Data Analytics Mastery with R and SQL

Master practical data analytics skills using R, SQL, Git, GitHub, and GitHub Actions. Learn data manipulation, visualization, automation, and reproducibility with hands-on projects and real-world applications.

Interested in this course? Email us at [email protected]

Course curriculum

1. How to use this course
2. Before we begin...
3. Course Overview and Structure
1. Lesson 1: Introduction to Data Analytics
2. Lesson 2: Data Analytics Tools
3. Lesson 3: Installation and Configuration of Data Analytics Tools
4. Lesson 4: RStudio IDE
5. Lesson 5: Basics of R - Data and Information
6. Lesson 6: Basics of R - Executable Codes and Comments
7. Lesson 7: Basics of R - Variables and Arithmetic Operators
8. Lesson 8: Basics of R - Logical Operators
9. Lesson 9: Basics of R - Vectors
10. Lesson 10: Data Types in R - Lists
11. Lesson 11: Data Types in R - Matrix
12. Lesson 12: Data Types in R - Data frames
13. Lesson 13: Subsetting Objects in R -Vectors
14. Lesson 14: Subsetting Objects in R - List
15. Lesson 15: Subsetting Objects in R - Matrix
16. Lesson 16: Subsetting Objects in R - Data frame
17. Lesson 17: Data Types - Factors
18. Lesson 18: Data types -Dates
19. Lesson 19: Functions in R
20. Week 1 Assignment
21. Weekly Feedback - 1
22. Week 1 Saturday LIVE Session
23. Week 1 Sunday LIVE Session
1. Curriculum Overview
2. Lesson 1: Importing data from tsv using base R function, read.table()
3. Lesson 2: Import CSV data using base R function, read.csv()
4. Lesson 3: Installing Packages in R
5. Lesson 4: Importing data from csv using readr package function, read_csv()
6. Lesson 5: Importing data from MS Excel Using readxl package function, read_excel()
7. Lesson 6: Importing data from MS Excel Worksheet Using readxl package function, read_xls()
8. Lesson 7: Importing data from MS Excel Workbook Using readxl package function, read_xlsx()
9. Lesson 8: Importing data from MS Excel Workbook Using readxl package function, read_xlsx() Extended
10. Weekly Feedback - 2
11. Data files
1. Lesson 1: Saving Data into R Data Format: RDS and RDATA
2. Lesson 2: Exporting data to different formats - CSV
3. Lesson 3: Exporting data to different formats - MS Excel
4. Weekly Feedback - 4
1. Lesson 1: Review of Subsetting in R using $, []
2. Lesson 2: Review of Subsetting in R using $, [] using Logical statement
3. Lesson 3: Introduction to dplyr, tidyr, and tidyverse
4. Lesson 4: Introduction to tibbles and why they are useful - Part 1
5. Lesson 5: Introduction to tibbles and why they are useful - Part 2
6. Lesson 6: Introduction to tibbles and the Pipe operators
7. Lesson 7: Data manipulation with dplyr package function, select() - Part 1
8. Lesson 8: Data manipulation with dplyr package function, select() - Part 2
9. Lesson 9: Data manipulation with dplyr package function, select() - Part 3
10. Lesson 10: Data manipulation with dplyr packagefunction, select(), rename()
11. Lesson 11: Data manipulation with dplyr package function, filter() Part 1
12. Lesson 12: Data manipulation with dplyr - filter() Part 2
13. Lesson 13: Data manipulation with dplyr package function, arrange()
14. Lesson 14: Data manipulation with dplyr package function, mutate()
15. Lesson 15: Data manipulation with dplyr package function, mutate(), if_else()
16. Lesson 16: Data manipulation with dplyr package function, mutate(), case_when()
17. Lesson 17: Joining data frames - Intro, bind_rows()
