Znajdź zawartość

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 Problem 269 Lecture 43 Soln 269 Lecture 44 Problem 270 Lecture 45 Soln 270 Section 3: 271-290 Lecture 46 Problem 271 Lecture 47 Soln 271 Lecture 48 Problem 272 Lecture 49 Soln 272 Lecture 50 Problem 273 Lecture 51 Soln 273 Lecture 52 Problem 274 Lecture 53 Soln 274 Lecture 54 Problem 275 Lecture 55 Soln 275 Lecture 56 Problem 276 Lecture 57 Soln 276 Lecture 58 Problem 277 Lecture 59 Soln 277 Lecture 60 Problem 278 Lecture 61 Soln 278 Lecture 62 Problem 279 Lecture 63 Soln 279 Lecture 64 Problem 280 Lecture 65 Soln 280 Lecture 66 Problem 281 Lecture 67 Soln 281 Lecture 68 Problem 282 Lecture 69 Soln 282 Lecture 70 Problem 283 Lecture 71 Soln 283 Lecture 72 Problem 284 Lecture 73 Soln 284 Lecture 74 Problem 285 Lecture 75 Soln 285 Lecture 76 Problem 286 Lecture 77 Soln 286 Lecture 78 Problem 287 Lecture 79 Soln 287 Lecture 80 Problem 288 Lecture 81 Soln 288 Lecture 82 Problem 289 Lecture 83 Soln 289 Lecture 84 Problem 290 Lecture 85 Soln 290 Section 4: 291-310 Lecture 86 Problem 291 Lecture 87 Soln 291 Lecture 88 Problem 292 Lecture 89 Soln 292 Lecture 90 Problem 293 Lecture 91 Soln 293 Lecture 92 Problem 294 Lecture 93 Soln 294 Lecture 94 Problem 295 Lecture 95 Soln 295 Lecture 96 Problem 296 Lecture 97 Soln 296 Lecture 98 Problem 297 Lecture 99 Soln 297 Lecture 100 Problem 298 Lecture 101 Soln 298 Lecture 102 Problem 299 Lecture 103 Soln 299 Lecture 104 Problem 300 Lecture 105 Soln 300 Lecture 106 Problem 301 Lecture 107 Soln 301 Lecture 108 Problem 302 Lecture 109 Soln 302 Lecture 110 Problem 303 Lecture 111 Soln 303 Lecture 112 Problem 304 Lecture 113 Soln 304 Lecture 114 Problem 305 Lecture 115 Soln 305 Lecture 116 Problem 306 Lecture 117 Soln 306 Lecture 118 Problem 307 Lecture 119 Soln 307 Lecture 120 Problem 308 Lecture 121 Soln 308 Lecture 122 Problem 309 Lecture 123 Soln 309 Lecture 124 Problem 310 Lecture 125 Soln 310 Section 5: 311-330 Lecture 126 Problem 311 Lecture 127 Soln 311 Lecture 128 Problem 312 Lecture 129 Soln 312 Lecture 130 Problem 313 Lecture 131 Soln 313 Lecture 132 Problem 314 Lecture 133 Soln 314 Lecture 134 Problem 315 Lecture 135 Soln 315 Lecture 136 Problem 316 Lecture 137 Soln 316 Lecture 138 Problem 317 Lecture 139 Soln 317 Lecture 140 Problem 318 Lecture 141 Soln 318 Lecture 142 Problem 319 Lecture 143 Soln 319 Lecture 144 Problem 320 Lecture 145 Soln 320 Lecture 146 Problem 321 Lecture 147 Soln 321 Lecture 148 Problem 322 Lecture 149 Soln 322 Lecture 150 Problem 323 Lecture 151 Soln 323 Lecture 152 Problem 324 Lecture 153 Soln 324 Lecture 154 Problem 325 Lecture 155 Soln 325 Lecture 156 Problem 326 Lecture 157 Soln 326 Lecture 158 Problem 327 Lecture 159 Soln 327 Lecture 160 Problem 328 Lecture 161 Soln 328 Lecture 162 Problem 329 Lecture 163 Soln 329 Lecture 164 Problem 330 Lecture 165 Soln 330 Section 6: 331-350 Lecture 166 Problem 331 Lecture 167 Soln 331 Lecture 168 Problem 332 Lecture 169 Soln 332 Lecture 170 Problem 333 Lecture 171 Soln 333 Lecture 172 Problem 334 Lecture 173 Soln 334 Lecture 174 Problem 335 Lecture 175 Soln 335 Lecture 176 Problem 336 Lecture 177 Soln 336 Lecture 178 Problem 337 Lecture 179 Soln 337 Lecture 180 Problem 338 Lecture 181 Soln 338 Lecture 182 Problem 339 Lecture 183 Soln 339 Lecture 184 Problem 340 Lecture 185 Soln 340 Lecture 186 Problem 341 Lecture 187 Soln 341 