useful and rare concepts that will surely give students a complete understanding of Data Science with R as well. So, some of those curriculum are discussed below as objectives:
- To make students well-versed with the fundamental concepts of Data Science with R like – Downloading and installing, Help of Function, Viewing documentation, Data Inputting in R, Data Visualization etc.
- To make students more knowledgeable in Data Science with R by making them learn concepts like – Basic Statistics, Functions and Programming in R, Data Manipulation in R etc.
- To make students experts in the advanced concepts in Data Science with R like – R and Database- Performing queries, Statistical Modelling in R etc.
Reason to choose Softlogic for Data Science with R Training training
- SLA stands out as the Exclusive Authorized Training and Testing partner in Tamil Nadu for leading tech giants including IBM, Microsoft, Cisco, Adobe, Autodesk, Meta, Apple, Tally, PMI, Unity, Intuit, IC3, ITS, ESB, and CSB ensuring globally recognized certification.
- Learn directly from a diverse team of 100+ real-time developers as trainers providing practical, hands-on experience.
- Instructor led Online and Offline Training. No recorded sessions.
- Gain practical Technology Training through Real-Time Projects.
- Best state of the art Infrastructure.
- Develop essential Aptitude, Communication skills, Soft skills, and Interview techniques alongside Technical Training.
- In addition to Monday to Friday Technical Training, Saturday sessions are arranged for Interview based assessments and exclusive doubt clarification.
- Engage in Codeathon events for live project experiences, gaining exposure to real-world IT environments.
- Placement Training on Resume building, LinkedIn profile creation and creating GitHub project Portfolios to become Job ready.
- Attend insightful Guest Lectures by IT industry experts, enriching your understanding of the field.
- Panel Mock Interviews
- Enjoy genuine placement support at no cost. No backdoor jobs at SLA.
- Unlimited Interview opportunities until you get placed.
- 1000+ hiring partners.
- Enjoy Lifelong placement support at no cost.
- SLA is the only training company having distinguished placement reviews on Google ensuring credibility and reliability.
- Enjoy affordable fees with 0% EMI options making quality training affordable to all.
Want to Speak to a Trainer about Data Science with R Training?Request a Free Callback
Highlights of The Data Science with R Training Course
What is Data Science with R?
1.
Data Science with R involves utilizing the R programming language for comprehensive data analysis. R, an open-source language tailored for statistics and visualization, facilitates tasks such as data collection, cleaning, exploratory analysis, statistical testing, machine learning, and visualization. Key features include powerful libraries and a supportive community.
What is Data Science with R Full Stack?
2.
Data Science with R Full Stack involves using the R programming language to manage the entire data science process. This includes data collection, cleaning, exploratory analysis, statistical testing, machine learning, visualization, and deployment, supported by various R packages and tools to ensure a comprehensive and integrated workflow.
What are the reasons for learning Data Science with R?
3.
The following are the reasons for learning Data Science with R:
- Advanced Statistical Analysis: R is tailored for sophisticated statistical methods, making it suitable for complex data analyses.
- Extensive Libraries: Packages like tidyverse, caret, and ggplot2 offer comprehensive tools for data handling, modeling, and visualization.
- Superior Visualization: R is proficient in generating detailed and customizable visualizations to reveal insights and patterns.
- Complete Workflow Support: It encompasses the entire data science process, from data collection and cleaning to analysis and reporting.
What are the prerequisites for learning Data Science with R ?
4.
The following are the prerequisites for learning Data Science with R:
- Algebra: Basic algebra skills, including operations and equations, are commonly used in data analysis and modeling.
- Data Manipulation: Experience with data manipulation techniques such as filtering, sorting, aggregating, and reshaping datasets is advantageous. Familiarity with tools like Excel or SQL can also be helpful.
- R Syntax: Basic knowledge of R syntax and operations, including working with vectors, matrices, data frames, and lists.
- Graphical Representation: Understanding how to create and interpret visual data representations such as charts, histograms, and scatter plots is useful.
What are the course fees and duration?
5.
Our Data Science with R Course Fees may vary depending on the specific course program you choose (basic / intermediate / full stack), course duration, and course format (remote or in-person). On an average the Data Science with R Course Fees range from 65k to 70k, for a duration of 3 months with international certification based on the above factors.
What are some of the jobs related to Data Science with R?
6.
The following are the jobs related to Data Science with R:
- Data Scientist
- Data Analyst
- Statistician
- Business Intelligence (BI) Analyst
- Machine Learning Engineer
- Data Engineer
- Quantitative Analyst
List a few real time Data Science with R applications.