18. Lesson 18: Joining data frames - Adding rows with non-identical sets of columns
19. Lesson 19: Joining data frames - Adding rows with columns in a different order
20. Lesson 20: Joining data frames - Adding rows with different column names
21. Lesson 21: Joining data frames - Combining Data Frames Horizontally by Columns
22. Lesson 22: Joining data frames - Combining Data Frames Horizontally in R, merge(), left join
23. Lesson 23: Joining data frames - Combining Data Frames Horizontally in R, merge(), Right join
24. Lesson 24: Joining data frames - Combining Data Frames Horizontally in R, merge(), Full join, Inner join, and diff col names
25. Lesson 25: Joining data frames - Combining Data Frames Horizontally in R, using dplyr functions 1
26. Lesson 26: Joining data frames - Combining Data Frames Horizontally in R, using dplyr functions 2
27. Download files
28. Weekly Feedback - 4
29. Week 4 Weekend LIVE Class (Saturday, 03-08-2024)
30. Week 4 Weekend LIVE Class 1 (Sunday, 04-08-2024)
31. Week 4 Weekend LIVE Class 2 (Sunday 04-08-2024)
1. Lesson 1: Principles of Data Visualization - Importance of data visualization
2. Lesson 2: Principles of Data Visualization - Types of charts and when to use them
3. Lesson 3: Creating visualizations using ggplot2 - Aesthetic mappings
4. Lesson 4: Creating visualizations using ggplot2 - Geometric objects (Points)
5. Lesson 5: Creating visualizations using ggplot2 - Color, size, shape and other aesthetic attributes
6. Lesson 6: Creating visualizations using ggplot2 - Geometry objects (Histogram)
7. Lesson 7: Creating visualizations using ggplot2 - Geometry objects (Line)
8. Lesson 8: Creating visualizations using ggplot2 - Geometry objects (Area)
9. Lesson 9: Creating visualizations using ggplot2 - Geometry objects (Bar)
10. Lesson 10: Creating visualizations using ggplot2 - Saving Plot Output as Variable
11. Lesson 11: Creating visualizations using ggplot2 - Saving Plot Output as File
12. Lesson 12: Creating visualizations using ggplot2 - Plot Labels
13. Lesson 13: Creating visualizations using ggplot2 - Themes
14. Lesson 14: Creating visualizations using ggplot2 - Legends
15. Lesson 15: Creating visualizations using ggplot2 - Facets (splits a plot into subplots)
16. Weekly Feedback - 5
17. Week 5 Downloads
18. Pre-week LIVE Recording (08-11-2024)
19. Weekend (Saturday) LIVE Hands-on Walkthrough on Data Manipulation and Visualization
20. Weekend (Sunday) LIVE Class - Project 1 Walkthrough on Data Manipulation and Visualization
1. Week 6 - Project 1 Data Manipulation and Visualization
2. Weekly Feedback - 6
1. Lesson 1: Introduction to Databases and SQL
2. Lesson 2: Install PostgreSQL
3. Lesson 3: Create a Database
4. Lesson 4: Create Database Entities (Tables)
5. Lesson 5: Insert data into Database Tables
6. Lesson 6: SQL Basics
7. Lesson 7: SELECT query with the WHERE clause
8. Lesson 8: SELECT query with the DISTINCT keyword
9. Lesson 9: SELECT query with DISTINCT and COUNT function
10. Lesson 10: SELECT query with the ORDER BY clause
11. Lesson 11: SELECT query using the LIMIT and OFFSET clauses
12. Lesson 12: SQL Alias
13. Lesson 13: SQL JOIN clause
14. Lesson 14: SELECT Query with GROUP BY and HAVING Clauses
15. Lesson 15: Connect to Database from R
16. Lesson 16: Working with Database in R
17. Weekly Feedback - 7
18. Week 7 - All File Downloads
19. Week 7 LIVE Class 1 (Sunday 25-08-2024)
20. Week 7 Summary LIVE Class (Saturday 30-08-2024)
1. Lesson 1: More functions from the dplyr package - arrange()
