R Programming Ninja Course 2024:Data Science with 5 Projects
R Programming Ninja Course 2024:Data Science with 5 Projects, available at $79.99, has an average rating of 4.5, with 50 lectures, based on 139 reviews, and has 1364 subscribers.
You will learn about R Programming R Datatypes R Data Structures Vectors, Matrices, Arrays, Lists Data analysis Data Visualization using GGPLOT2 Case Studies on Data analysis using R Projects on Data analysis using R Data Cleaning Data Transformations using tidyr, Dplyr String Manipulations using Stringr Handling Date and Time using Lubridate Projects on Data Visualization using R This course is ideal for individuals who are Beginner or Intermediate or Advanced It is particularly useful for Beginner or Intermediate or Advanced.
Enroll now: R Programming Ninja Course 2024:Data Science with 5 Projects
Summary
Title: R Programming Ninja Course 2024:Data Science with 5 Projects
Price: $79.99
Average Rating: 4.5
Number of Lectures: 50
Number of Published Lectures: 49
Number of Curriculum Items: 50
Number of Published Curriculum Objects: 49
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- R Programming
- R Datatypes
- R Data Structures
- Vectors, Matrices, Arrays, Lists
- Data analysis
- Data Visualization using GGPLOT2
- Case Studies on Data analysis using R
- Projects on Data analysis using R
- Data Cleaning
- Data Transformations using tidyr, Dplyr
- String Manipulations using Stringr
- Handling Date and Time using Lubridate
- Projects on Data Visualization using R
Who Should Attend
- Beginner
- Intermediate
- Advanced
Target Audiences
- Beginner
- Intermediate
- Advanced
Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems. The field of data science has exploded in the past two decades and shows no signs of stopping any time soon. Many big or small businesses and companies wish to make use of the insights gained through the big data.
Due to its open-source nature and its extreme versatility, R has become the primary tool for statistical analysis and data science. With the industry facing a shortage of data scientists all over the world, both novice and professional R programmers can enter. R community represents the cutting-edge in the field of data science.
This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.
This course providesFull-fledged knowledge of R, we cover it all.
Our exotic journey will include the concepts of:
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What’s and Why’s of R programming Language –Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.
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Core knowledge for DataTypes.
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String Manipulation and handling using Stringr Package
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Data Structures (Vectors, Matrices, Arrays, List)
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Loops and Conditionsand Functionsfor programming skills in R.
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Dataframesexplained in detail and perspective for Data Analysis Process and Concepts.
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Most importantly Data Transformationshave been covered to make you comfortable with how data should be handled and transformed for analysis.
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Date Time Modulehelps to understand and handle date and time in R.
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Descriptive Statisticsallows to explore the data summaries for statistics.
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Data Visualization using GGPLOT2used for simple and complex visual analysis.
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All the modules include practice questionsand case studiesto give you idea on the real world problems and enhancing problem solving skills.
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5 Projects allow you to perform analysis on datasets with scope for further exploring and enhancing skills while building confidence.
Course Curriculum
Chapter 1: Introduction To Course
Lecture 1: Course Introduction
Chapter 2: Rstudio – Getting Started
Lecture 1: Introduction
Lecture 2: Rstudio Introduction and Loading Packages
Chapter 3: R Datatypes
Lecture 1: R Datatypes
Chapter 4: R Datatypes Practice
Lecture 1: R Datatypes Practice
Chapter 5: R Class object Data types and Logical operators
Lecture 1: R Class object Data types and Logical operators
Chapter 6: Characters
Lecture 1: characters
Chapter 7: Practice : Numbers and Strings
Lecture 1: PQ: Numbers and Strings
Chapter 8: Vectors vs List vs Matrix vs Array vs DataFrames
Lecture 1: Vectors vs list vs matrix vs array vs DF
Chapter 9: Vectors
Lecture 1: Vectors
Lecture 2: Vector Indexing
Chapter 10: Matrices
Lecture 1: Matrices
Chapter 11: Lists
Lecture 1: Lists
Chapter 12: Practice Questions : Vectors
Lecture 1: PQ3: Vectors
Lecture 2: PQ4: Vectors
Chapter 13: Loops and Conditions
Lecture 1: Loops And Conditions
Chapter 14: Functions
Lecture 1: Functions
Lecture 2: Functions and Scope of Variables
Chapter 15: Practice Questions
Lecture 1: PQ5: Functions
Chapter 16: Factor Variable Trap (FVT)
Lecture 1: Factor Variable Trap
Chapter 17: Stringr Package
Lecture 1: String Functions using StringR
Chapter 18: Dataframes
Lecture 1: Dataframes
Lecture 2: Variable Types
Lecture 3: Data Preparation Guidelines
Chapter 19: Practice Questions
Lecture 1: PQ6: Dataframes
Chapter 20: Data Transformations (Apply Family)
Lecture 1: Data Transformations
Chapter 21: Data Transformation Using Dplyr & Tidyr
Lecture 1: Data Transformations using Dplyr and tidyr
Chapter 22: Practice Questions
Lecture 1: PQ7: Data Preparation
Lecture 2: PQ 8: Data transformation
Chapter 23: Date Time in R using Lubridate
Lecture 1: lubridate
Chapter 24: Descriptive Statistics using R
Lecture 1: Descriptive Statistics
Lecture 2: Descriptive Statistics Using R
Chapter 25: Data Visualization Using GGPLOT2
Lecture 1: GGPLOT INTRO
Lecture 2: GGPLOT2 plotting
Lecture 3: QPlot function
Chapter 26: GGPLOT2 Practice questions
Lecture 1: ggplot2 practice questions
Chapter 27: Case Studies
Lecture 1: Case Study 1 :Titanic Dataset Complete Visualization
Lecture 2: Case Study 2 : HR Analytics
Chapter 28: Projects And Solutions
Lecture 1: Project 1: Seattle Data Transformation Problem
Lecture 2: Project 1: Seattle Collision Data Transformation Solution
Lecture 3: Project 2: Diamond data analysis Problem
Lecture 4: Project 2: Diamonds Dataset Analysis Solution
Lecture 5: Project 3: Facebook data analysis Problem
Lecture 6: Project 3: Facebook data Analysis Solution
Lecture 7: Project 4: GOT Data Analysis Problem
Lecture 8: Project 4: GOT Data Analysis Solution
Chapter 29: Major Project
Lecture 1: Project 5: NYC Taxi Data Analysis Problem
Lecture 2: Project 5: NYC Taxi Data Analysis Solution
Chapter 30: BONUS!!!
Lecture 1: BONUS !!!
Instructors
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MG Analytics
Data Scientist and Professional Trainer
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 2 votes
- 3 stars: 17 votes
- 4 stars: 41 votes
- 5 stars: 75 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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