Skocz do zawartości

Aktywacja nowych użytkowników
Zakazane produkcje

  • advertisement_alt
  • advertisement_alt
  • advertisement_alt
Courses2024

R Programming for Data Science- Practise 250 Exercises-Part1

Rekomendowane odpowiedzi

e6bc03150561a38ad9089b6fd17fde72.jpeg
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

Ukryta Zawartość

    Treść widoczna tylko dla użytkowników forum DarkSiders. Zaloguj się lub załóż darmowe konto na forum aby uzyskać dostęp bez limitów.




Ukryta Zawartość

    Treść widoczna tylko dla użytkowników forum DarkSiders. Zaloguj się lub załóż darmowe konto na forum aby uzyskać dostęp bez limitów.

No Password - Links are Interchangeable

Udostępnij tę odpowiedź


Odnośnik do odpowiedzi
Udostępnij na innych stronach

Dołącz do dyskusji

Możesz dodać zawartość już teraz a zarejestrować się później. Jeśli posiadasz już konto, zaloguj się aby dodać zawartość za jego pomocą.

Gość
Dodaj odpowiedź do tematu...

×   Wklejono zawartość z formatowaniem.   Usuń formatowanie

  Dozwolonych jest tylko 75 emoji.

×   Odnośnik został automatycznie osadzony.   Przywróć wyświetlanie jako odnośnik

×   Przywrócono poprzednią zawartość.   Wyczyść edytor

×   Nie możesz bezpośrednio wkleić grafiki. Dodaj lub załącz grafiki z adresu URL.

    • 1 Posts
    • 15 Views
    • 1 Posts
    • 19 Views
    • 1 Posts
    • 11 Views
    • 1 Posts
    • 13 Views
    • 1 Posts
    • 9 Views

×
×
  • Dodaj nową pozycję...

Powiadomienie o plikach cookie

Korzystając z tej witryny, wyrażasz zgodę na nasze Warunki użytkowania.