7.
The following are the real-time Data Science with R applications:
- Financial Market Analysis
- Healthcare Analytics
- E-Commerce Personalization
- Social Media Monitoring
- Fraud Detection
- Traffic Management
Who are our Trainers for The Data Science with R Training Course?
Our Mentors are from Top Companies like:
- Our trainers bring over 10 years of extensive experience in Data Science and R programming, providing certified training.
- They specialize in helping students acquire skills quickly, flexibly, and reliably.
- They are knowledgeable across the full range of Data Science and R programming topics, including data visualization, exploration, cleansing, analysis, and predictive modeling.
- They have led numerous workshops that cover the complexities of Data Science and R programming, delivering a thorough overview.
- Their expertise includes deep knowledge of algorithms and big data tools like Hadoop, Apache Spark, and NoSQL databases.
- They offer precise guidance on visualization, simulation, real-time application development, and high-performance computation.
- They have developed a broad set of technical problem-solving skills applicable to various business environments and are recognized for their innovative solutions to complex issues.
- They are proficient at quickly learning new technologies, understanding and developing advanced analytics modules, and excelling in both theoretical and practical programming.
- They are capable of training individuals or groups of any size effectively.
- Their experience and skills are crucial for successfully equipping students with the necessary abilities for job placement.
Want to Speak to a Trainer about Data Science with R Training?Request a Free Callback
What Modes of Training are available for Data Science with R Training?
Offline / Classroom Training
A Personalized Learning Experience with Direct Trainer Engagement!
- Direct Interaction with the Trainer
- Clarify doubts then and there
- Airconditioned Premium Classrooms and Lab with all amenities
- Codeathon Practices
- Direct Aptitude Training
- Live Interview Skills Training
- Direct Panel Mock Interviews
- Campus Drives
- 100% Placement Support
Online Training
Instructor Led Live Training! Learn at the comfort of your home
- No Recorded Sessions
- Live Virtual Interaction with the Trainer
- Clarify doubts then and there virtually
- Live Virtual Interview Skills Training
- Live Virtual Aptitude Training
- Online Panel Mock Interviews
- 100% Placement Support
Corporate Training
Blended Delivery model (both Online and Offline as per Clients’ requirement)
- Industry endorsed Skilled Faculties
- Flexible Pricing Options
- Customized Syllabus
- 12X6 Assistance and Support
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Certifications
Take your career to new heights with Softlogic's software training certifications.
Improve your abilities to get access to rewarding possibilities
Improve your abilities to get access to rewarding possibilities
Earn Your Certificate of Completion
Validate your achievements with Softlogic's Certificate of Completion, verifying successful fulfillment of all essential components.
Take Your Career to the Next Level with an IBM Certification
Get an IBM certification through our training programs to gain a competitive edge in the industry.
Stand Out from the Crowd with Codethon Certificate
Verify the authenticity of your real-time projects with Softlogic's Codethon certificate.
Project Practices for The Data Science with R Training Course
Weather Forecasting
Create a forecasting model to predict weather conditions using real-time meteorological data.
Energy Consumption Forecasting
Develop a model to forecast energy consumption and manage distribution based on data from smart meters.
Customer Support Automation
Build a chatbot or automated response system for customer service using real-time interaction data.
Supply Chain Analytics
Create a real-time dashboard for monitoring and optimizing supply chain operations, including inventory levels and logistics.
Traffic Flow Optimization
Develop a tool for analyzing and optimizing traffic flow using real-time data from traffic sensors and GPS systems.
Fraud Detection System
Design a system to detect and prevent fraudulent activities in financial transactions through anomaly detection and classification.
Social Media Sentiment Analysis
Analyze live social media data to assess public sentiment regarding brands, products, or events.
E-Commerce Recommendation System
Build a recommendation engine for an e-commerce platform that suggests products based on user behavior and preferences.
Healthcare Predictive Modeling
Develop a predictive model to forecast patient outcomes from real-time health data sourced from electronic health records (EHRs).
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The SLA way to Become
a Data Science with R Training Expert
Enrollment
Technology Training
Coding Practices
Realtime Projects
Realtime Projects
Placement Training
Aptitude Training
Interview Skills
Interview Skills
Panel Mock
Interview
Unlimited
Interviews
Interview
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See More ReviewsPlacement Support for a Data Science with R Training Job
Genuine Placements. No Backdoor Jobs at Softlogic Systems.
