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Students - You'll know how and why use R for data science and machine learning
- You'll get practical tips and advise on Data Scientist's career
- You'll work on practical tasks and examine case studies to fully understand the fundamentals of R programming
- You'll be able to use the core features and packages of R

22h 31m

02:40

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56:59

Introduction To Data Scientist

12:31

How to switch your career into ML part 1

14:56

How to switch your career into ML part 2

03:31

22:41

Course Curriculum Overview

22:41

26:15

Introduction to R

15:07

Setting up R

11:08

55:14

R Operator

13:45

R Conditional Statement & Loop

12:02

R Programming - R Function #1

13:20

R Programming - R Function #2

10:10

R Programming - R Function #3

05:57

49:23

An Introduction of R Data Structure + Vector

11:13

Matrix, Array and Data Frame

14:37

A Deep Drive to R Data Frame

13:02

Factor

04:12

R Data Structure - List

06:19

32:16

Import CSV Data in R

09:26

Import Text Data in R

03:19

Import Excel, Web Data in R

12:47

Export Data in R - Text

02:37

Export Data in R - CSV & Excel

04:07

1:33:49

Data Manipulation - Apply Function

13:15

Data Manipulation - select

11:46

Data Manipulation - mutate

14:28

Data Manipulation - filter

14:11

Data Manipulation - arrange

09:38

Data Manipulation - Pipe Operator

08:30

Data Manipulation - group by

11:26

Data Manipulation - Date

10:35

2:10:04

Introduction to Data Visualiztion & Scatter Plot

12:01

Data Visualization - mfrow

07:37

Data Visualization - pch

12:30

Data Visualization - Color

01:19

Data Visualization - Line Chart

03:21

Data Visualization - Bar Plot

07:05

Data Visualization - Pie Chart

06:43

Data Visualization - Histogram

07:06

Data Visualization - Density Plot

02:26

Data Visualization - Box Plot

05:01

Data Visualization - Mosaic Plot and Heat Map

07:59

Data Visualization - 3D Plot

10:39

Correlation Plot and Word Cloud

09:02

Data Visualization - ggplot2 Part 1

14:03

Data Visualization - ggplot2 Part 2

08:08

Data Visualization - ggplot2 Part 3

15:04

2:02:57

Introduction To Statistics Part 1

13:25

Introduction To Statistics Part 2

08:53

Introduction To Statistics Part 3

14:55

Introduction To Statistics Part 4

04:15

Introduction To Statistics Part 5

15:10

Introduction To Statistics Part 6

08:21

Introduction To Statistics Part 7

15:04

Introduction To Statistics Part 8

10:45

Introduction To Statistics Part 9

10:24

Introduction To Statistics Part 10

14:34

Introduction To Statistics Part 11

07:11

41:58

Hypothesis Testing Part 1

10:08

Hypothesis Testing Part 2

11:28

Hypothesis Testing Part 3

14:21

Hypothesis Testing Part 4

06:01

2:14:01

Hypothesis Testing in Practice Part 1

15:04

Hypothesis Testing in Practice Part 2

09:36

Hypothesis Testing in Practice Part 3

14:16

Hypothesis Testing in Practice Part 4

12:36

Hypothesis Testing in Practice Part 5

10:29

Hypothesis Testing in Practice Part 6

13:46

Chi Square Part 1

11:19

Chi Square Part 2

14:57

ANOVA Part 1

12:30

ANOVA Part 2

14:20

What we discussed in the chapter so far?

