R Programming For Data Science- Practise 250 Exercises-Part2

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

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Lecture 98 Problem 297

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Lecture 100 Problem 298

Lecture 101 Soln 298

Lecture 102 Problem 299

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Lecture 104 Problem 300

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Lecture 106 Problem 301

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Lecture 110 Problem 303

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Lecture 120 Problem 308

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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

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Lecture 134 Problem 315

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Lecture 136 Problem 316

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Lecture 138 Problem 317

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Lecture 140 Problem 318

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Lecture 142 Problem 319

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Lecture 144 Problem 320

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Lecture 146 Problem 321

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Lecture 148 Problem 322

Lecture 149 Soln 322

Lecture 150 Problem 323

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Lecture 152 Problem 324

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Lecture 156 Problem 326

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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

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Lecture 176 Problem 336

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Lecture 178 Problem 337

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Lecture 180 Problem 338

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Lecture 190 Problem 343

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Lecture 192 Problem 344

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Lecture 194 Problem 345

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Lecture 196 Problem 346

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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

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Lecture 214 Problem 355

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Lecture 216 Problem 356

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Lecture 220 Problem 358

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Lecture 226 Problem 361

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Lecture 228 Problem 362

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Lecture 230 Problem 363

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Lecture 232 Problem 364

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Lecture 234 Problem 365

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Lecture 236 Problem 366

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Lecture 240 Problem 368

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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

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Lecture 250 Problem 373

Lecture 251 Soln 373

Lecture 252 Problem 374

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Lecture 270 Problem 383

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Lecture 272 Problem 384

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Lecture 274 Problem 385

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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

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Lecture 374 Problem 435

Lecture 375 Soln 435

Lecture 376 Problem 436

Lecture 377 Soln 436

Lecture 378 Problem 437

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Lecture 380 Problem 438

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Lecture 382 Problem 439

Lecture 383 Soln 439

Lecture 384 Problem 440

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Lecture 386 Problem 441

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Lecture 388 Problem 442

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Lecture 390 Problem 443

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Lecture 392 Problem 444

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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

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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.