Free 100% Placement Support
Aptitude Training
from Day 1
Interview Skills
from Day 1
Softskills Training
from Day 1
Build Your Resume
Build your LinkedIn Profile
Build your GitHub
digital portfolio
Panel Mock Interview
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FAQs for
The Data Science with R Training Course
How do R and Python differ for data science tasks?
1.
R is tailored for statistical analysis and data visualization, offering a rich set of libraries for these functions. Python, while also capable in data science, is a more versatile language with applications extending beyond data analysis, such as web development. R is preferred for in-depth statistical modeling, while Python excels in integration and a broad range of programming tasks.
What methods are available in R to manage missing data in a dataset?
2.
R provides various methods for dealing with missing data, including na.omit() for removing rows with missing values, is.na() for detecting them, and imputation functions from packages like mice or Amelia to estimate and fill in missing values. The choice of method depends on the dataset and the analysis needs.
Which techniques can be used for feature selection in R?
3.
In R, feature selection techniques include functions from the caret package such as findCorrelation() for removing highly correlated features and rfe() for recursive feature elimination. Other useful packages for feature selection are Boruta and FeatureSelection.
How can interactive visualizations be created in R?
4.
Interactive visualizations in R can be created using packages like shiny, which enables the development of interactive web applications, and plotly, which offers interactive charts and integrates with ggplot2 for dynamic visualizations.
Which R packages are essential for machine learning tasks?
5.
Key R packages for machine learning include caret for model training and evaluation, randomForest for decision tree-based models, xgboost for gradient boosting, and e1071 for support vector machines and additional algorithms.
How is time series analysis conducted in R?
6.
Time series analysis in R can be performed using packages like forecast for model building and evaluation, xts and zoo for managing time series data, and TTR for technical trading rules and indicators.
What are some popular data visualization libraries in R and their features?
7.
Popular data visualization libraries in R include ggplot2, known for its comprehensive grammar of graphics for diverse plot types; lattice, which supports trellis graphics; and plotly, which enables interactive and dynamic visualizations.
techniques can be employed to enhance the performance of R code?
8.
To improve R code performance, techniques such as vectorization to eliminate loops, utilizing efficient data manipulation packages like data.table, profiling code with profvis to pinpoint bottlenecks, and applying parallel processing with packages like foreach or parallel can be employed.
Where is the corporate office of Softlogic Systems located?
9.
The corporate office of the Softlogic Systems is located at the institute’s K.K.Nagar branch.
What payment methods does Softlogic accept?
10.
Softlogic accepts a wide range of payment methods, including:
- Cash
- Debit cards
- Credit cards (MasterCard, Visa, Maestro)
- Net banking
- UPI
- Including EMI.
Additional Information for
The Data Science with R Training Course
1.
Scopes available in the future for learning Data Science with R.
The following are the scopes available in the future for learning the Data Science with R Course:
- Advanced Machine Learning and AI Integration: As machine learning and AI technologies evolve, R’s role in developing and implementing complex models will continue to grow. With advancing packages like xgboost and caret, R will remain essential for training sophisticated algorithms and models.
- Big Data Analytics: With the rise of big data, R’s integration with tools such as Hadoop and Apache Spark will become more significant. R’s capability to manage and analyze large datasets effectively will enhance its importance in large-scale data analysis.
- Data Visualization Innovations: R’s data visualization capabilities are set to advance with new libraries and tools. Innovations in interactive and immersive visualizations through packages like ggplot2, plotly, and shiny will play a crucial role in data exploration and presentation.
- Healthcare and Biomedical Research: The use of R in healthcare and biomedical research will expand as fields like personalized medicine and genomics grow. R will be instrumental in analyzing complex biological data, modeling patient outcomes, and supporting clinical research.
- Real-Time Data Processing: The growing need for real-time data analysis across various industries will highlight R’s capabilities in processing and analyzing live data streams. Improvements in relevant packages and integration with streaming technologies will drive this trend.
- Financial Analytics and Risk Management: R will continue to be valuable in finance for tasks such as risk management, trading algorithms, and financial modeling. Its robust statistical tools will be leveraged to develop innovative financial strategies and assess risks.
- Education and Training: As data science skills become increasingly sought after, R will remain a core component of educational programs and professional training. New resources and tools will emerge to support learning and proficiency in R.
- Collaboration and Integration with Other Tools: R’s ability to integrate with other languages and tools, such as Python and SQL, will enhance its versatility. Improved collaboration features and interoperability with various platforms will expand its use in diverse workflows.
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