Upon successful completion of this course, the learner will be skilled in Python programming to perform data analytics on business data and apply data science concepts on data. This will give the learner ability to do predictive modelling using Linear Regression and Logistic Regression.
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
Life Time Access.
The prerequisites for this course would include Python Programming. As a recommendation the learner is advised to complete the Certificate Program in Data Analytics using Python before taking up this course.
Type of Certification
Certificate of Completion
Format of Certification
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
Introduction To Statistics 1.100:01:22
Introduction to Statistics 1.200:03:44
Introduction to Statistics 1.300:06:20
Introduction To Statistics 1.400:04:54
Introduction To Statistics 1.500:03:51
Introduction To Statistics 1.600:06:10
Introduction To Statistics 1.700:07:11
Introduction To Statistics 1.800:05:11
Introduction To Statistics 1.900:02:11
Introduction To Statistics 1.1000:00:58
Introduction To Statistics 1.1100:04:38
Introduction To Statistics 1.1200:02:26
Introduction To Statistics 1.1300:04:42
Introduction To Statistics 1.1400:05:51
Introduction To Statistics 1.1500:04:22
Linear Regression 2.100:05:01
Linear Regression 2.200:03:27
Linear Regression 2.300:02:42
Linear Regression 2.400:04:56
Linear Regression 2.500:08:09
Linear Regression 2.600:01:56
Linear Regression 2.700:03:04
Linear Regression 2.800:02:39
Linear Regression 2.900:06:42
Linear Regression 2.1000:05:45
Linear Regression 2.1100:04:32
Linear Regression 2.1200:08:20
Linear Regression 2.1300:04:50
Simple Linear Regression 2.1400:11:20
Simple Linear Regression 2.1500:04:58
Simple Linear Regression 2.1600:06:17
Simple Linear Regression 2.1700:09:17
Multiple Linear Regression 2.1800:15:45
Multiple Linear Regression 2.1900:06:32
Multiple Linear Regression 2.2000:06:45
Multiple Linear Regression 2.2100:08:29
Multiple Linear Regression 2.2200:02:50
Multiple Linear Regression 2.2300:05:00
Multiple Linear Regression 2.2400:08:18
Multiple Linear Regression on Boston Dataset 2.2500:12:58
Multiple Linear Regression on Boston Dataset 2.2600:13:06
Multiple Linear Regression on Boston Dataset 2.2700:09:14
Multiple Linear Regression on Boston Dataset 2.2800:09:51
Multiple Linear Regression on Boston Dataset 2.2900:08:35
Multiple Linear Regression on Boston Dataset 2.3000:19:18
Multiple Linear Regression on Boston Dataset 2.3100:12:12
Logistic Regression 3.100:04:51
Logistic Regression 3.200:06:53
Logistic Regression 3.300:03:46
Logistic Regression 3.400:10:58
Logistic Regression 3.500:10:01
Logistic Regression 3.600:04:47
Logistic Regression 3.700:07:58
Logistic Regression 3.800:04:22
Logistic Regression 3.900:06:29
Logistic Regression 3.1000:12:29
Logistic Regression 3.1100:10:54
Logistic Regression 3.1200:14:13
Logistic Regression on Diabetes Dataset 3.1300:11:11
Logistic Regression on Diabetes Dataset 3.1400:12:30
Logistic Regression on Diabetes Dataset 3.1500:07:49
Logistic Regression on Diabetes Dataset 3.1600:07:08
Logistic Regression on Diabetes Dataset 3.1700:16:16
Logistic Regression on Diabetes Dataset 3.1800:07:29
Logistic Regression on Diabetes Dataset 3.1900:14:03
Logistic Regression on Diabetes Dataset 3.2000:11:25
Credit Risk Case using Logistic Regression 3.2100:12:00
Credit Risk Case using Logistic Regression 3.2200:09:27
Credit Risk Case using Logistic Regression 3.2300:08:20
Credit Risk Case using Logistic Regression 3.2400:14:29
Credit Risk Case using Logistic Regression 3.2500:13:08
Credit Risk Case using Logistic Regression 3.2600:12:30
Credit Risk Case using Logistic Regression 3.2700:08:49
Credit Risk Case using Logistic Regression 3.2800:13:35
Credit Risk Case using Logistic Regression 3.2900:04:36
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I learnt Python in my college, but want to understand Data Science aspect of the software, Imurgence team is still helping me learn it, so I want to thank them for keeping me in their technical chat in spite of my course completion in Jan 19.
I was aware of Python development, however, this course has been able to train me on Data Science aspect of using Python
Awesome experience. A I appreciate the critical thinking and I thoroughly enjoyed the theory along with assessment and projects incorporated in the course, it gives us the much needed hand on practice. Excellent!
A Every session propels you to scrutinize your present methodology and search for applying the right model. Excellent theories, models and training, the best part was how tailored it was to relevant Python topics.
The basics of Python were exceptionally useful for me. I, as of now am knowledgeable in Data Science using Python, my next target is to learn the machine learning aspect of Python. A I suggest the Sales team at Imurgence, should advise Students to enroll for the Comprehensive module at the beginning itself, as this can help students a get good discount and more importantly a methodical approach to learning basic, intermediate and advanced levels.
As someone with a solid analytical foundation in Data Administration , I should state that the insights outlined in this course were totally brilliant. Thanks to Imurgence for providing this valuable content that too with installment scheme. I cleared my course with 97% and received my mark sheet and certificate.
I am working as data analyst still I enrolled and I am thoroughly enjoying the course. I feel the courses touches upon all aspects of Data Science including those which are required at project level.