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Bundle course - Data Science & Analytics with R

Master R for AI, Data Science, Machine Learning, and Data Visualization – A Complete Career-Oriented Bundle
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The Data Science & Analytics with R bundle is a comprehensive, hands-on learning program tailored for aspiring data scientists, analysts, and professionals seeking to gain end-to-end expertise in R programming and its applications in data science, machine learning, and visualization. This bundle combines five robust sub-courses into one powerful learning journey—covering everything from foundational R programming to advanced analytical projects.

Included Sub-Courses:

  • Artificial Intelligence, Data Science and Machine Learning with R

  • Data Science with R

  • Data Visualization in R

  • R Programming (Basic to Advanced)

  • Project on Data Visualization with R

This course equips learners to handle the complete data pipeline: clean and process data, apply machine learning algorithms, visualize insights, and deliver business-ready analytics using R—a language trusted by statisticians, researchers, and top data teams.

What You Will Learn

  • Master R programming, including data types, control structures, functions, and libraries.

  • Build machine learning models using R (classification, regression, clustering).

  • Perform exploratory data analysis and data wrangling.

  • Create compelling and interactive data visualizations using ggplot2, plotly, and Shiny.

  • Understand AI and predictive analytics using real-world datasets.

  • Complete a capstone project focused on visual storytelling and analytics with R.

Who This Course Is For

  • Aspiring data scientists and statisticians with an interest in R.

  • Data analysts seeking to transition into data science and AI roles.

  • Business professionals and researchers who want to extract insights from data.

  • Students and graduates aiming for careers in analytics, research, or academia.

  • Professionals preparing for R-based data science certifications.

Course/Topic 1 - Course access through Google Drive

  • Google Drive

    • 01:20

  • Google Drive

    • 01:20

Course/Topic 2 - R Programming (basic to advanced) - all lectures

  • In this lecture session we learn about basic introduction to R programming and also talk about some key features of R programming.

    • 16:58

  • In this lecture session we learn about the setup of R language in your system and also talk about the importance of R programming.

    • 24:13

  • In this lecture session we learn about variables and data types in R language and also talk about types of variables and data types in R programming.

    • 31:36

  • In this lecture session we learn about uses of variable and data types in our programs and also talk about some key features of variables and data types.

    • 29:16

  • In this lecture session we learn about input - output features and also talk about features of input - output features.

    • 38:05

  • In this lecture session we learn about posted function () in input output features and also talk about features of posted functions().

    • 25:43

  • In this lecture session we learn about operators in R and also talk about features of operators in R programming.

    • 34:16

  • In this lecture session we learn about different types of operators in R language and also talk about features of all types of operators in R.

    • 29:19

  • In this lecture session we learn about vectors in data structure in R programming and also talk about features of vectors in data structures in brief.

    • 32:29

  • In this lecture session we learn about the importance of vectors in data structure and also talk about vectors in data structures in brief.

    • 30:29

  • In this lecture session we learn about list data structure in R programming and also talk about features of list in data structure.

    • 33:47

  • In this lecture session we learn about more operations on the list and also talk about features of List in data structures in brief.

    • 29:10

  • In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.

    • 43:57

  • In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.

    • 25:15

  • In this lecture session we learn about matrix data structure in R programming and also talk about some key features of matrix and data structure.

    • 09:36

  • In this lecture session we learn about arrays in R programming and also talk about features of arrays.

    • 31:56

  • In this lecture session we learn about different types of arrays in data structure and also talk about features of Arrays in data structure in brief.

    • 38:35

  • In this lecture session we learn about data frame in R programming and also talk about function of data frame.

    • 43:06

  • In this lecture session we learn about data frame features in R programming and also talk about the importance of data structure.

    • 33:59

  • In this lecture session we learn about the importance of Data frame in brief and also talk about function of data frame in R programming.

    • 31:20

  • In this lecture session we learn about data frame key features of data frame in data structure in brief.

    • 19:40

  • In this lecture session we learn about factors data structures in R programming and also talk about the importance of factors.

