Practical Data Science with R Training

Practical Data Science with R (PDSwR) is the course to start with if you are starting as a data scientist or aspire to be one. If you already practice data science, PDSwR will fill in knowledge gaps and even give you a new perspective on the technologies you now employ.

📘 Data Science 👥 1928 Enrolled ⏱️ 5 Days 💼 Level ⭐ 4.8 | 113 Reviews

Why Microtek Learning?

500+

Courses

10+ Years

Experience

95K+

Global Learners

Virtual Instructor-Led Training

$2999

Course Overview

This course covers data science, a discipline that builds predictive models using findings from machine learning, statistics, and computer science.

Critical business data is being gathered, curated, examined, and reported more frequently by business analysts and engineers. Without much academic theory or complex mathematics, one can efficiently complete daily Data Science work using the R language and its companion tools.

You can learn how to use the R programming language and practical statistical approaches in ordinary business circumstances with the help of experimental data science with R training.

It demonstrates how to conduct experiments (such as A/B testing), create prediction models, and deliver results to audiences of different levels using examples from business intelligence, marketing, and decision support.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will learn

Business professionals' guide to data science
Utilizing R to conduct statistical analyses
Planning through delivery: the lifespan of a project
Many immediately recognizable usage cases
Effective data presentations: key elements

Who Should Attend This Course?

Data Scientists
Data Engineers
Data Analysts
Business Analysts

Prerequisites

Familiarity with SQL databases, statistics, and R

📞 Talk to a Learning Advisor

📘 Practical Data Science with R Outline

a. The data science process

The roles in a data science project
Stages of a data science project
Setting expectations

b. Loading data into R

Working with data from files
Working with relational databases

c. Exploring data

Using summary statistics to spot problems
Spotting problems using graphics and visualization

d. Managing data

Cleaning data
Sampling for modeling and validation

a. Choosing and evaluating models

Mapping problems to machine learning tasks
Evaluating models
Validating models

b. Memorization methods

KDD and KDD Cup 2009
Building single-variable models
Building models using many variables

c. Linear and logistic regression

Using linear regression
Using logistic regression

d. Unsupervised methods

Cluster analysis
Association rules

e. Exploring advanced methods

Using bagging and random forests to reduce training variance
Using generalized additive models (GAMs) to learn nonmonotone relationships
Using kernel methods to increase data separation
Using SVMs to model complicated decision boundaries

a. Documentation and deployment

The buzz dataset
Using knitr to produce milestone documentation
Using comments and version control for running documentation
Deploying models

b. Producing effective presentations

Presenting your results to the project sponsor
Presenting your model to end users
Presenting your work to other data scientists

Still have questions?

Reach out to our learning advisors for personalized guidance on choosing the right course, group training, or enterprise packages.

📞 Talk to an Advisor

What You Get with Microtek Learning

Instructor-Led Excellence

✓ Certified Instructor-led Training
✓ Top Industry Trainers
✓ Official Student Handbooks

Measurable Learning Outcomes

✓ Pre- & Post-Training Assessments
✓ Practice Tests
✓ Exam-Oriented Curriculum

Real-World Skill Building

✓ Hands-on Activities & Scenarios
✓ Interactive Online Courses
✓ Peer Collaboration (Not in self-paced)

Full Support & Perks

✓ Exam Scheduling Support ^*
✓ Learn & Earn Program ^*
✓ Support from Certified Experts
✓ Gov. & Private Pricing ^*

Our Clients

For over 10 years, Microtek Learning has helped organizations, leaders, students and professionals to reach their maximum potential. We have led the path by addressing their challenges and advancing their performances.