Certificate Program in Data Science & Advanced Machine Learning using R

Learn concepts of data analytics, data science and advanced machine learning using R with hands-on case studies

paid 26 Students Enrolled
Created By Imurgence Learning Last Updated Wed, 25-Sep-2019
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Description
This is an advanced course by Imurgence using R which dives deep into data analytics, R interface, and data handling by thoroughly understanding data structure and data types in R, R internal functions, data with manipulation and visualisation, basic statistics, probability, inferential statistics, linear and logistic regression, decision tree, ensemble learning, support vector machines, market basket analysis, k-nearest neighbours, clustering and artificial neural network.
Free Courses Included

  • Upon successful completion of this course, the learner will be skilled in R programming to perform data analytics and machine learning 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.

Type of Certification
Certificate of Completion

Format of Certification
Digital

Professional Association/Affiliation
The certificate is issued by Imurgence an autonomous institution and endorsed by SiCureMi an IIT Delhi incubated Analytics Firm.

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
281 Lessons 26:24:08 Hours
Introduction to Data Analytics
6 Lessons 00:40:33 Hours
  • Overview and Scope of Analytics 1.1 00:10:57
  • Overview and Scope of Analytics 1.2 00:08:26
  • Overview and Scope of Analytics 1.3 00:12:24
  • Introduction to R 1.4 00:04:14
  • Introduction to R 1.5 00:02:02
  • Application of Analytics 1.6 00:02:30
  • R Base and R Studio Installation 2.1 00:04:32
  • How to Use R Studio 2.2 00:02:34
  • How to Use R Studio 2.3 00:08:56
  • Data Types 3.1 00:05:14
  • Data Types 3.2 00:06:33
  • Data Types 3.3 00:04:01
  • Data Types 3.4 00:07:42
  • Data Types 3.5 00:05:48
  • Data Types 3.6 00:09:25
  • Arithmetic Operators in R 3.7 00:04:54
  • Relational Operators in R 3.8 00:06:24
  • Logical Operators in R 3.9 00:09:12
  • Assignment Operators in R 3.10 00:04:09
  • Matrix 3.11 00:05:54
  • Matrix 3.12 00:02:01
  • Matrix 3.13 00:06:40
  • Matrix 3.14 00:06:20
  • Matrix 3.15 00:12:19
  • Factors 3.16 00:04:56
  • Factors 3.17 00:06:59
  • Factors 3.18 00:09:31
  • Factors 3.19 00:03:37
  • Lists 3.20 00:04:51
  • Lists 3.21 00:07:08
  • DataFrames 3.22 00:04:46
  • DataFrames 3.23 00:08:04
  • DataFrames 3.24 00:05:13
  • DataFrames 3.25 00:09:02
  • if statement 4.1 00:06:18
  • if else statement 4.2 00:13:17
  • Switch Statement 4.3 00:04:05
  • Repeat Loop 4.4 00:05:47
  • While loop 4.5 00:03:17
  • For loop 4.6 00:02:13
  • Apply function 5.1 00:04:02
  • Apply Function 5.2 00:07:53
  • Apply Function 5.3 00:04:27
  • Apply Function 5.4 00:02:14
  • Lapply Function 5.5 00:04:41
  • Sapply Function 5.6 00:10:08
  • Vapply Function 5.7 00:02:26
  • Tapply Function 5.8 00:03:57
  • User-defined Functions 5.9 00:07:32
  • User-defined Functions 5.10 00:08:32
  • Import and Export of Data 6.1 00:05:15
  • Sample Function 6.2 00:10:55
  • Data Exploration in R 6.3 00:06:36
  • Data Exploration in R 6.4 00:09:43
  • Data Exploration in R 6.5 00:05:44
  • Missing Values 6.6 00:08:49
  • Basic Plot 7.1 00:08:04
  • Plotting Categorical Data 7.2 00:04:54