Lecture 188 Problem 342 Lecture 189 Soln 342 Lecture 190 Problem 343 Lecture 191 Soln 343 Lecture 192 Problem 344 Lecture 193 Soln 344 Lecture 194 Problem 345 Lecture 195 Soln 345 Lecture 196 Problem 346 Lecture 197 Soln 346 Lecture 198 Problem 347 Lecture 199 Soln 347 Lecture 200 Problem 348 Lecture 201 Soln 348 Lecture 202 Problem 349 Lecture 203 Soln 349 Lecture 204 Problem 350 Lecture 205 Soln 350 Section 7: 351-370 Lecture 206 Problem 351 Lecture 207 Soln 351 Lecture 208 Problem 352 Lecture 209 Soln 352 Lecture 210 Problem 353 Lecture 211 Soln 353 Lecture 212 Problem 354 Lecture 213 Soln 354 Lecture 214 Problem 355 Lecture 215 Soln 355 Lecture 216 Problem 356 Lecture 217 Soln 356 Lecture 218 Problem 357 Lecture 219 Soln 357 Lecture 220 Problem 358 Lecture 221 Soln 358 Lecture 222 Problem 359 Lecture 223 Soln 359 Lecture 224 Problem 360 Lecture 225 Soln 360 Lecture 226 Problem 361 Lecture 227 Soln 361 Lecture 228 Problem 362 Lecture 229 Soln 362 Lecture 230 Problem 363 Lecture 231 Soln 363 Lecture 232 Problem 364 Lecture 233 Soln 364 Lecture 234 Problem 365 Lecture 235 Soln 365 Lecture 236 Problem 366 Lecture 237 Soln 366 Lecture 238 Problem 367 Lecture 239 Soln 367 Lecture 240 Problem 368 Lecture 241 Soln 368 Lecture 242 Problem 369 Lecture 243 Soln 369 Lecture 244 Problem 370 Lecture 245 Soln 370 Section 8: 371-390 Lecture 246 Problem 371 Lecture 247 Soln 371 Lecture 248 Problem 372 Lecture 249 Soln 372 Lecture 250 Problem 373 Lecture 251 Soln 373 Lecture 252 Problem 374 Lecture 253 Soln 374 Lecture 254 Problem 375 Lecture 255 Soln 375 Lecture 256 Problem 376 Lecture 257 Soln 376 Lecture 258 Problem 377 Lecture 259 Soln 377 Lecture 260 Problem 378 Lecture 261 Soln 378 Lecture 262 Problem 379 Lecture 263 Soln 379 Lecture 264 Problem 380 Lecture 265 Soln 380 Lecture 266 Problem 381 Lecture 267 Soln 381 Lecture 268 Problem 382 Lecture 269 Soln 382 Lecture 270 Problem 383 Lecture 271 Soln 383 Lecture 272 Problem 384 Lecture 273 Soln 384 Lecture 274 Problem 385 Lecture 275 Soln 385 Lecture 276 Problem 386 Lecture 277 Soln 386 Lecture 278 Problem 387 Lecture 279 Soln 387 Lecture 280 Problem 388 Lecture 281 Soln 388 Lecture 282 Problem 389 Lecture 283 Soln 389 Lecture 284 Problem 390 Lecture 285 Soln 390 Section 9: 391-410 Lecture 286 Problem 391 Lecture 287 Soln 391 Lecture 288 Problem 392 Lecture 289 Soln 392 Lecture 290 Problem 393 Lecture 291 Soln 393 Lecture 292 Problem 394 Lecture 293 Soln 394 Lecture 294 Problem 395 Lecture 295 Soln 395 Lecture 296 Problem 396 Lecture 297 Soln 396 Lecture 298 Problem 397 Lecture 299 Soln 397 Lecture 300 Problem 398 Lecture 301 Soln 398 Lecture 302 Problem 399 Lecture 303 Soln 399 Lecture 304 Problem 400 Lecture 305 Soln 400 Lecture 306 Problem 401 Lecture 307 Soln 401 Lecture 308 Problem 402 Lecture 309 Soln 402 Lecture 310 Problem 403 Lecture 311 Soln 403 Lecture 312 Problem 404 Lecture 313 Soln 404 Lecture 314 Problem 405 Lecture 315 Soln 405 Lecture 316 Problem 406 Lecture 317 Soln 406 Lecture 318 Problem 407 Lecture 319 Soln 407 Lecture 320 Problem 408 Lecture 321 Soln 408 Lecture 322 Problem 409 Lecture 323 Soln 409 Lecture 324 Problem 410 Lecture 325 Soln 410 Section 10: 411-430 Lecture 326 Problem 411 Lecture 327 Soln 411 Lecture 328 Problem 412 Lecture 329 Soln 412 Lecture 330 Problem 413 Lecture 331 Soln 413 Lecture 332 Problem 414 Lecture 333 Soln 414 Lecture 