R Programming For Data Science- Practise 250 Exercises-Part2

[mitsumi]

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.

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rapidgator.net:

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ddownload.com:

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