Decision Tree Modeling using R Certification Training
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Overview Getting a crystal clear understanding of Decision Tree Modeling Using R Certification Training is vital to apply the most …
Overview
Getting a crystal clear understanding of Decision Tree Modeling Using R Certification Training is vital to apply the most in-demand technique in the analytics industry. This training will enable you to use specialised skills in a number of business scenarios such as telecom, automobile, and manufacturing industry. If you are interested in learning the Decision Tree algorithm, this is the right course to get started.
The Decision Tree Modeling Using R Certification Training will allow you to learn what decision tree is, where to apply it, what the benefits are, what different algorithms behind it are, and how you can develop a decision tree using R. This course covers rich material through videos in HD format, PDF files, downloadable Excel files, and R Software.
The Decision Tree Modeling Using R Certification Training will also teach you advanced concepts such as Meta and Graph Patterns, Input-Output Patterns, Graph Patterns, etc. Learning valuable concepts such as Pruning, CHAID, CART, and Regression Tree will set you on a course of continued success in the challenging industry. Consider taking this course as soon as possible if you want to excel as an in-demand Decision Tree professional.
Why You Should Take This Training at Study365?
Study365 is a leading online provider for several accrediting bodies and provides learners with the opportunity to take this exclusive course awarded by Edureka. At Study365, we give our fullest attention to our learners’ needs and ensure they have the necessary information to proceed with the Course.
Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships, and their favourite restaurants.
- Access Duration
- Who Is This Course For?
- Entry Requirements
- Certification
- Awarding Body
- Method of Assessment
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months of access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for flourishing your career as a:
- Decision Tree Expert
- Decision Tree Analyst
- Business Manager
- Consultant and Trainer
- Learners should have a basic know-how of English, ICT, and numeracy.
- A sound educational background is preferable for this course.
- Basic R programming skills are desirable.
Upon successfully completing the course, you will be awarded the 'Decision Tree Modelling using R Certification Training' by Edureka.
Edureka is the fastest-growing online learning platform with a trusted name in the industry. The platform has the highest course completion rate and turns beliefs into realities by ridiculously committing to their students. Edureka collaborates with Study365 and many other educational bodies to provide guaranteed learning and success to global students & professionals.
To successfully obtain this Edureka certification, learners will have to submit an assignment that proves their worth and skill related to this particular course.
If you want to become seasoned analytics professional in the challenging IT industry, the Decision Tree Modeling Using R Certification Training will give you the skills and knowledge to make an impact. You will apply techniques to perform actionable analytics and become an influential data scientist or strategist. The top organisations will recognise your skills and consider you a viable candidate for the most challenging roles.
Given below are job titles you can compete for, along with the average UK salary per annum, according to https://www.glassdoor.com.
- Decision Tree Analyst - £95,000 (Approximately)
- Data Analyst- £27755 Per Year Approx.
Course Curriculum
| 1. Introduction to Decision Tree | |||
| Decision Tree Modeling Objective | |||
| Anatomy of a Decision Tree | |||
| Gains from a Decision Tree (KS Calculations) | |||
| Definitions Related To Objective Segmentations | |||
| 2. Data Design for Modelling | |||
| Historical Window | |||
| Performance Window | |||
| Decide Performance Window Horizon Using Vintage Analysis | |||
| General Precautions Related to Data Design | |||
| 3. Data Treatment before Modelling | |||
| Data Sanity Check-Contents | |||
| View | |||
| Frequency Distribution | |||
| Means / Uni-variate | |||
| Categorical Variable Treatment | |||
| Missing Value Treatment Guideline | |||
| Capping Guideline | |||
| 4. Classification of Tree Development and Algorithm Details | |||
| Preamble to Data | |||
| Installing R Package and R Studio | |||
| Developing First Decision Tree in R Studio | |||
| Find Strength of the Model | |||
| Algorithm behind Decision Tree | |||
| How is a Decision Tree Developed? | |||
| First on Categorical Dependent Variable | |||
| GINI Method | |||
| Steps Taken By Software Programs to Learn the Classification (Develop the Tree) | |||
| Assignment on Decision Tree | |||
| 5. Industry Practice of Classification Tree-Development, Validation and Usage | |||
| Discussion on Assignment | |||
| Find Strength of the Model | |||
| Steps Taken by Software Program to Implement the Learning on Unseen Data | |||
| Learning More from Practical Point of View | |||
| Model Validation and Deployment. | |||
| 6. Regression Tree and Auto Pruning | |||
| Introduction to Pruning | |||
| Steps of Pruning | |||
| Logic of Pruning | |||
| Understand K Fold Validation for Model | |||
| Implement Auto Pruning Using R | |||
| Develop Regression Tree | |||
| Interpret the Output | |||
| How It is Different from Linear Regression | |||
| Advantages and Disadvantages over Linear Regression | |||
| Another Regression Tree Using R | |||
| 7. CHAID Algorithm | |||
| Key Features of CART | |||
| Chi Square Statistics | |||
| Implement Chi Square for Decision Tree Development | |||
| Syntax for CHAID Using R | |||
| CHAID vs CART | |||
| 8. Other Algorithms | |||
| Entropy in the Context of Decision Tree | |||
| ID3 | |||
| Random Forest Method and Using R for Random Forest Method | |||
| Project Work | |||
Ali Brooks
The course has covered everything that you need to know when learning about the Decision tree modeling.
Cory Palmer
This is how courses should be devised. I feel as if this course was prepared especially for me.
Reed Porter
I had no problems at all understanding the various concepts explained in the course which I didn’t understand before.
Glenn Hart
I feel as if my knowledge base has grown and I can expertly use MapReduce.