    • 34:09

  • In this lecture session we learn about factors of data structure in R programming and also talk about different types of factors in R language.

    • 17:14

  • In this lecture session we learn about decision making in R programming and also talk about features of decision making in R.

    • 32:52

  • In this lecture session we learn about different types of decision making statements and also talk about features of all decision statements.

    • 15:53

  • In this lecture session we learn about decision making using integers and also talk about functions of integers.

    • 30:02

  • In this lecture session we learn about Loops in R programming and also talk about factors of Loops in R language.

    • 28:28

  • In this lecture session we learn about functions of Loops and why we need Loop statement in R programming and also talk about key features of Loop statement.

    • 32:03

  • In this lecture session we learn about different types of Loops in R programming and also talk about features of For loop, while loop and do while loop.

    • 25:29

  • In this lecture session we learn about functions in R programming language and also talk about features of functions in R language.

    • 37:42

  • In this lecture session we learn about different types of functions in R programming and also talk about the importance of functions.

    • 34:11

  • In this lecture session we learn about string in R programming and also talk about features of string function in R.

    • 25:18

  • In this lecture session we learn about why we need strings in R programming and also talk about the importance of strings.

    • 26:51

  • In this lecture session we learn about packages in R programming and also talk about features of packages in R.

    • 35:17

  • In this lecture session we learn about data and file management in R programming and also talk about functions of data and file management.

    • 32:45

  • In this lecture session we learn about how we manage the data and file in R programming and also talk about the importance of data and file management.

    • 23:18

  • In this lecture session we learn about Line chart in R programming and also talk about features of line chart in brief.

    • 33:06

  • In this lecture session we learn about scatterplot in R language and also talk about functions of scatters plot.

    • 26:38

  • In this lecture session we learn about Pie chart in R programming and also talk about features of Pie Chart in brief.

    • 33:32

  • In this lecture session we learn about bar charts in R language and also talk about features of Bar chart in brief.

    • 09:43

  • In this lecture session we learn about how we use bar charts in R programming and also talk about features of Bar charts.

    • 29:50

  • In this lecture session we learn about histogram in R programming and also talk about features of histogram.

    • 26:34

  • In this lecture session we learn about Boxplots in R programming and also talk about features of Boxplot in R language.

    • 21:49

Course/Topic 3 - Data Visualization in R - all lectures

  • In this introductory tutorial on Data Visualization in R Programming, you will learn about what is data visualization, the type of graph or chart one should select for data visualization, what is the importance and benefits of data visualization and finally what are the applications of data visualization.

    • 28:24

  • In this video, you will learn how to work on the Histogram, which falls under different Chart types used in Data Visualization in R Programming; along with working on the bar chart, box plot and heat map. You will be seeing a detailed explanation by the instructor on the complete workaround of these by taking different examples.

    • 42:00

  • In this video, you will learn what is density plot and how you can create the density plot by taking different examples for it. You will also learn about the different applications being used in the density plot under Data Visualization with R Programming.

    • 17:28

  • In this tutorial, you will learn about Data Visualization with GGPLOT2 Package where inside it you will learn the overview of GGPLOT2, iteratively building plots, univariate distributions and bar plot, annotation with GGPLOT2, axis manipulation and the density plot. You will get a complete understanding of the theoretical concept along with the implementation of each of these.

    • 41:07

  • In this second part of the video tutorial, you will learn about Plotting with GGPLOT2 and building your plots iteratively, along with the importance of the ‘+’ symbol and its use in the GGPLOT2 work process. You will be seeing a detailed explanation from the instructor by taking different examples.

    • 27:40

  • In this video you will learn about the complete theoretical and practical implementation of Univariate Distribution and Bar Plot, which can be seen explained in complete details by the instructor throughout the tutorial.

    • 30:42

  • In this tutorial, you will learn about annotation with ggplot2, along with geom text () and adding labels with geom label () with complete explanation on this by the instructor with the help of different examples.

    • 50:28

  • In this tutorial, you will learn about Axis Manipulation with ggplot2, its complete overview and in-depth concepts along with the different functions used during the process. You will be seeing explaining the topic in complete details by the instructor by taking examples and working in R studio.