2. Lesson 2: More functions from the dplyr package - distinct()
3. Lesson 3: More functions from the dplyr package - count()
4. Lesson 4: More functions from the dplyr package - relocate()
5. Lesson 5: Grouping and Summarizing Data with the dplyr package
6. Lesson 6: Column wise operation with dplyr
7. Lesson 7: Tidying Data - pivot_longer(), pivot_wider()
8. Lesson 8: Writing Custom functions in R - Why and When
9. Lesson 9: Writing Custom functions in R
10. Lesson 10: Writing Custom functions in R - Real-life Scenario
11. Weekly Feedback - 8
12. Week 8 LIVE Class 1 (Sunday 01-09-2014)
13. Week 8 Downloads
1. Lesson 1: Manipulation Strings 1
2. Lesson 2: Manipulation Strings 2
3. Lesson 3: Manipulation Strings 3
4. Lesson 4: Handling Categorical Data in R 1
5. Lesson 5: Handling Categorical Data in R 2
6. Lesson 6: Handling Categorical Data in R 3
7. Lesson 7: Working with Dates 01
8. Lesson 8: Working with Dates 02
9. Lesson 9: Working with Dates 03
10. Lesson 10: Visualizing timeseries and categorical data
11. Weekly Feedback - 13
12. Week 9 LIVE Class (Sunday 09/08/2024)
13. Week 9 Downloads
1. Lesson 1: Introduction - Definition, Importance of EDA
2. Lesson 2: EDA - Univariate and Bivariate Analysis
3. Lesson 3: EDA - Categorical Analysis, Missing Data, and Outlier detection
4. Lesson 4: EDA - Performing EDA Task
5. Lesson 5: Answering EDA Questions with Visuals
6. Lesson 6: Automating EDA Task
7. Lesson 7: Introduction to Reporting
8. Lesson 8: RMarkdown 1
9. Lesson 9: RMarkdown 2
10. Lesson 10: Quarto
11. Weekly Feedback - 10
12. Week 10: LIVE Class 1 (09/14/2024)
13. Week 10 LIVE Class 2 (09/15/2024)
14. Week 10 Downloads
1. Lesson 1: Control Structures and Iteration - Introduction
2. Lesson 2: Control Structure - if statement
3. Lesson 3: Control Structure - if else statement
4. Lesson 4: Control Structure - if-else, else-if, else statement
5. Lesson 5: Control Structure - Nested if else statement
6. Lesson 6: Control Structure - Practice Example
7. Lesson 7: if_else() and case_when() from the dplyr package
8. Lesson 8: Control Structure - For loop
9. Lesson 9: Control Structure - Nested For loop
10. Lesson 10: Control Structure - While loop
11. Lesson 11: Control Structure - Switch statement
12. Lesson 10: Iteration with purrr package - Introduction
13. Lesson 11: Functional Programming Review
14. Lesson 12: purrr package - mapping functions 1
15. Lesson 13: purrr package - mapping functions 2
16. Lesson 14: purrr package - Practical Examples
17. Week 11 Downloads
18. Weekly Feedback - 9
19. Week 11 LIVE Class 1 (Saturday 09/21/2024)
20. Week 11 LIVE Class 2 (Sunday 09/22/2024)
1. Weekly Feedback - 12 and 13
2. Project Description

About this course

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176 lessons
42.5 hours of video content

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Business Data Laboratory Courses

Data Analytics Mastery with R and SQL

Course curriculum

Welcome to the course!

Week 1: Introduction to Data Analysis and R

Week 2: Importing Data in R

Week 3: Exporting Data in R

Week 4: Data Manipulation in R

Week 5: Data Visualization in R

Week 6: Project 1

Week 7: SQL for Data Analysis

Weeks 8: Intermediate Data Manipulation

Week 9: Working with Numbers, Strings, Factors and Dates

Week 10: Exploratory Data Analysis and Reporting in R

Week 11: Control Structure and Iteration with purrr package

Week 12 and 13: Project 1

About this course

Discover your potential, starting today