  • Histogram 7.3 00:10:14
  • Pie Chart 7.4 00:09:40
  • Introduction To Statistics 8.1 00:01:22
  • Introduction To Statistics 8.2 00:03:44
  • Introduction To Statistics 8.3 00:06:20
  • Introduction to Statistics 8.4 00:04:54
  • Introduction To Statistics 8.5 00:03:51
  • Introduction To Statistics 8.6 00:06:10
  • Introduction To Statistics 8.7 00:07:11
  • Introduction To Statistics 8.8 00:05:11
  • Introduction To Statistics 8.9 00:02:11
  • Introduction To Statistics 8.10 00:00:58
  • Introduction To Statistics 8.11 00:04:38
  • Introduction To Statistics 8.12 00:02:26
  • Introduction To Statistics 8.13 00:04:42
  • Introduction To Statistics 8.14 00:05:51
  • Introduction To Statistics 8.15 00:04:22
  • Probability Theory 9.1 00:06:07
  • Probability Theory 9.2 00:06:02
  • Probability Theory 9.3 00:04:49
  • Probability Theory 9.4 00:06:19
  • Probability Theory 9.5 00:06:18
  • Inferential Statistics 10.1 00:01:53
  • Inferential Statistics 10.2 00:01:28
  • Inferential Statistics 10.3 00:04:27
  • Inferential Statistics 10.4 00:04:14
  • Inferential Statistics 10.5 00:08:20
  • Inferential Statistics 10.6 00:04:26
  • Inferential Statistics 10.7 00:06:09
  • Inferential Statistics 10.8 00:06:21
  • Inferential Statistics 10.9 00:05:09
  • Inferential Statistics 10.10 00:06:37
  • Inferential Statistics 10.11 00:02:38
  • Inferential Statistics 10.12 00:03:32
  • Inferential Statistics 10.13 00:03:58
  • Inferential Statistics 10.14 00:01:46
  • Inferential Statistics 10.15 00:05:57
  • Linear Regression 11.1 00:05:01
  • Linear Regression 11.2 00:03:27
  • Linear Regression 11.3 00:02:42
  • Linear Regression 11.4 00:04:56
  • Linear Regression 11.5 00:08:09
  • Linear Regression 11.6 00:01:56
  • Linear Regression 11.7 00:03:04
  • Linear Regression 11.8 00:02:39
  • Linear Regression 11.9 00:06:42
  • Linear Regression 11.10 00:05:45
  • Simple Linear Regression 11.11 00:04:22
  • Simple Linear Regression 11.12 00:05:45
  • Simple Linear Regression 11.13 00:07:18
  • Simple Linear Regression 11.14 00:04:46
  • Simple Linear Regression 11.15 00:07:46
  • Simple Linear Regression 11.16 00:10:57
  • Dummies 11.17 00:05:33
  • Multiple Linear Regression 11.18 00:05:20
  • Multiple Linear Regression 11.19 00:03:14
  • Model Optimisation 11.20 00:07:45
  • Model Optimization 11.21 00:04:34
  • Multiple Linear Regression 11.22 00:08:17
  • Multiple Linear Regression 11.23 00:06:15
  • Multiple Linear Regression on Boston Dataset 11.24 00:07:59
  • Multiple Linear Regression on Boston Dataset 11.25 00:06:52
  • Multiple Linear Regression on Boston Dataset 11.26 00:09:20
  • Multiple Linear Regression on Boston Dataset 11.27 00:04:03
  • Multiple Linear Regression on Boston Dataset 11.28 00:07:25
  • Multiple Linear Regression on Boston Dataset 11.29 00:08:24
  • Multiple Linear Regression on Boston Dataset 11.30 00:04:05
  • Logistic Regression 12.1 00:04:51
  • Logistic Regression 12.2 00:06:53
  • Logistic Regression 12.3 00:03:46
  • Logistic Regression 12.4 00:10:58
  • Logistic Regression 12.5 00:10:01
  • Logistic Regression 12.6 00:04:47
  • Logistic Regression 12.7 00:07:58
  • Logistic Regression 12.8 00:04:22
  • Logistic Regression 12.9 00:06:29
  • Logistic Regression 12.10 00:07:34
  • Logistic Regression 12.11 00:08:15
  • Logistic Regression 12.12 00:03:07