    • 34:13

  • In this section, you will learn about Text Mining and Word Cloud, along with the Radar Chart, Waffle Chart, Area Chart and the Correlogram. In this first part of the video, you will learn about the Text Mining and Word Cloud, the different reasons behind using Word Cloud for text data, who is using Word Clouds and the various steps involved in creating word clouds.

    • 31:43

  • In this video, you will learn how to execute data using redline function. Also, you will understand the usage of corpus function and content transformer function. Further, you will understand about the text stemming, Term Document Matrix function and the Max word’s function.

    • 31:49

  • In this tutorial, you will learn about the Radar Chart, the function used in the Radar Chart which is gg Radar (), scales, mapping and the use label. Along with this, you will also learn how to create Radar Chart in R studio. Moreover, you will learn about the Waffle Chart in R and how to create vector data in Waffle Chart with the help of different examples.

    • 34:18

  • In this last part of the session, you will learn about the Area Chart, its in-depth concepts and how to work on it. This will be seen explained in detail by the instructor. Moreover, you will also learn about the Correlogram in R, the correlation matrix, Mt cars and the work around on different visualization methods been used.

    • 39:54

  • This is a project tutorial titled Visualizing COVID-19 where you will see the different scenarios being explained by the tutor on visualizing COVID-19 data and how it can be done through Data Visualization in R process. In this first part, you will understand the complete overview of the project, its description and the different tasks associated with it being done by the ggplot.

    • 34:59

  • In this second part of the project video, you will learn about the “Annotate” process and the number of COVID cases being reported in China with the help of Data Visualization. You will be seeing the task performed on the dataset being provided by the WHO along with understanding the tribble function and how it will help during the entire work process.

    • 37:38

  • In this last part of the session, you will understand the work around of the task being done with the help of plot. You will see a detailed explanation by the instructor seeking help of few examples to explain the complete process of plotting in respect to the COVID-19 project being implemented.

    • 34:50

Course/Topic 4 - Project on Data Visualization with R - all lectures

  • This is a project tutorial titled Visualizing COVID-19 where you will see the different scenarios being explained by the tutor on visualizing COVID-19 data and how it can be done through Data Visualization in R process. In this first part, you will understand the complete overview of the project, its description and the different tasks associated with it being done by the ggplot.

    • 34:59

  • In this second part of the project video, you will learn about the “Annotate” process and the number of COVID cases being reported in China with the help of Data Visualization. You will be seeing the task performed on the dataset being provided by the WHO along with understanding the tribble function and how it will help during the entire work process.

    • 37:38

  • In this last part of the session, you will understand the work around of the task being done with the help of plot. You will see a detailed explanation by the instructor seeking help of few examples to explain the complete process of plotting in respect to the COVID-19 project being implemented.

    • 34:50

Course/Topic 5 - Data Science with R - all lectures

  • In this lecture session we learn about introduction of data science and also talk about features of data science in R.

    • 54:03

  • In this lecture session we learn about data collection and management and also talk about features of data collection and management in data science with R.

    • 29:32

  • In this lecture session we learn about model deployment and maintenance and also talk about functions of model deployment and maintenance in data science with R.

    • 12:47

  • In this lecture session we learn about setting expectations and also talk about factors of setting expectations in brief.

    • 10:18

  • In this lecture session we learn about loading data into R and also talk about features of loading data into R and also talk about the importance of loading data into R.

    • 42:20

  • In this lecture session we learn about exploring data in data science and machine learning and also talk about features of exploring data in data science and machine learning.

    • 08:01

  • In this lecture session we learn about features of exploring data using R and also talk about factors of exploring data using R.

    • 45:36

  • In this lecture session we learn about benefits of data cleaning and also talk about features of benefits of data cleaning.

    • 22:44

  • In this lecture session we learn about cross validation in R and also talk about features of validation in data science with R.

    • 17:32

  • In these lecture sessions we learn about data transformation in data science with R and also talk about features of data transformation in brief.

