Certificate Program in Data Science using R

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

paid 0(0 Ratings) 7 Students Enrolled
Created By Imurgence Learning Last Updated Thu, 18-Apr-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.

Target Audience
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.

Access Time frame
One Year  from the date of access.

Prerequisites
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

Type of Certification
Certificate of Completion

Format of Certification
Digital

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

Method of Obtaining Certification
Upon successfully completing 80% of this course, the learner will be able to download digital copy of the Certificate and Mark sheet from the Certificates section . The Mark Sheet will keep on updating as the learner progresses towards 100% completion.



Curriculum For This Course
102 Lessons 9:11:47 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 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 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 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 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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Includes:
  • Self-Paced Training
  • 9:11:47 Hours Videos
  • 102 Lessons
  • Certification Course
  • All Levels