Machine Learning Scientist with R

Accrediations

Data Camp

Durartion of the Course

93 Hours

Course Fee

$3,980

Mode of Learning

Self-Paced

What is The Course All About?

The Machine Learning Scientist with R Course provided by Data Camp is an online Programming course meant for building one’s expertise in R: one of the most used languages for data science and computer programming.

Who is the Course Meant For?

Being an intermediate-level course, the Machine Learning Scientist with R Course is an ideal entry-level course for learners like:

A fresher who wants to build their career in Programming, and,
An IT professional who wants to upskill themselves in their existing skillset.
Any individuals looking for a new career in programming or who want to upskill themselves in the same.

Skills: What does this program cover?

Depth and Breadth of Topics
ⓘ
- Range of topics covered. - Number of topics, comprehensiveness (introductory to advanced)
- Theoretical vs. practical balance
- Alignment with industry standards and current trends
Not Applicable
Case Studies
ⓘ
- Number and variety of case studies
- Relevance of case studies to the course topic and target audience
Not Applicable
Tools
ⓘ
- Use of industrystandard tools and technologies
- Handson experience with tools
Not Applicable
Capstone Projects/Real World Application
ⓘ
- Presence of a project or practical application
- Complexity and scope of the project
Not Applicable

Skills: NaN /30

Faculties: Who are the experts behind the course?

Academic/Industry Exp.
ⓘ
- Educational background
- Work experience in relevant industries
Not Applicable
Teaching Experience
ⓘ
- Experience teaching similar courses or topics
- Teaching style and approach (engaging, clear, accessible)
Not Applicable
Industry Recognition
ⓘ
- Awards and recognition in the field
- Contributions to opensource projects, communities, or publications
Not Applicable

Faculties: 0 /20

Delivery Methods: How Is the program delivered?

Live Interaction
ⓘ
- Availability of live lectures, Q&A sessions, or webinars
- Provide direct interaction and realtime clarification of concepts
- Quality and effectiveness of live interactions
Not Applicable
Self-Paced
ⓘ
- Flexibility in scheduling and pacing
- Prerecorded videos covering lectures, tutorials, demonstrations, and supplemental content, allowing students to engage with course material flexibly.
- Quality and availability of selfpaced materials (videos, readings, exercises)
Not Applicable
Assessment
ⓘ
- Variety of assessment types (quizzes, assignments, projects)
- Alignment of assessments with learning objectives
- Quality and timeliness of feedback on assessments
Not Applicable
Community
ⓘ
- Presence and activity of online forums, discussion boards, or other community spaces
- Level of engagement and support from instructors and moderators
Not Applicable
Collaborative
ⓘ
- Opportunities for group projects, peer review, or other collaborative activities
Not Applicable

Delivery Methods: 0 /30

Career Assistance: How does the program support career growth?

Mentoring
ⓘ
- Availability of personalized guidance and support from mentors or career coaches
Not Applicable
Personalized Assistance
ⓘ
- Availability of career Assessments (personality, skills, interests) to help learners understand their strengths and potential career paths
Not Applicable
Alumni Network
ⓘ
- Active alumni network for networking
Not Applicable
Industry Partnerships
ⓘ
- Partnerships with companies
Not Applicable

Career Assistance: 0 /15

Student Satisfaction and Feedback: What Do Students Say About the Course?

Feedback & Improvement
ⓘ
- Quantity and quality of student reviews
- Responsiveness of course providers to feedback
- Evidence of continuous improvement in the course content and delivery
Not Applicable

Alumini Feedback: 0 /5

Benchmark Labels:

Strong (75-100%): This indicates the course demonstrates exceptional strengths in this area, exceeding expectations.

Moderate (50-74%): This signifies the course performs adequately in this area, covering essential aspects.

Limited (25-49%): This highlights areas where the course could significantly improve to provide a more comprehensive learning experience.

Very Limited (1-24%): This indicates that coverage in this area may be minimal, or even non-existent.

Not Applicable: This indicates that this criteria is not applicable for this course.