334 Problem 415 Lecture 335 Soln 415 Lecture 336 Problem 416 Lecture 337 Soln 416 Lecture 338 Problem 417 Lecture 339 Soln 417 Lecture 340 Problem 418 Lecture 341 Soln 418 Lecture 342 Problem 419 Lecture 343 Soln 419 Lecture 344 Problem 420 Lecture 345 Soln 420 Lecture 346 Problem 421 Lecture 347 Soln 421 Lecture 348 Problem 422 Lecture 349 Soln 422 Lecture 350 Problem 423 Lecture 351 Soln 423 Lecture 352 Problem 424 Lecture 353 Soln 424 Lecture 354 Problem 425 Lecture 355 Soln 425 Lecture 356 Problem 426 Lecture 357 Soln 426 Lecture 358 Problem 427 Lecture 359 Soln 427 Lecture 360 Problem 428 Lecture 361 Soln 428 Lecture 362 Problem 429 Lecture 363 Soln 429 Lecture 364 Problem 430 Lecture 365 Soln 430 Section 11: 431-450 Lecture 366 Problem 431 Lecture 367 Soln 431 Lecture 368 Problem 432 Lecture 369 Soln 432 Lecture 370 Problem 433 Lecture 371 Soln 433 Lecture 372 Problem 434 Lecture 373 Soln 434 Lecture 374 Problem 435 Lecture 375 Soln 435 Lecture 376 Problem 436 Lecture 377 Soln 436 Lecture 378 Problem 437 Lecture 379 Soln 437 Lecture 380 Problem 438 Lecture 381 Soln 438 Lecture 382 Problem 439 Lecture 383 Soln 439 Lecture 384 Problem 440 Lecture 385 Soln 440 Lecture 386 Problem 441 Lecture 387 Soln 441 Lecture 388 Problem 442 Lecture 389 Soln 442 Lecture 390 Problem 443 Lecture 391 Soln 443 Lecture 392 Problem 444 Lecture 393 Soln 444 Lecture 394 Problem 445 Lecture 395 Soln 445 Lecture 396 Problem 446 Lecture 397 Soln 446 Lecture 398 Problem 447 Lecture 399 Soln 447 Lecture 400 Problem 448 Lecture 401 Soln 448 Lecture 402 Problem 449 Lecture 403 Soln 449 Lecture 404 Problem 450 Lecture 405 Soln 450 Section 12: 451-470 Lecture 406 Problem 451 Lecture 407 Soln 451 Lecture 408 Problem 452 Lecture 409 Soln 452 Lecture 410 Problem 453 Lecture 411 Soln 453 Lecture 412 Problem 454 Lecture 413 Soln 454 Lecture 414 Problem 455 Lecture 415 Soln 455 Lecture 416 Problem 456 Lecture 417 Soln 456 Lecture 418 Problem 457 Lecture 419 Soln 457 Lecture 420 Problem 458 Lecture 421 Soln 458 Lecture 422 Problem 459 Lecture 423 Soln 459 Lecture 424 Problem 460 Lecture 425 Soln 460 Lecture 426 Problem 461 Lecture 427 Soln 461 Lecture 428 Problem 462 Lecture 429 Soln 462 Lecture 430 Problem 463 Lecture 431 Soln 463 Lecture 432 Problem 464 Lecture 433 Soln 464 Lecture 434 Problem 465 Lecture 435 Soln 465 Lecture 436 Problem 466 Lecture 437 Soln 466 Lecture 438 Problem 467 Lecture 439 Soln 467 Lecture 440 Problem 468 Lecture 441 Soln 468 Lecture 442 Problem 469 Lecture 443 Soln 469 Lecture 444 Problem 470 Lecture 445 Soln 470 Section 13: 471-490 Lecture 446 Problem 471 Lecture 447 Soln 471 Lecture 448 Problem 472 Lecture 449 Soln 472 Lecture 450 Problem 473 Lecture 451 Soln 473 Lecture 452 Problem 474 Lecture 453 Soln 474 Lecture 454 Problem 475 Lecture 455 Soln 475 Lecture 456 Problem 476 Lecture 457 Soln 476 Lecture 458 Problem 477 Lecture 459 Soln 477 Lecture 460 Problem 478 Lecture 461 Soln 478 Lecture 462 Problem 479 Lecture 463 Soln 479 Lecture 464 Problem 480 Lecture 465 Soln 480 Lecture 466 Problem 481 Lecture 467 Soln 481 Lecture 468 Problem 482 Lecture 469 Soln 482 Lecture 470 Problem 483 Lecture 471 Soln 483 Lecture 472 Problem 484 Lecture 473 Soln 484 Lecture 474 Problem 485 Lecture 475 Soln 485 Lecture 476 Problem 486 Lecture 477 Soln 486 Lecture 478 Problem 487 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