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Free Download R Programming For Data Science- Practise 250 Exercises-Part2 Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 644.41 MB | Duration: 3h 0m Level Up Your Skills: Advanced Challenges & Expert Insights in R Programming! What you'll learn Develop a strong foundation in R programming by solving diverse exercises, reinforcing key concepts like data types, control structures, and functions. Gain hands-on experience with popular R libraries such as dplyr, ggplot2, tidyverse, and caret to manipulate and visualize datasets effectively. Apply data wrangling techniques to clean, transform, and organize real-world datasets using R. Master data visualization by creating insightful and professional-quality plots with ggplot2 and other visualization libraries. Enhance your statistical analysis skills by performing descriptive statistics, hypothesis testing, and regression analysis in R. Explore different datasets available in R and use them to practice machine learning algorithms such as linear regression, classification, and clustering. Debug and optimize R code by identifying common errors and applying best practices for efficient coding. Prepare for real-world data science challenges by solving exercises that reflect common tasks in data analysis and machine learning projects. Requirements Basic understanding of R programming: Familiarity with R syntax, variables, data types, and basic functions. Introduction to data structures in R: Knowledge of common data structures like vectors, data frames, and lists. Passion to become Data Scientist Internet connection and Laptop Description Welcome to R Programming for Data Science - Practice 250 Exercises: Part 2! If you're ready to take your R programming skills to the next level, this course is the ultimate hands-on experience you've been waiting for. Designed for data enthusiasts, aspiring data scientists, and R programmers, this course brings you 250 brand-new challenges that will deepen your understanding of R programming, data analysis, and machine learning.Whether you're continuing from Part 1 or just starting here, this course promises to engage, challenge, and refine your skills in real-world applications of R. Dive into problem-solving scenarios, practice advanced techniques, and get ready to supercharge your data science career!10 Reasons Why You Should Enroll in This Course:250 New Exercises: Gain practical, hands-on experience with 250 fresh challenges that will test your R programming skills.Real-World Data Science Scenarios: Solve exercises designed to mimic real data science problems, giving you valuable experience that you can apply in your job.Advanced R Concepts: This course builds on foundational R knowledge, introducing more advanced topics such as data visualization, statistical analysis, and machine learning.Project-Based Learning: Learn by doing! Each exercise is a mini-project that will help you understand complex concepts in a simple, practical way.Self-Paced Learning: Enjoy the flexibility to learn at your own speed, whether you're a full-time student or a working professional.Skill-Building for Data Science: Strengthen your R programming and data science abilities, making you more competitive in the job market.Instant Feedback & Solutions: Get access to detailed solutions and explanations for each exercise, so you can learn from your mistakes and improve rapidly.Perfect for Career Growth: Whether you're aiming for a data scientist, analyst, or R programming role, this course will provide the expertise you need to succeed.Expand Your Data Science Toolkit: Learn to use R effectively for data manipulation, analysis, and visualization, essential tools for any data science professional.Supportive Learning Environment: Benefit from an active Q&A section and a community of learners who are just as passionate about data science as you are.Enroll now and take your R programming skills to the next level with R Programming for Data Science - Practice 250 Exercises: Part 2! Overview Section 1: Introduction Lecture 1 Welcome to the Course Lecture 2 Introduction to AI and ML Lecture 3 Introduction to R Programming Lecture 4 Art of Good Programming Lecture 5 Course Overview Section 2: 251-270 Lecture 6 Problem 251 Lecture 7 Soln 251 Lecture 8 Problem 252 Lecture 9 Soln 252 Lecture 10 Problem 253 Lecture 11 Soln 253 Lecture 12 Problem 254 Lecture 13 Soln 254 Lecture 14 Problem 255 Lecture 15 Soln 255 Lecture 16 Problem 256 Lecture 17 Soln 256 Lecture 18 Problem 257 Lecture 19 Soln 257 Lecture 20 Problem 258 Lecture 21 Soln 258 Lecture 22 Problem 259 Lecture 23 Soln 259 Lecture 24 Problem 260 Lecture 25 Soln 260 Lecture 26 Problem 261 Lecture 27 Soln 261 Lecture 28 Problem 262 Lecture 29 Soln 262 Lecture 30 Problem 263 Lecture 31 Soln 263 Lecture 32 Problem 264 Lecture 33 Soln 264 Lecture 34 Problem 265 Lecture 35 Soln 265 Lecture 36 Problem 266 Lecture 37 Soln 266 Lecture 38 Problem 267 Lecture 39 Soln 267 Lecture 40 Problem 268 Lecture 41 Soln 268 Lecture 42 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Lecture 479 Soln 487 Lecture 480 Problem 488 Lecture 481 Soln 488 Lecture 482 Problem 489 Lecture 483 Soln 489 Lecture 484 Problem 490 Lecture 485 Soln 490 Section 14: 491-500 Lecture 486 Problem 491 Lecture 487 Soln 491 Lecture 488 Problem 492 Lecture 489 Soln 492 Lecture 490 Problem 494 Lecture 491 Soln 494 Lecture 492 Problem 495 Lecture 493 Soln 495 Lecture 494 Problem 496 Lecture 495 Soln 496 Lecture 496 Problem 497 Lecture 497 Soln 497 Lecture 498 Problem 498 Lecture 499 Soln 498 Lecture 500 Problem 499 Lecture 501 Soln 499 Lecture 502 Problem 500 Lecture 503 Soln 500 Aspiring Data Scientists: Those looking to build a strong foundation in R programming while solving real-world data science problems.,Students and Academics: Learners studying data science or related fields who want hands-on practice with R and its various libraries and datasets.,Professionals in Data-Driven Roles: Individuals working in fields like business analytics, finance, healthcare, or marketing who want to enhance their data analysis skills using R.,Self-Learners and Coding Enthusiasts: Those passionate about learning R programming through practical exercises and improving their coding proficiency in data science projects. Homepage https://www.udemy.com/course/r-programming-for-data-science-practise-250-exercises-part2/ Rapidgator https://rg.to/file/0c09915486d9be403aa8f4e81b94e3aa/owfxy.R.Programming.For.Data.Science.Practise.250.ExercisesPart2.rar.html Fikper Free Download https://fikper.com/M58XAPzwpT/owfxy.R.Programming.For.Data.Science.Practise.250.ExercisesPart2.rar.html No Password - Links are Interchangeable