  • Logistic Regression 12.13 00:07:41
  • Logistic Regression on Diabetes Dataset 12.14 00:05:25
  • Logistic Regression on Diabetes Dataset 12.15 00:07:41
  • Logistic Regression on Diabetes Dataset 12.16 00:10:08
  • Logistic Regression on Diabetes Dataset 12.17 00:03:11
  • Logistic Regression on Diabetes Dataset 12.18 00:03:11
  • Logistic Regression on Diabetes Dataset 12.19 00:07:42
  • Logistic Regression on Diabetes Dataset 12.20 00:06:46
  • Logistic Regression on Diabetes Dataset 12.21 00:02:50
  • Credit Risk Case using Logistic Regression 12.22 00:04:34
  • Credit Risk Case using Logistic Regression 12.23 00:01:51
  • Credit Risk Case using Logistic Regression 12.24 00:02:24
  • Credit Risk Case using Logistic Regression 12.25 00:05:18
  • Credit Risk Case using Logistic Regression 12.26 00:05:42
  • Credit Risk Case using Logistic Regression 12.27 00:04:50
  • Credit Risk Case using Logistic Regression 12.28 00:10:54
  • Credit Risk Case using Logistic Regression 12.29 00:06:48
  • Credit Risk Case using Logistic Regression 12.30 00:03:39
  • Credit Risk Case using Logistic Regression 12.31 00:05:56
  • Credit Risk Case using Logistic Regression 12.32 00:03:25
  • Credit Risk Case using Logistic Regression 12.33 00:05:18
  • Credit Risk Case using Logistic Regression 12.34 00:05:42
  • Credit Risk Case using Logistic Regression 12.35 00:04:43
  • Credit Risk Case using Logistic Regression 12.36 00:05:52
  • ROC Curve 12.37 00:04:06
  • Introduction to Machine Learning 13.0 00:04:44
  • Decision Tree 13.1 00:05:59
  • Decision Tree 13.2 00:06:42
  • Decision Tree 13.3 00:03:09
  • Decision Tree 13.4 00:05:57
  • Decision Tree 13.5 00:04:55
  • Decision Tree 13.6 00:03:04
  • Decision Tree 13.7 00:06:39
  • Decision Tree 13.8 00:09:28
  • Decision Tree 13.9 00:09:18
  • Decision Tree on Diabetes Dataset 13.10 00:03:54
  • Decision Tree on Diabetes Dataset 13.11 00:06:59
  • Decision Tree on Diabetes Dataset 13.12 00:02:30
  • Random Forest 14.1 00:08:00
  • Random Forest 14.2 00:03:22
  • Random Forest 14.3 00:05:39
  • Random Forest 14.4 00:05:23
  • Random Forest 14.5 00:06:37
  • Random Forest on Diabetes Dataset 14.6 00:03:56
  • Random Forest on Diabetes Dataset 14.7 00:06:59
  • Random Forest on Diabetes Dataset 14.8 00:02:30
  • Random Forest on Diabetes Dataset 14.9 00:05:26
  • Random Forest Regression 14.10 00:04:27
  • Random Forest Regression 14.11 00:05:17
  • Random Forest Regression 14.12 00:01:01
  • Random Forest Regression 14.13 00:04:50
  • Random Forest Regression 14.14 00:05:32
  • Random Forest Regression 14.15 00:02:56
  • IT Network Intrusion Detection Case using Decision Tree 14.16 00:05:37
  • IT Network Intrusion Detection Case using Decision Tree 14.17 00:07:30
  • IT Network Intrusion Detection Case using Decision Tree 14.18 00:04:39
  • IT Network Intrusion Detection Case using Decision Tree 14.19 00:03:01
  • IT Network Intrusion Detection Case using Decision Tree 14.20 00:07:44
  • IT Network Intrusion Detection Case using Decision Tree 14.21 00:03:02
  • IT Network Intrusion Detection Case using Decision Tree 14.22 00:08:12
  • IT Network Intrusion Detection Case using Decision Tree 14.23 00:04:37
  • IT Network Intrusion Detection Case using Decision Tree 14.24 00:04:14
  • Support Vector Machine 15.1 00:04:00
