R for Data Analysis: Students and Professionals

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Taesun Yoo

11:12:01

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1. Welcome to the Course

1. 0 1. Lecture Part A - Course Intro.mp4

06:56

2. 0 2. Lecture Part B - Overview of R and R Studio.mp4

03:24

3. 0 3. Lecture Part C - Install R and Launching R Studio.mp4

01:58

4. 0 4. Lecture Part D - Intro to R Packages and Installation.mp4

04:51

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.mp4

03:37

7. 0 6. Demo Part A - How to Download R and R Studio.mp4

03:02

8. 0 7. Demo Part B - How to Install R.mp4

02:10

9. 0 8. Demo Part C - How to Install R Studio.mp4

01:50

10. 0 9. Demo Part D - Navigate R and R Studio.mp4

04:08

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.mp4

05:33

3. 1 2. Lecture Part B - Define Path and Load Data.mp4

07:54

4. 1 3. Lecture Part C - Write Data.mp4

04:03

5. 1 4. Welcome to Lab 1 Overview.mp4

01:57

6. 1 5. Problem 1 Install R Packages.mp4

05:24

7. 1 6. Problem 2 Define Folder Paths and Setup Directories.mp4

02:39

8. 1 7. Problem 3 Load Data into R Workspace.mp4

12:08

9. 1 8. Problem 4 Write Data into R Workspace.mp4

08:51

10. 1 9. Extra Problem Capture a Snapshot Date from Filenames.mp4

11:22

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.mp4

05:09

3. 2 2. Lecture Check Column Names and Rename Columns.mp4

02:50

4. 2 3. Lecture Date Formatting - Year, Month, etc..mp4

04:37

5. 2 4. Lecture Character Formatting - Add Leading Zeros.mp4

03:22

6. 2 5. Welcome to Lab 2 Overview.mp4

02:14

7. 2 6. Problem 1 Check Data Types.mp4

07:23

8. 2 7. Problem 2 Rename Columns.mp4

02:11

9. 2 8. Problem 3 Date Formatting.mp4

05:38

10. 2 9. Problem 4 Add Leading Zeros.mp4

06:31

4. Data Manipulation

1. What You Will Learn Module 3.html

2. 3 1. Lecture Clean Data (drop columns, remove duplicates).mp4

04:18

3. 3 2. Lecture Clean Data (recode and replace values).mp4

03:03

4. 3 3. Lecture Sort and Order Data.mp4

04:49

5. 3 4. Lecture Subset Data (Columns, List, Conditions).mp4

04:29

6. 3 5. Welcome to Lab 3 Overview.mp4

01:22

7. 3 6. Problem 1 Cleaning Data.mp4

05:34

8. 3 7. Problem 2 Recode Data.mp4

02:56

9. 3 8. Problem 3 Replace Data.mp4

05:10

10. 3 9. Problem 4 Arrange Data.mp4

05:44

11. 3 10. Problem 5 Sort Data.mp4

04:55

12. 3 11. Problem 6 Subset Data.mp4

08:26

5. Join Data Operations

1. What You Will Learn Module 4.html

2. 4 1. Lecture What is Join and Types of Join.mp4

05:21

3. 4 2. Lecture Perform Joins with dplyr.mp4

05:02

4. 4 3. Lecture Perform Joins with sqldf.mp4

06:38

5. 4 4. Lecture Advanced Join Problem - Temporal.mp4

04:56

6. 4 5. Lecture Advanced Join Problem - Subquery with Max().mp4

07:22

7. 4 7. Weclome to Lab 4 Overview.mp4

03:02

8. 4 8. Problem 1 Perform Joins with dplyr.mp4

15:15

9. 4 9. Problem 2 Perform Joins with sqldf.mp4

08:12

10. 4 10. Problem 3 Perform Joins on Multiple Tables.mp4

08:35

11. 4 11. Problem 4 Advanced Join Temporal.mp4

15:31

12. 4 12. Problem 5 Advanced Subquery Max().mp4

12:42

13. 4 13. Extra Problem Identify Changes in Account Information.mp4

15:41

