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Valued Courses

Price:

$399

Instructor : Ahsan Mumtaz

Term and Conditions

1. Complete fee for courses, including any applicable taxes, must be received by Knowledge Uplift before the Student is considered to be registered. Only registered students can join the class.

2. Fee being paid is for the specific course offered by Knowledge Uplift by the esteemed teacher.

3. Any other material such as labs, books, documents etc. are not included in the course fee.

4. All material presented in the class during this course is propriety information that cannot be copied or shared without prior approval from Knowledge Uplift.

5. If you would like to purchase any content for personal use please reach out to Knowledge Uplift for further discussion.

6. In order to receive a Certificate of Completion, the Student must successfully complete the Course including all assignments, projects and tests, and fee must be paid in full beforehand.

7. The Student may request that enrollment be canceled at any time before the Student is registered for and granted access to the Course. NO REFUND WILL BE GRANTED AFTER THE FIRST CLASS.

9. Fee may not be transferred to another course once the course has begun.

10. Academy\Teachers reserve the right to reschedule some or all classes.

12. If the content is completed before the end of the course, then all remaining classes may be cancelled.

13. If the teacher becomes unavailable then the Academy is responsible to try and substitute where possible.

14. In the case that the academy or teacher is not able to complete the course due to unforeseen circumstances then we will look into a partial refund judging by was left of the course.

15. If you cannot complete the course and would like to put a hold until the next session, then the Academy reserves the right to see if it is possible or not.

16. No cheating, plagiarism or the use of any unlawful means will be tolerated throughout the execution of the course.

17. Students are responsible for using lawful material on their own throughout course execution.

18. Students should not solicit other students in the course.

19. All above rules will apply to all courses including possible free sessions where possible.

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Course Details

Business Analytics Data drives our decisions in our daily lives as well in enterprise environments. It’s the building block of our decision making and how human mind can effectively and efficiently apply problem solving to enhance its outputs. It is the gold piled up in the transnational flows and customer touch points. The businesses who are able to extract such scattered information across the enterprise and assemble them in patterns for analysis are the ones shaping future. The course is designed as core and advance modules each uncovering the concepts and applied techniques of dealing with data and applied side of analytics. Course participants will be able to develop the hands on approach to the concepts and use them while working with data and undertaking applied analytics using programming languages of R, Python and analytics platforms of Google Analytics, Microsoft Power BI and Tableau. The core module is a good unconditional run through for those who are unfamiliar with math and statistical concepts or may need to refresher of the concepts related to data analysis. It introduces the statistical programming language R and the module is designed to help even those who does not have earlier programming experience. After the core module the participants are led through the advance concepts of statistical modelling & machine learning followed by hands on project submission. The concepts covered in these lessons lay the foundation for using JavaScript in any environment.

Advance Module The module provides the participants with data mining and predictive analytics concepts to create powerful analytics. The focus is on creating info graphics, PowerPoint presentations, dash boarding and writing code in R to create data descriptive charts and infographics. Advance concepts of classification algorithms such as Cluster Analysis, Neural Networks, Discriminant Analysis, and Support Vector Machines will be looked into and how the analysis can be used in an organizational context.

Exploratory Data Analysis in Python.
Data Mining & Machine Learning-Classification.
Analysis Data Visualization, Dashboard development using Tableau.
Machine Learning – Logistic Regression, Decision Tree, Neural Net.
Machine Learning – Ensemble, Tree Net, Random Forest and MARS Project Presentations