Data Analytics using R programming

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Vignesh Muthuvelan

13:56:09

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1. Introduction

1. Introduction.html

2. Prerequisites.html

2. Data Analytics

1. What is Data.mp4

02:50

2. Importance of Data.mp4

03:35

3. Type of Data - Categorical.mp4

03:46

4. Type of Data - Numerical.mp4

02:10

5. Analytics and Analysis.mp4

02:26

6. Data Analytics.mp4

05:39

7. Data Analysis.mp4

02:04

8. Classification of Data Analytics.mp4

09:57

9.1 1. Data Analytics Introduction.pptx

9. Process.mp4

03:12

3. Intro to R and R studio

1. Introduction to R.mp4

02:40

2. Benefits of R.mp4

02:58

4. R and R studio installation in Ubuntu

1. install R in Ubuntu GUI.mp4

04:57

2. install R in Ubuntu terminal.mp4

02:57

3. R studio GUI overview.mp4

04:35

4. How to create and run R file in GUI.mp4

03:03

5. How to save and run R file in Terminal.mp4

04:52

6. Rdata and Rhistory.mp4

03:42

5. R programming Basics

1. Variable in R.mp4

04:17

2. DataTypes in R.mp4

05:06

3. Print vs Cat function in R.mp4

03:24

4. ls,rm function in R.mp4

00:54

5. Rules to create variable in R.mp4

04:43

6. Special keywords in R.mp4

01:17

7. Different datatypes in R.mp4

02:17

8. Vectorization in R.mp4

05:44

9. Implicit Cohesion.mp4

10:55

10.1 first pro.zip

10. ls function in detail.mp4

10:55

6. Operators in R

1. Operators in R.mp4

01:15

2. Arithmetic Operators.mp4

07:02

3. Relational Operators.mp4

03:06

4. Logical Operators.mp4

04:17

5. Miscellaneous Operators.mp4

07:38

6.1 opr in r.zip

6. R basics summary.mp4

07:51

7. Control structures in R

1. Conditional statement - if, else, else if.mp4

06:17

2.1 conditional statement.pptx

2. Conditional statement - switch.mp4

03:16

3. Lab exercise.html

8. Looping Statement in R

1. For.mp4

03:23

2. While.mp4

04:25

3.1 r control structures.zip

3. Repeat.mp4

02:56

9. String Handling in R

1.1 Missing Value, Vector in R.pptx

1. getting user input and explicit cohersion.mp4

11:31

2. getting user input part 2.mp4

03:45

3. logical check for string - grepl and grep.mp4

03:56

4. print vs cat vs paste method.mp4

04:01

5.1 string handling.zip

5. String methods - toupper, tolower, substr, format.mp4

07:31

10. Vector operation in R

1. Indexing in vector.mp4

05:20

2. Indexing in vector - part 2.mp4

03:16

3. Built-in operation in R.mp4

03:04

4. Repeat operation in R.mp4

03:44

5. Lab exercise.html

6. Lab solution - part 1.mp4

12:54

7. Lab solution - part 2.mp4

13:52

11. Functions in R

1. Intro to Function in R.mp4

04:19

2. Built-in function - seq, seq along.mp4

06:27

3. Built-in function - seq len.mp4

08:32

4. Built-in function rnorm.mp4

05:03

5. law of large number.mp4

03:10

6. Built-in function rnorm - part 2.mp4

04:08

7. Built-in function - runif.mp4

01:46

8.1 functions.zip

8. Built-in function - sample.mp4

04:56

9. Lab exercise.html

10. Lab solution - part 1.mp4

09:08

11. Lab solution - part 2.mp4

09:15

12. Lab solution - part 3.mp4

10:42

12. User defined function in R

1. User defined function - part 1.mp4

09:25

2. User defined function - part 2.mp4

07:30

3. User defined function - part 3.mp4

03:42

4. User defined function - part 4.mp4

06:28

5. User defined function - part 5.mp4

04:14

6. User defined function - part 6.mp4

03:56

7. User defined function - part 7.mp4

11:43

8.1 functions cont.zip

8. User defined function - part 8.mp4

10:49

9. Lab exercise.html

13. Vectorization in R

1. Vectorized Approach.mp4

12:35

2.1 Vector.pptx

2.2 vectorized fun.zip

2. Vectorized Function.mp4

06:10

14. Data Structure in R

1. Introduction to Data Structure.mp4

04:41

2. List - Part 1.mp4

12:33

3. List - Part 2.mp4

10:10

4. List summary.mp4

07:58

5.1 list.pptx

5.2 list.zip

5. Manipulating List.mp4

08:26

6.1 List.pptx

6.2 list.zip

6. Converting List to Vector.mp4

04:53

7.1 matrix.pptx

7. Matrix - Part 1.mp4

08:03

8. Matrix - Part 2.mp4

05:29

9. Matrix - Part 3.mp4

05:09

10. Matrix - Part 4.mp4

05:14

11.1 matrix.zip

11. Matrix - Part 5.mp4

10:38

12. Lab exercise.html

13. Date - Part 1.mp4

15:08

14.1 date.zip

14. Date - Part 2.mp4

04:28

15. Factor - Part 1.mp4

08:30

16. Factor - Part 2.mp4

07:25

17.1 factor.zip

17. Factor - Part 3.mp4

03:05

18. Array - Part 1.mp4

04:48

19. Array - Part 2.mp4

09:05

20. Array - Part 3.mp4

08:46

21.1 array.zip

21. Array - Part 4.mp4

03:20

22. Lab Exercise.html

23. DataFrame - Part 1.mp4

04:52

24. DataFrame - Part 2.mp4

07:09

25. DataFrame - Part 3.mp4

05:07

26. DataFrame - Part 4.mp4

06:21

27. DataFrame - Part 5.mp4

09:39

28. DataFrame - Part 6.mp4

03:37

29.1 DataFrame.pdf

29.2 dataframe.zip

29. DataFrame - Summary.mp4

02:18

30. Lab exercise.html

15. Data Manipulation

1. Data Manipulation - Part 1.mp4

07:00

2. Data Manipulation - Part 2.mp4

08:10

3. Data Manipulation - Part 3.mp4

07:27