6. Data Aggregation and Feature Engineering

1. What You Will Learn Module 5.html

2. 5 1. Lecture Summarize Data (count(), sum(), etc.).mp4

06:55

3. 5 2. Lecture Filtering and Slicing Data.mp4

06:22

4. 5 3. Lecture Convert a Summary Table Format.mp4

04:14

5. 5 4. Lecture Feature Engineering using mutate().mp4

06:47

6. 5 5. Welcome to Lab 5 Overview.mp4

02:36

7. 5 6. Problem 1 Summarize Data with dplyr using summarize().mp4

11:00

8. 5 7. Problem 2 Filter Data with dplyr.mp4

05:17

9. 5 8. Problem 2 Slice Data with dplyr.mp4

03:39

10. 5 9. Problem 3 Sort Data with dplyr.mp4

01:50

11. 5 10. Problem 4 Convert a Summary Table Format.mp4

07:18

12. 5 11. Problem 5 Feature Engineering.mp4

09:52

7. Time Intelligence

1. What You Will Learn Module 6.html

2. 6 1. Lecture Calculate Time Features using Date Manipulation.mp4

04:47

3. 6 2. Lecture Calculate Event Sequence Analysis.mp4

03:22

4. 6 3. Lecture Calculate Number of Business Days.mp4

09:25

5. 6 4. Lecture Calculate KPIs with Different Frequencies.mp4

06:58

6. 6 5. Welcome to Lab 6 Overview.mp4

01:53

7. 6 6. Problem 1 Date Manipulation - Time Dimension.mp4

10:56

8. 6 7. Problem 1 Date Manipulation - Durations.mp4

04:01

9. 6 8. Problem 2 Calculate Event Sequence Analysis.mp4

20:13

10. 6 9. Problem 3 Calculate Business Days using bizdays Package.mp4

09:20

11. 6 10. Problem 4 Calculate a Measure at Daily Snapshot.mp4

08:14

12. 6 11. Extra Problem Calculate a Measure at Monthly Snapshot.mp4

06:27

8. Data Visualization with ggplot2

1. What You Will Learn Module 7.html

2. 7 1. Lecture Intro to Exploratory Data Analysis.mp4

05:00

3. 7 2. Lecture Uni-Variate Bar Chart.mp4

03:32

4. 7 3. Lecture Uni-Variate Pie Chart.mp4

03:49

5. 7 4. Lecture Uni-Variate Line Chart.mp4

02:46

6. 7 5. Lecture Uni-Variate Histogram.mp4

01:06

7. 7 6. Lecture Uni-Variate Density.mp4

01:14

8. 7 7. Lecture Bi-Variate Box Plot.mp4

02:30

9. 7 8. Lecture Bi-Variate Scatter Plot.mp4

03:33

10. 7 9. Lecture Bi-Variate Correlation Matrix.mp4

02:55

11. 7 10. Lecture Bi-Variate Cross Tabulation.mp4

03:16

12. 7 11. Welcome to Lab 7 Overview.mp4

02:26

13. 7 12. Problem 1 Uni-Variate Categorical Bar Chart.mp4

15:36

14. 7 13. Problem 1 Uni-Variate Categorical Pie Chart.mp4

07:06

15. 7 14. Problem 2 Uni-Variate Numerical Line Chart.mp4

06:58

16. 7 15. Problem 2 Uni-Variate Numerical Histogram and Density Plot.mp4

03:36

17. 7 16. Problem 3 Bi-Variate Categorical Charts Cross Tab.mp4

07:33

18. 7 17. Problem 4 Bi-Variate Numerical Charts Box Plot.mp4

03:59

19. 7 18. Problem 4 Bi-Variate Numerical Charts Scatter Plot.mp4

07:19

20. 7 19. Problem 4 Bi-Variate Numerical Charts Correlation Matrix.mp4

08:43

9. Capstone Projects

1. Capstone Project 1 - OpenAirBnB.mp4

05:50

2. 8 1. Capstone Project 1 - Solution Part A.mp4

29:36

3. 8 2. Capstone Project 1 - Solution Part B.mp4

35:03

4. 8 3. Capstone Project 1 - Solution Part C.mp4

40:02

5. 8 4. Capstone Project 1 - Solution Part D.mp4

23:23

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!!!.mp4

02:59

3. Bonus Lecture.html

Description

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

What You'll Learn?