Free Download R Programming for Data Science- Practise 250 Exercises-Part1 Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 2h 55m | Size: 608 MB Learn by Doing: Practical R Programming with Data Frames, ggplot2, and dplyr for Data Science using RStudio What you'll learn Develop a strong foundation in R programming by solving diverse exercises, reinforcing key concepts like data types, control structures, and functions. Gain hands-on experience with popular R libraries such as dplyr, ggplot2, tidyverse, and caret to manipulate and visualize datasets effectively. Apply data wrangling techniques to clean, transform, and organize real-world datasets using R. Master data visualization by creating insightful and professional-quality plots with ggplot2 and other visualization libraries. Enhance your statistical analysis skills by performing descriptive statistics, hypothesis testing, and regression analysis in R. Explore different datasets available in R and use them to practice machine learning algorithms such as linear regression, classification, and clustering. Debug and optimize R code by identifying common errors and applying best practices for efficient coding. Prepare for real-world data science challenges by solving exercises that reflect common tasks in data analysis and machine learning projects. Requirements Basic understanding of programming concepts Introductory knowledge of R programming Familiarity with basic statistics and data analysis Description This course is designed to help you master R programming through 250 practical, hands-on exercises. Whether you're a beginner or looking to strengthen your R skills, this course covers a wide range of topics that are essential for data science. Let's dive into what this course has to offer! 1. Learn the Fundamentals of R ProgrammingStart by understanding the core concepts of R programming, including variables, data types, and basic syntax. These exercises will give you the foundation needed to tackle more advanced topics later in the course.2. Master Data Cleaning and TransformationGain practical experience with data wrangling using popular libraries like dplyr and tidyverse. Learn to clean, transform, and organize real-world datasets, preparing them for analysis.3. Visualize Data Using ggplot2Data visualization is crucial in data science. In this section, you'll work with ggplot2 to create informative and attractive plots. This will help you gain insights from your data more effectively. 4. Explore Statistical Analysis TechniquesGet hands-on practice with statistics in R, learning how to calculate mean, median, variance, and standard deviation. You will also perform hypothesis testing and regression analysis. 5. Apply Machine Learning AlgorithmsWork on basic machine learning techniques like linear regression, classification, and clustering using real datasets. This section will help you understand how to apply machine learning models in R. 6. Practice Debugging and Code OptimizationAs you progress, you'll encounter coding challenges that will sharpen your debugging and optimization skills. Learn how to identify and fix errors in your code while ensuring it runs efficiently. 7. Work with Real-World DatasetsThroughout the course, you'll be working with various real-world datasets available in R. From health statistics to economic data, these datasets provide a diverse range of challenges to solve. 8. Test Your Knowledge with Challenging ExercisesEach problem is designed to test your knowledge and improve your understanding of R. By the end of the course, you'll be equipped to apply R programming in real-world data science projects.9. Get Ready for Part 2!Once you've completed Part 1, you're encouraged to enroll in "R Programming for Data Science-Practice 250 Questions-Part 2" for even more advanced exercises and deeper insights into R programming. Keep the momentum going and continue mastering your skills! Who this course is for Aspiring data scientists looking to strengthen their R programming skills through hands-on practice. R programmers seeking to improve their problem-solving abilities and apply advanced R libraries in real-world data analysis. Students and professionals in data science who want to enhance their understanding of data manipulation, visualization, and machine learning in R. Self-learners and enthusiasts interested in applying R to solve diverse data challenges using real-world datasets. Anyone preparing for data science job interviews or certifications that require proficiency in R programming and data analysis techniques. Homepage https://www.udemy.com/course/r-programming-for-data-science-practise-250-exercises-part1/ Rapidgator https://rg.to/file/d39fdceb2ab76f896938afbc184c46ab/htixc.R.Programming.for.Data.Science.Practise.250.ExercisesPart1.rar.html Fikper Free Download https://fikper.com/TBRjF0pA39/htixc.R.Programming.for.Data.Science.Practise.250.ExercisesPart1.rar.html No Password - Links are Interchangeable

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