4.1 data manipulation.zip

4. Data Manipulation - Part 4.mp4

05:44

16. R Package

1. R Package - Part 1.mp4

10:46

2.1 packages.zip

2. R Package - Part 2.mp4

13:37

17. apply functions in R

1. apply function - part 1.mp4

06:21

2. apply function - part 2.mp4

07:50

3. apply function - part 3.mp4

01:55

4. lapply function - part 1.mp4

00:59

5. lapply function - part 2.mp4

05:50

6. sapply function - part 1.mp4

03:20

7. sapply function - part 2.mp4

01:48

8. tapply function.mp4

04:33

9.1 apply function.zip

9. summary.mp4

04:55

18. Data Reshaping

1.1 data reshaping.zip

1.2 NYC temperature data.csv

1. Data Reshaping introduction.mp4

13:41

2. Aggregating - Part 1.mp4

07:25

3. Aggregating - Part 2.mp4

04:21

4. sorting.mp4

03:09

5. mergining - inner join.mp4

05:33

6. types of joins.mp4

02:04

7. left, right and full join.mp4

07:54

8. Lab exercise.html

19. Data visualization

1. Data visualization - part 1.mp4

08:39

2. Data visualization - part 2.mp4

08:22

3. scatter plot using base R.mp4

02:29

4. scatter plot using ggplot - part 1.mp4

06:00

5. scatter plot using ggplot - part 2.mp4

08:34

6. Summary.mp4

04:02

7. Line plot using base R.mp4

05:35

8. Line plot using ggplot - part 1.mp4

08:39

9.1 line plot.zip

9. Line plot using ggplot - part2.mp4

06:19

10. Histogram using base R.mp4

06:25

11.1 histogram plot.zip

11. Histogram uisng ggplot.mp4

04:47

12. Bar plot using base R - part 1.mp4

08:25

13. Bar plot using base R - part 2.mp4

04:26

14.1 bar plot.zip

14. Bar plot using ggplot.mp4

05:26

15. Box plot using Base R.mp4

17:07

16.1 box plot.zip

16. Box plot using ggplot.mp4

03:18

20. Working with Excel file

1. Introduction to working with excel file.mp4

11:44

2. Data cleaning - part 1.mp4

11:25

Description

Data analytics, R programming

What You'll Learn?

What is data and its types
Overview of the R programming language.
Installation of R and Rstudio in Ubuntu environment
Basic syntax and data structures
Operators, control and looping statement in R
String handling, vector operator in R
Built-in and user defined function in R
Vectorization in R
Data Structure Data Manipulation, Data Reshaping, Data visualization
Data visualization using base R, ggplot2 and other visualization libraries.
Reading and importing and handling missing data from different source (CSV, Excel, databases).
Different Case studies and practical projects.

Who is this for?

Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics.

IT professionals seeking to expand their skills into the field of data analytics using R.

Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.

What You Need to Know?

Having a basic understanding of programming concepts can be beneficial.

A foundational understanding of basic statistical concepts like mean, median, standard deviation, and so on.

Basic mathematical operations used in data analytics.

An awareness of fundamental data concepts, such as types of data, and basic data structures, can be beneficial.

More details

Description
Unlock the power of data with our comprehensive "Data Analytics Using R Programming" course. In this immersive learning experience, participants will delve into the world of data analytics, mastering the R programming language to extract valuable insights from complex datasets. Whether you're a seasoned data professional or a newcomer to the field, this course provides a solid foundation and advanced techniques to elevate your analytical skills.

Key Learning Objectives:

R Programming Fundamentals:
Gain a deep understanding of the R programming language, covering syntax, data structures, and essential functions.
Data Import and Cleaning:
Learn how to import data from various sources and perform data cleaning and preprocessing to ensure accurate analysis.
Exploratory Data Analysis (EDA):
Develop skills in descriptive statistics, data summarization, and advanced visualization techniques using ggplot2.
Real-World Applications:
Apply your newfound knowledge to real-world data analytics challenges, working on hands-on projects that simulate the complexities of professional scenarios.
Course Format:
This course is delivered through a combination of video lectures, hands-on exercises, and real-world projects. Participants will have access to a supportive online community and regular opportunities for live Q&A sessions.

By the end of this course, you will be equipped with the skills to navigate the data analytics landscape confidently, making informed decisions and uncovering hidden patterns in data. Join us on this journey to become a proficient data analyst using the versatile R programming language. Enroll today and harness the power of data!
Who this course is for:
Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics.
IT professionals seeking to expand their skills into the field of data analytics using R.
Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.

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