Learn concepts of data science using R programming with hands-on case studies.

Created By Imurgence i
Last Updated Mon, 11-Feb-2019

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Description

This Imurgence course covers topics including basic statistics, probability, inferential statistics, and linear and logistic regression.

- Upon successful completion of this course, the learner will be skilled in R programming to perform data analytics and predictive modelling on business data.

This course is ideal for anyone looking to improve their skills or start a career in data science, business analytics, artificial intelligence (AI) or machine learning.

Six months from the date of access.

There are no prerequisites for this course, but a general understanding of statistics and an inclination to learn coding would benefit the learner. Basic understanding of R Programming is essential to complete and understand the course.

Recommended : Certificate Program in Data Analytics using R

Certificate of Completion

Digital

The certificate is issued by Imurgence an autonomous institution. The certificate is endorsed by SiCureMi an IIT Delhi incubated Health care analytics company.

Upon successful completion of this course, the learner will be sent a digital copy of the certificate to their email.

Curriculum For This Course

102 Lessons
09:11:47 Hours

Basic Statistics

15 Lessons
01:03:51 Hours

- Introduction to Statistics 1.1 00:01:22
- Introduction to Statistics 1.2 00:03:44
- Introduction to Statistics 1.3 00:06:20
- Introduction to Statistics 1.4 00:04:54
- Introduction to Statistics 1.5 00:03:51
- Introduction to Statistics 1.6 00:06:10
- Introduction to Statistics 1.7 00:07:11
- Introduction to Statistics 1.8 00:05:11
- Introduction to Statistics 1.9 00:02:11
- Introduction To Statistics 1.10 00:00:58
- Introduction To Statistics 1.11 00:04:38
- Introduction To Statistics 1.12 00:02:26
- Introduction To Statistics 1.13 00:04:42
- Introduction To Statistics 1.14 00:05:51
- Introduction to Statistics 1.15 00:04:22

Probability

6 Lessons
00:35:32 Hours

- Probability Theory 2.1 00:06:07
- Probability Theory 2.2 00:06:02
- Probability Theory 2.3 00:04:49
- Probability Theory 2.4 00:06:19
- Probability Theory 2.5 00:06:18
- Inferential Statistics 3.15 00:05:57

Inferential

14 Lessons
01:00:58 Hours

- Inferential Statistics 3.1 00:01:53
- Inferential Statistics 3.2 00:01:28
- Inferential Statistics 3.3 00:04:27
- Inferential Statistics 3.4 00:04:14
- Inferential Statistics 3.5 00:08:20
- Inferential Statistics 3.6 00:04:26
- Inferential Statistics 3.7 00:06:09
- Inferential Statistics 3.8 00:06:21
- Inferential Statistics 3.9 00:05:09
- Inferential Statistics 3.10 00:06:37
- Inferential Statistics 3.11 00:02:38
- Inferential Statistics 3.12 00:03:32
- Inferential Statistics 3.13 00:03:58
- Inferential Statistics 3.14 00:01:46

Linear Regression

30 Lessons
02:53:21 Hours

- Linear Regression 4.1 00:04:01
- Linear Regression 4.2 00:03:27
- Linear Regression 4.3 00:02:42
- Linear Regression 4.4 00:04:56
- Linear Regression 4.5 00:08:09
- Linear Regression 4.6 00:01:56
- Linear Regression 4.7 00:03:04
- Linear Regression 4.8 00:02:39
- Linear Regression 4.9 00:06:42
- Linear Regression 4.10 00:05:45
- Simple Linear Regression 4.11 00:04:22
- Simple Linear Regression 4.12 00:05:45
- Simple Linear Regression 4.13 00:07:18
- Simple Linear Regression 4.14 00:04:46
- Simple Linear Regression 4.15 00:07:46
- Simple Linear Regression 4.16 00:10:57
- Dummy Variables 4.17 00:05:33
- Multiple Linear Regression 4.18 00:05:20
- Multiple Linear Regression 4.19 00:03:14
- Model Optimization 4.20 00:07:45
- Model Optimization 4.21 00:04:34
- Multiple Linear Regression 4.22 00:08:17
- Multiple Linear Regression 4.23 00:06:15
- Multiple Linear Regression on Boston Dataset 4.24 00:07:59
- Multiple Linear Regression on Boston Dataset 4.25 00:06:52
- Multiple Linear Regression on Boston Dataset 4.26 00:09:20
- Multiple Linear Regression on Boston Dataset 4.27 00:04:03
- Multiple Linear Regression on Boston Dataset 4.28 00:07:25
- Multiple Linear Regression on Boston Dataset 4.29 00:08:24
- Multiple Linear Regression on Boston Dataset 4.30 00:04:05

Logistic Regression

37 Lessons
03:38:05 Hours

- Logistic Regression 5.1 00:04:51
- Logistic Regression 5.2 00:06:53
- Logistic Regression 5.3 00:03:46
- Logistic Regression 5.4 00:10:58
- Logistic Regression 5.5 00:10:01
- Logistic Regression 5.6 00:04:47
- Logistic Regression 5.7 00:07:58
- Logistic Regression 5.8 00:04:22
- Logistic Regression 5.9 00:06:29
- Logistic Regression 5.10 00:07:34
- Logistic Regression 5.11 00:08:15
- Logistic Regression 5.12 00:03:07
- Logistic Regression 5.13 00:07:41
- Logistic Regression on Diabetics dataset 5.14 00:05:25
- Logistic Regression on Diabetes Dataset 5.15 00:07:41
- Logistic Regression on Diabetes Dataset 5.16 00:10:08
- Logistic Regression on Diabetes Dataset 5.17 00:03:11
- Logistic Regression on Diabetes Dataset 5.18 00:03:11
- Logistic Regression on Diabetes Dataset 5.19 00:07:42
- Logistic Regression on Diabetes Dataset 5.20 00:06:46
- Logistic Regression on Diabetes Dataset 5.21 00:02:50
- Credit Risk Case using Logistic Regression 5.22 00:04:34
- Credit Risk Case using Logistic Regression 5.23 00:05:18
- Credit Risk Case using Logistic Regression 5.24 00:02:24
- Credit Risk Case using Logistic Regression 5.25 00:05:18
- Credit Risk Case using Logistic Regression 5.26 00:05:42
- Credit Risk Case using Logistic Regression 5.27 00:04:50
- Credit Risk Case using Logistic Regression 5.28 00:10:54
- Credit Risk Case using Logistic Regression 5.29 00:06:48
- Credit Risk Case using Logistic Regression 5.30 00:03:39
- Credit Risk Case using Logistic Regression 5.31 00:05:56
- Credit Risk Case using Logistic Regression 5.32 00:03:25
- Credit Risk Case using Logistic Regression 5.33 00:05:18
- Credit Risk Case using Logistic Regression 5.34 00:05:42
- Credit Risk Case using Logistic Regression 5.35 00:04:43
- Credit Risk Case using Logistic Regression 5.36 00:05:52
- ROC Curve 5.37 00:04:06

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