Certificate Program in Data Science using R

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

paid 16 Students Enrolled
Created By Imurgence Learning Last Updated Thu, 12-Sep-2019
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Description
This Imurgence course covers topics including basic statistics, probability, inferential statistics, and linear and logistic regression.
Free Courses Included

  • 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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Reviews
  • Sat, 06-Jul-2019
    Harish Reddy
    I am a Btech student, I enrolled for this course and am studying R. I would want to mention that course focuses on training us on Data Analytics and Statistical concepts. I have just started with the Advanced Machine learning and I believe learning Data Analytics and Data Science has helped me in understanding these advanced concepts.
  • Wed, 17-Jul-2019
    Mitesh Purohit
    I am a BE student, I enrolled for this course and I am studying statistical programming using R tool. The course content are easy to learn and understand I got 94 marks in this course.
  • Mon, 22-Jul-2019
    Kush Verma
    This data science course is instructive, and having a great deal of practical exercises/projects. The Technical chat group is extremely receptive to respond to our inquiries ,this makes learning additionally engrossing. It is a joy learning!
  • Tue, 23-Jul-2019
    hiza das
    Overall its an informative course for having a decent comprehension of the subject, it will even give a push forward to those who want to master the field of Data Science using R language.
  • Thu, 25-Jul-2019
    Fatima Shaikh
    Amazing course, and it surely seems to be have made with a ton of diligent work by the tutors. Beginning from nuts and bolts to covering the intricacies of the Data Science topics . Extraordinary Experience, the mentors are well grounded when they are talking about.
  • Fri, 26-Jul-2019
    naksh mahajan
    I'm absolutely amazed at the quantity and quality of content this course provides. It took more than a couple of months to cover it , but today when I look back I find it justifiable even if it meant adding long hours of study to my daily schedule.Thank you team for creating this content, it definitely is worth more than what I paid.
  • Sat, 27-Jul-2019
    chetan tyagi
    Commendation for the content team who definitely knows explaining math and stats in a simple language to emphasize the topics I will say learning inferential, probability, linear and logistics regression was made simple. Highly Recommended!
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EMI starts at 1000/month for 2 months. View Plan >
Includes:
  • Self-Paced Training
  • 9:11:47 Hours Videos
  • 102 Lessons
  • Certification Course
  • All Levels