  • Support Vector Machine 15.2 00:04:35
  • Support Vector Machine 15.3 00:04:44
  • Support Vector Machine 15.4 00:02:54
  • Support Vector Machine 15.5 00:08:42
  • Support Vector Machine 15.6 00:04:51
  • Support Vector Machine 15.7 00:04:20
  • Support Vector Machine 15.8 00:06:18
  • Support Vector Machine 15.9 00:05:35
  • Support Vector Machine 15.10 00:02:59
  • Support Vector Machine 15.11 00:03:11
  • Support Vector Machine 15.12 00:03:29
  • Support Vector Machine 15.13 00:03:03
  • SVM on Iris Dataset 15.14 00:03:45
  • SVM on Iris Dataset 15.15 00:08:13
  • SVM on Iris Dataset 15.16 00:07:08
  • SVM on Iris Dataset 15.17 00:07:12
  • SVM on Iris Dataset 15.18 00:04:30
  • SVM on Iris Dataset 15.19 00:04:07
  • Credit Risk Case Using SVM 15.20 00:04:02
  • Credit Risk Case Using SVM 15.21 00:04:08
  • Credit Risk Case Using SVM 15.22 00:05:13
  • Credit Risk Case Using SVM 15.23 00:06:50
  • Credit Risk Case Using SVM 15.24 00:07:25
  • K Fold Cross Validation 15.25 00:04:16
  • K Fold Cross Validation 15.26 00:09:09
  • K Fold Cross Validation 15.27 00:02:10
  • K Fold Cross Validation 15.28 00:07:28
  • Market Basket Analysis 16.1 00:06:48
  • Market Basket Analysis 16.2 00:06:12
  • Market Basket Analysis 16.3 00:03:06
  • Market Basket Analysis 16.4 00:02:35
  • French Store Analysis Using MBA 16.5 00:05:34
  • French Store Analysis Using MBA 16.6 00:05:10
  • French Store Analysis Using MBA 16.7 00:03:36
  • French Store Analysis Using MBA 16.8 00:08:37
  • French Store Analysis Using MBA 16.9 00:05:32
  • k Nearest Neighbours 17.1 00:07:31
  • k Nearest Neighbours 17.2 00:03:36
  • k Nearest Neighbours 17.3 00:05:09
  • kNN on Advertisement Dataset 17.4 00:04:50
  • kNN on Advertisement Dataset 17.5 00:05:19
  • kNN on Advertisement Dataset 17.6 00:04:16
  • kNN on Cancer Dataset 17.7 00:02:42
  • kNN on Cancer Dataset 17.8 00:06:17
  • kNN on Cancer Dataset 17.9 00:03:48
  • kNN on Cancer Dataset 17.10 00:04:43
  • kNN on Cancer Dataset 17.11 00:05:38
  • kNN on Cancer Dataset 17.12 00:09:03
  • Customer Churn using kNN 17.13 00:11:11
  • Customer Churn using kNN 17.14 00:06:50
  • Customer Churn using kNN 17.15 00:06:12
  • Customer Churn using kNN 17.16 00:10:32
  • kMeans 18.1 00:03:18
  • kMeans 18.2 00:05:45
  • kMeans 18.3 00:03:42
  • kMeans 18.4 00:04:24
  • kMeans 18.5 00:06:30
  • kMeans Customer Segmentation Case 18.6 00:03:35
  • kMeans Customer Segmentation Case 18.7 00:06:26
  • kMeans Customer Segmentation Case 18.8 00:04:15
  • kMeans Customer Segmentation Case 18.9 00:06:05
  • kMeans Customer Segmentation Case 18.10 00:03:30
  • Artificial Neural Network 19.1 00:01:52
  • Artificial Neural Network 19.2 00:07:35
  • Artificial Neural Network 19.3 00:06:14
  • Artificial Neural Network 19.4 00:02:04
  • Artificial Neural Network 19.5 00:03:59
  • Artificial Neural Network 19.6 00:06:41
  • Artificial Neural Network 19.7 00:06:42
  • Artificial Neural Network 19.8 00:03:32
  • Artificial Neural Network 19.9 00:08:28
  • Artificial Neural Network 19.10 00:15:31
  • Artificial Neural Network 19.11 00:11:24
  • Artificial Neural Network 19.12 00:08:17
  • Artificial Neural Network 19.13 00:05:57
  • Bank Customer Churn Case Using ANN 19.14 00:05:35
  • Bank Customer Churn Case Using ANN 19.15 00:04:20
  • Bank Customer Churn Case Using ANN 19.16 00:05:31