    • 1:35:26

  • In this lecture session we learn about modeling methods in data science with R and also talk about the importance of modeling methods.

    • 20:13

  • In this lecture session we learn about solving classification problems and also talk about features of solving classification problems in brief.

    • 11:55

  • In this lecture session we learn about working without known targets in data science with r and also talk about features of working without known targets.

    • 19:58

  • In this lecture session we learn about evaluating models in data science with R and also talk about features of evaluating models in brief.

    • 28:11

  • In this lecture session we learn about confusion matrix in indian accounting standards and also talk about features of confusion matrix.

    • 34:03

  • In this lecture session we learn about introduction to linear regression and also talk about features of linear regression in indian accounting standards.

    • 1:25:24

  • In this lecture session we learn about linear regression in R and also talk about features and functions of linear regression in brief.

    • 26:51

  • In this lecture session we learn about linear regression in R in data science with r and also talk about features of linear regression in R language.

    • 41:22

  • In this lecture session we learn about simple and multiple regression in data science with r and also talk about the basic difference between simple and multiple regression in brief.

    • 56:54

  • In this lecture session we learn about linear and logistic regression in data science with r language and also talk about functions of linear and logistics regressions.

    • 29:09

  • In this lecture session we learn about support vector machines (SVM) in R and also talk about features of support vector machines in data science with R language.

    • 45:18

  • In this lecture session we learn about factors of support vectors machines in data science with R and also talk about features of support vectors machines.

    • 1:30:55

  • In this lecture session we learn about unsupervised methods in data science with R and also talk about functions of unsupervised methods in data science.

    • 24:44

  • In this lecture session we learn about clustering in data science with R language and also talk about features of clustering in data science.

    • 50:44

  • In this lecture session we learn about K-means algorithms in R and also talk about all types of algorithms in data science with R language.

    • 1:09:44

  • In this lecture session we learn about hierarchical clustering in data science with R language and also talk about features of hierarchical clustering.

    • 33:41

  • In this lecture session we learn about libraries in data science with R and also talk about libraries of hierarchical clustering in brief.

    • 23:15

  • In this lecture session we learn about the dendrogram of diana and also talk about all types of clustering in data science with R.

    • 41:05

  • In this lecture session we learn about market basket analysis in data science with R and also talk about features of market basket analysis in data science with R.

    • 05:08

  • In this lecture session we learn about MBA and association rule mining in data science with r language.

    • 23:52

  • In this lecture session we learn about implementing MBA in data science with R and also talk about implementing MBA.

    • 09:18

  • In this lecture session we learn about association rule learning in data science with R and also talk about features of association rule learning.

    • 24:02

  • In this lecture session we learn about decision tree algorithms in data science with R and also talk about features of tree algorithms.

    • 36:29

  • In this lecture session we learn about exploring advanced methods in tree algorithms in data science with R and also talk about features of exploring advanced methods.

    • 48:16

  • In this lecture session we learn about using kernel methods and also talk about features of using kernel methods in data science with R.

    • 47:43

  • In this lecture session we learn about documentation and deployment and also talk about features of documentation and deployment in data science with R.

    • 30:09
Course Objectives Back to Top

By the end of this bundle course, learners will:

  • Gain practical proficiency in R programming and its data analysis ecosystem.

  • Build and evaluate AI/ML models using R’s machine learning libraries.

  • Develop insightful dashboards and charts using ggplot2, plotly, and Shiny.

  • Conduct data cleaning, feature selection, and model validation.

  • Deliver a full-scale data visualization project from raw data to final presentation.

Course Syllabus Back to Top

Course Syllabus

  1. Artificial Intelligence, Data Science and Machine Learning with R

    • Introduction to AI, ML, and R ecosystem

    • Supervised and unsupervised learning techniques

    • Decision trees, random forests, SVM, KNN, and clustering in R

    • Model tuning, validation, and accuracy metrics

  2. Data Science with R

    • Data import, manipulation, and transformation

    • Exploratory Data Analysis (EDA)

    • Statistical modeling and hypothesis testing

    • Time series and forecasting techniques

  3. Data Visualization in R

    • Data storytelling principles

    • Visualizing distributions, trends, and correlations

    • Advanced plots with ggplot2, lattice, and plotly

    • Interactive web visualizations with Shiny

  4. R Programming (Basic to Advanced)

    • Variables, loops, functions, and data structures

    • Working with packages (dplyr, tidyr, stringr, etc.)

