R for Data Analysis: Students and Professionals

سرفصل های دوره

Most Honest Crash Course to Become a Data Analyst using R by Solving Real-World Data Problems

1. Welcome to the Course

1. 0 1. Lecture Part A - Course Intro

2. 0 2. Lecture Part B - Overview of R and R Studio

3. 0 3. Lecture Part C - Install R and Launching R Studio

4. 0 4. Lecture Part D - Intro to R Packages and Installation

5.1 CoursePack R for Data Analysts.zip

5. DOWNLOAD COURSE PACK Datasets, Coding Exercises, Course Outline and Cheatsheet.html

6. 0 5. Demo Overview of Course Folder Structure

7. 0 6. Demo Part A - How to Download R and R Studio

8. 0 7. Demo Part B - How to Install R

9. 0 8. Demo Part C - How to Install R Studio

10. 0 9. Demo Part D - Navigate R and R Studio

2. Load and Write Data in R

1. What You Will Learn Module 1.html

2. 1 1. Lecture Part A - Summary of Data Objects and Structures

3. 1 2. Lecture Part B - Define Path and Load Data

4. 1 3. Lecture Part C - Write Data

5. 1 4. Welcome to Lab 1 Overview

6. 1 5. Problem 1 Install R Packages

7. 1 6. Problem 2 Define Folder Paths and Setup Directories

8. 1 7. Problem 3 Load Data into R Workspace

9. 1 8. Problem 4 Write Data into R Workspace

10. 1 9. Extra Problem Capture a Snapshot Date from Filenames

3. Data Types and Formatting

1. What You Will Learn Moudle 2.html

2. 2 1. Lecture Data Types and Data Type Conversion in R

3. 2 2. Lecture Check Column Names and Rename Columns

4. 2 3. Lecture Date Formatting - Year, Month, etc.

5. 2 4. Lecture Character Formatting - Add Leading Zeros

6. 2 5. Welcome to Lab 2 Overview

7. 2 6. Problem 1 Check Data Types

8. 2 7. Problem 2 Rename Columns

9. 2 8. Problem 3 Date Formatting

10. 2 9. Problem 4 Add Leading Zeros

4. Data Manipulation

1. What You Will Learn Module 3.html

2. 3 1. Lecture Clean Data (drop columns, remove duplicates)

3. 3 2. Lecture Clean Data (recode and replace values)

4. 3 3. Lecture Sort and Order Data

5. 3 4. Lecture Subset Data (Columns, List, Conditions)

6. 3 5. Welcome to Lab 3 Overview

7. 3 6. Problem 1 Cleaning Data

8. 3 7. Problem 2 Recode Data

9. 3 8. Problem 3 Replace Data

10. 3 9. Problem 4 Arrange Data

11. 3 10. Problem 5 Sort Data

12. 3 11. Problem 6 Subset Data

5. Join Data Operations

1. What You Will Learn Module 4.html

2. 4 1. Lecture What is Join and Types of Join

3. 4 2. Lecture Perform Joins with dplyr

4. 4 3. Lecture Perform Joins with sqldf

5. 4 4. Lecture Advanced Join Problem - Temporal

6. 4 5. Lecture Advanced Join Problem - Subquery with Max()

7. 4 7. Weclome to Lab 4 Overview

8. 4 8. Problem 1 Perform Joins with dplyr

9. 4 9. Problem 2 Perform Joins with sqldf

10. 4 10. Problem 3 Perform Joins on Multiple Tables

11. 4 11. Problem 4 Advanced Join Temporal

12. 4 12. Problem 5 Advanced Subquery Max()

13. 4 13. Extra Problem Identify Changes in Account Information

6. Data Aggregation and Feature Engineering

1. What You Will Learn Module 5.html

2. 5 1. Lecture Summarize Data (count(), sum(), etc.)

3. 5 2. Lecture Filtering and Slicing Data

4. 5 3. Lecture Convert a Summary Table Format

5. 5 4. Lecture Feature Engineering using mutate()

6. 5 5. Welcome to Lab 5 Overview

7. 5 6. Problem 1 Summarize Data with dplyr using summarize()

8. 5 7. Problem 2 Filter Data with dplyr

9. 5 8. Problem 2 Slice Data with dplyr

10. 5 9. Problem 3 Sort Data with dplyr

11. 5 10. Problem 4 Convert a Summary Table Format

12. 5 11. Problem 5 Feature Engineering

7. Time Intelligence

1. What You Will Learn Module 6.html

2. 6 1. Lecture Calculate Time Features using Date Manipulation

3. 6 2. Lecture Calculate Event Sequence Analysis

4. 6 3. Lecture Calculate Number of Business Days

5. 6 4. Lecture Calculate KPIs with Different Frequencies

6. 6 5. Welcome to Lab 6 Overview

7. 6 6. Problem 1 Date Manipulation - Time Dimension

8. 6 7. Problem 1 Date Manipulation - Durations

9. 6 8. Problem 2 Calculate Event Sequence Analysis

10. 6 9. Problem 3 Calculate Business Days using bizdays Package

11. 6 10. Problem 4 Calculate a Measure at Daily Snapshot

12. 6 11. Extra Problem Calculate a Measure at Monthly Snapshot

8. Data Visualization with ggplot2

1. What You Will Learn Module 7.html

2. 7 1. Lecture Intro to Exploratory Data Analysis

3. 7 2. Lecture Uni-Variate Bar Chart

4. 7 3. Lecture Uni-Variate Pie Chart

5. 7 4. Lecture Uni-Variate Line Chart

6. 7 5. Lecture Uni-Variate Histogram

7. 7 6. Lecture Uni-Variate Density

8. 7 7. Lecture Bi-Variate Box Plot

9. 7 8. Lecture Bi-Variate Scatter Plot

10. 7 9. Lecture Bi-Variate Correlation Matrix

11. 7 10. Lecture Bi-Variate Cross Tabulation

12. 7 11. Welcome to Lab 7 Overview

13. 7 12. Problem 1 Uni-Variate Categorical Bar Chart

14. 7 13. Problem 1 Uni-Variate Categorical Pie Chart

15. 7 14. Problem 2 Uni-Variate Numerical Line Chart

16. 7 15. Problem 2 Uni-Variate Numerical Histogram and Density Plot

17. 7 16. Problem 3 Bi-Variate Categorical Charts Cross Tab

18. 7 17. Problem 4 Bi-Variate Numerical Charts Box Plot

19. 7 18. Problem 4 Bi-Variate Numerical Charts Scatter Plot

20. 7 19. Problem 4 Bi-Variate Numerical Charts Correlation Matrix

9. Capstone Projects

1. Capstone Project 1 - OpenAirBnB

2. 8 1. Capstone Project 1 - Solution Part A

3. 8 2. Capstone Project 1 - Solution Part B

4. 8 3. Capstone Project 1 - Solution Part C

5. 8 4. Capstone Project 1 - Solution Part D

6. Capstone Project 2 - Hotel Bookings (Extra).html

7. 8 5. Capstone Project 2 - Solution (Text Reminder).html

10. CONGRATULATIONS!!!

1. Course Wrap Up.html

2. Congratulations!!!

3. Bonus Lecture.html

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تولید کننده: Udemy