  • Bank Customer Churn Case Using ANN 19.17 00:05:38
  • Bank Customer Churn Case Using ANN 19.18 00:05:09
  • Bank Customer Churn Case Using ANN 19.19 00:01:55
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Student Feedback
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Reviews
  • Tue, 27-Nov-2018
    Deepak Thakur
  • Mon, 26-Aug-2019
    Aakashdeep Chauhan
    I'm halfway through, but the course has already exceeded my expectations. The material can be understood at any level. Great course at an affordable price. Anybody can start the course with the basic knowledge of coding.
  • Wed, 28-Aug-2019
    Sanjay Sachdev
    So this was my first course in the field of data science and I guess I have learned a lot from this course. I learned how to execute projects on machine learning using the tool r programming. I had Great experience with Imurgence team . Overall good learning management system, they also provide an online mark sheet which you can rarely find in other data science institute.
  • Fri, 30-Aug-2019
    Shivansh Rao
    I am a student with engineering background, I studied some R & Python concepts in my education, however, I wanted to learn Data Science & Machine Learning in depth. Luckily this course has the right combination of both concepts and the online technical chat helps me in solving my queries.
  • Tue, 03-Sep-2019
    Tejas Singh
    I am a Student, who had learned R programming but I wanted to learn the r libraries vital to Data Science application. I have liked the basic course and I am sure I will enroll for advanced courses in R.
  • Fri, 06-Sep-2019
    Shaurya Batra
    For an introduction into the different aspects of R this course deserves five stars. The LMS is good and user friendly. The online support chat system is good, where you can clear all your doubts regarding the course. Thanks to Imurgence & the team.
  • Fri, 06-Sep-2019
    Ishaan Shah
    So much content and detail as compared to other courses I've paid for. I am working as a data scientist and what I've learned here can be correlated to my job role. I think this data science course of Imurgence is the best value for your money .
  • Sat, 07-Sep-2019
    Chetan Parekh
    It is a good course and I realized it after embarking on the journey of ML. I now feel, I have not wasted my time and money. Its worth for everyone. Highly recommend to go with Imurgence training institute.
  • Sat, 07-Sep-2019
    Navin Naidu
    This is one of the best course for machine learning, its current price is 1/10th of the cost offered by other institutes . I am happy to take this course and anyone can use it as a reference for future work or study.
  • Mon, 09-Sep-2019
    Shusmita Kulkarni
    I enrolled in this Advanced Machine learning and data science course, and I must say, I liked the ideas and examples used in this course.
  • Fri, 20-Sep-2019
    Parth Modi
    I have already completed 1st Module, and I found it appealing and challenging at the same time. I am currently pursuing the 2nd module of Data Science using R. I am very excited to learn advanced modules of machine learning.
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Includes:
  • Self-Paced Training
  • 26:24:08 Hours Videos
  • 281 Lessons
  • Certification Course
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