    • File I/O, error handling, and regular expressions

    • Writing reusable R scripts and automation

  5. Project on Data Visualization with R

    • Case study selection and dataset preparation

    • Building dashboards and reports using Shiny

    • End-to-end visualization project: data cleaning, insights, storytelling

    • Final project presentation and evaluation

Certification Back to Top

Upon successful completion, learners will receive a Certificate of Completion from Uplatz, validating their expertise in data science and analytics using R. This certificate strengthens professional credentials and showcases job-readiness in data analytics, machine learning, and visualization.

Career & Jobs Back to Top

This course prepares learners for dynamic roles such as:

  • Data Analyst (R)

  • Data Scientist

  • Machine Learning Analyst

  • R Programmer

  • Business Intelligence Analyst

  • Research Data Analyst

R professionals are in demand in industries like healthcare, finance, government, research, and tech.

Interview Questions Back to Top

  1. What are the strengths of R compared to Python for data science?

  2. How do you handle missing data in R?

  3. What are some popular R packages used for data manipulation?

  4. Explain the difference between apply, lapply, and sapply.

  5. How do you build a linear regression model in R?

  6. What is the purpose of ggplot2 and how does it differ from base R plotting?

  7. How do you deploy interactive dashboards with Shiny?

  8. What is overfitting and how do you detect it in an R-based model?

  9. Explain how clustering works and which algorithm you’d choose in R.

  10. How do you interpret a confusion matrix in R?

Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

Q3. How long is the course access?
A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

Q4. Are the videos downloadable?
A4. Video courses cannot be downloaded, but you have lifetime access to any video course you purchase on our website. You will be able to play the videos on our our website and mobile app.

Q5. Do you take exam? Do I need to pass exam? How to book exam?
A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

Q6. Can I get study material with the course?
A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php

Q7. What is your refund policy?
A7. Please refer to our Refund policy mentioned on our website, here is the link to Uplatz refund policy https://training.uplatz.com/refund-and-cancellation-policy.php

Q8. Do you provide any discounts?
A8. We run promotions and discounts from time to time, we suggest you to register on our website so you can receive our emails related to promotions and offers.

Q9. What are overview courses?
A9. Overview courses are 1-2 hours short to help you decide if you want to go for the full course on that particular subject. Uplatz overview courses are either free or minimally charged such as GBP 1 / USD 2 / EUR 2 / INR 100

Q10. What are individual courses?
A10. Individual courses are simply our video courses available on Uplatz website and app across more than 300 technologies. Each course varies in duration from 5 hours uptop 150 hours. Check all our courses here https://training.uplatz.com/online-it-courses.php?search=individual

Q11. What are bundle courses?
A11. Bundle courses offered by Uplatz are combo of 2 or more video courses. We have Bundle up the similar technologies together in Bundles so offer you better value in pricing and give you an enhaced learning experience. Check all Bundle courses here https://training.uplatz.com/online-it-courses.php?search=bundle

Q12. What are Career Path programs?
A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

Q13. What are Learning Path programs?
A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done

Q14. What are Premium Career tracks?
A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive.

Q15. How unlimited subscription works?
A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription

Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

Q19. How do I enrol in Uplatz video courses?
A19. To enroll, click on "Buy This Course," You will see this option at the top of the page.
a) Choose your payment method.
b) Stripe for any Credit or debit card from anywhere in the world.
c) PayPal for payments via PayPal account.
d) Choose PayUmoney if you are based in India.
e) Start learning: After payment, your course will be added to your profile in the student dashboard under "Video Courses".

Q20. How do I access my course after payment?
A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.
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