05:08

26:31

Machine Learning Toolbox Part 1

14:00

Machine Learning Toolbox Part 2

12:31

12:51

Business Case Understanding

12:51

1:24:14

Data Pre-Processing part 1

14:45

Data Pre-Processing part 2

14:29

Data Pre-Processing part 3

10:25

Data Pre-Processing part 4

09:39

Data Pre-Processing part 5

12:33

Data Pre-Processing part 6

07:19

Data Pre-Processing part 7

15:04

3:06:14

Linear Regression part 1

11:47

Linear Regression part 2

14:23

Linear Regression part 3

20:21

Linear Regression part 4

19:02

Linear Regression part 5

25:00

Linear Regression part 6

15:02

Linear Regression part 7 - Correlation Part 1

14:31

Linear Regression part 7 - Correlation Part 2

13:44

Linear Regression part 8 - Stepwise Regression

12:52

Linear Regression part 9 - Stepwise Regression

16:03

Linear Regression part 10 - Dummy Variable

12:34

Linear Regression part 11 - Non Linear

10:55

13:29

Classification Overview

13:29

1:11:26

Logistics Regression Intuition

14:04

R Code Implementation Part 1

05:09

R Code Implementation Part 2

10:37

Model Evaluation

12:28

Telecom Churn Case Study

22:27

Summary

06:41

40:13

K-NN Intuition

13:26

K-NN R Code Implementation

12:48

K-NN Case Study

13:59

45:18

SVM - Intuition

08:44

SVM - R Code Implementation

08:22

SVM - Model Tuning

09:00

SVM - Telecom Case Study

07:56

SVM - Non Separable Case and Pros and Cons

07:27

SVM Chapter summary

03:49

36:46

Naive Bayes - Intuition

19:56

Naive Bayes - R Code Implementation

08:25

Naive Bayes - Case Study

08:25

1:06:18

Decision Tree Intuition

14:54

Decision Tree - How it works

07:40

Decision Tree - R Code Implementation

13:44

Decision Tree - Pruning

15:36

Decision Tree - Case Study

14:24

Are you one of those who wonders what programming language to learn and what data science course to take? Is the idea of learning R for data science knocking on your mind’s door, but you still have doubts of letting it in? In data science, R is getting more and more popular, and it’s not an accident. Data manipulation, data visualization, and machine learning are at the basis of building R, and the package ecosystem lets you do amazing things which can be otherwise much more complicated using other programming languages. Especially in data visualization.

If you’ve recently checked job offers, you must have noticed the increasing demand in Data Scientists. And that is a global trend. In the next 3 years, 10 Million+ new jobs will be vacant for those who spare a bit of their time to take a data science course and learn R programming. If you want to secure a job for a long time, and also earn a high salary already today, invest your time to learn R from scratch.

I intend this data science course to guide you through the basics of machine learning with R. In this course, you’ll learn data science from scratch from me, an R-native tutor, by working on 4 projects and exploring 8 case studies. These will help you with rapid progress to learn data science from scratch. R is commonly known as a bit challenging to learn, but I’ll be there to take you through the basics until you feel confident.

Realistic examples will help you answer both, “how” and “why” learn R for data science. In this 30+ hours data science course you’ll make use of 100+ pieces of study material as well as code templates, so you can put your hands on R easier and saving your time.

- When you learn R programming from scratch with me, you’ll start from the fundamentals of how data science, machine learning and artificial intelligence are beneficial in the modern world.
- I’ll explain the value of your decision to learn data science from scratch and share my experience and tips on career switching in data science.
- I’ll make sure you’re clear with such fundamentals as importing/exporting your data with R.
- You’ll also see the benefits of the dplyr package for easy data manipulation, and you’ll see why ggplot2 is so suitable for data visualization.
- We’ll explore things like data visualization, data manipulation, R’s conditional statements.

These bullet points are just the tip of data science course iceberg. We’ll power on our yellow submarine to explore R even in greater depth.

Now, I know there’s no one single programming language that will suit any needs. It’s just life. I can only tell you that Google exploits R at assessing ad effectiveness and making economic forecasts. Facebook uses R for user’s behavior analysis. Twitter's team does data visualization with R, too. Microsoft, AirBnB, Uber and other big names are also hiring R-competent data scientists, so it seems like a good idea to take a data science course in 2019 and learn programming with R. Trust me to guide you through: learn R from scratch, dip your toes into data science and discover machine learning with R.

- No prior knowledge is required to enrol this course
- Software and data needed in the course will be provided for free

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