Installing R and R Studio for seamless coding environment setup.
Mastering data type conversion and formatting techniques for consistent data representation.
Utilizing dplyr functions for efficient data manipulation tasks.
Implementing various types of join operations to merge datasets effectively.
Aggregating data and engineering new features for insightful analysis.
Handling date and time data effectively using lubridate package.
Creating customizable visualizations with ggplot2 for effective data communication
Complete a capstone project: OpenAirBnB data using concepts and skills learned from this course to create effective visualizations and communicate your findings

Who is this for?

This course is designed for individuals with no prior experience in tools (e.g., R or Python).

For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself

For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets.

What You Need to Know?

Operating Systems: 64-bit versions of Microsoft Windows 7, 8.1 and 10 or Mac

Installation of R and R Studio

No prior experience in R but highly desirable to know some basic analytics with Excel

More details

Description
Interested in becoming a Data Analyst? Want to gain practical skills and solve real-world business problems? Then this is the perfect course for you! This course is created by a Senior Data Analyst who has 10 years of experience in the Insurance and Health Care sectors. This course will equip you with foundational knowledge and help you learn key concepts of loading data, data manipulation, data aggregation, and how to use libraries/packages in a simple method.
I will guide you step-by-step into the World of Data Analysis. With every lecture and lab exercise, you will gain and develop understandings of these concepts to tackle real data problems! This course is mainly designed using R to solve the labs and capstone projects.
This course will be super useful and exciting. I tried my best to design the course curriculum in the most natural logical flow:
Â· Module 0 - Intro to R: set up R environment and understand the basics of R packages/libraries
Â· Module 1 - Load and Write Data: learn how to load and write data from flat files (i.e., .csv or Excel format)
Â· Module 2 - Data Types and Formatting: master the data types and learn how to convert data types for right operations
Â· Module 3 - Data Manipulation: clean and preprocess data, perform sorting, ordering, and subsetting records
Â· Module 4 - Join Operations: learn how to perform joins using R packages (i.e., dplyr and sqldf)
Â· Module 5 - Data Aggregation: learn how to aggregate data using summary statistics and perform feature engineering
Â· Module 6 - Time Intelligence: learn how to calculate business days and time dimension analysis
Â· Module 7 - Data Visualization: learn the basics of exploratory data analysis (EDA) and uni-variate/bi-variate visualizations
Each module is independent content. Technically speaking, you can take the course from start to end or jump into any specific topics of your interest. However, I highly recommend students to take the course from Module 1 to 7 in order to complete the capstone project challenge!
This course is packed with real-world data/business problems that I solved during my career as a senior data analyst. You will learn not just concepts but also a lot of practical and hands-on experience from the course. Enroll today and take the first step towards mastering the art of data analysis using R.
Who this course is for:
This course is designed for individuals with no prior experience in tools (e.g., R or Python).
For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself
For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets.

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Taesun is a seasoned data analytics specialist with a wealth of experience in managing large-scale projects. His expertise spans from overseeing data quality and integration testing to crafting informed ad-hoc reports and developing insightful analytics products. Additionally, he serves as a freelance mentor and is the proud owner of the YouTube Channel 'DataScienceOne,' dedicated to showcasing data science projects for aspiring enthusiasts and career-switchers. Taesun's vision is to influence and educate as many individuals as possible, guiding them to launch their first data analytics career.

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