LinkedIn Learning Alternative: 365 Data Science vs LinkedIn Learning Comparison

Course Content, Features, Pricing: Why 365 Data Science Brings More Value to Beginner Data Science Students
LinkedIn Learning, formerly Lynda, is a popular learning library that offers over 20,000+ video courses, taught by real-world professionals, on business, finance, digital design, programming, and data science topics. But is there an alternative to LinkedIn Learning for Data Science courses? While 365 Data Science shares similarities with LinkedIn Learning regarding the standard of instructors and video-format teaching style, 365’s overall learning approach differs in a few key ways that might meet students’ needs better.

Pricing: 365 Data Science vs LinkedIn Learning

365 Data Science is particularly suitable for beginner and intermediate students. The course curriculum ensures a solid foundation that allows you to understand the more complicated topics later.

Premium

$15.37 per month

Billed annually
  • Online On-demand Video
  • Gamifield Platform
  • Comprehensive Portfolio
  • Free Plan
  • Step-by-Step Curriculum
  • Real environment practice
  • Personalized learning dashboard
  • Career tracks
  • Exams
Premium

$19.00 per month

Billed annually
  • Online On-demand Video
  • Gamifield Platform
  • Comprehensive Portfolio
  • Free Plan
  • Step-by-Step Curriculum
  • Real environment practice
  • Personalized learning dashboard
  • Career tracks
  • Exams

Course Content: 365 Data Science vs LinkedIn Learning

365 Data Science

365 Data Science offers three career tracks: Data Scientist, Data Analyst, and Business Analyst. Each track starts with an Introduction to Data and Data Science that sets the groundwork for the upcoming concepts and theory.

What makes 365 Data Science particularly suitable for novice users is that each course is a natural continuation of the previous ones and considers skill levels that are already reached. You won’t need to adhere, for example, to repeated explanations just because the course authors were unsure whether a given concept had been taught in previous courses. All the Career Tracks teach fundamental statistical concepts required for working with data. Additionally, the Data Science career track dives into great detail on advanced probability concepts necessary for students to become competent data scientists who can think computationally.

LinkedIn Learning

Regarding the amount of content, LinkedIn Learning courses are over 500 on data science topics, which gives them the edge. But, on closer inspection, we can see overlap in the material in courses like Introduction to Data Science, Data Science and Analytics Career Paths and Certifications: First Steps, and Data Science Foundation Fundamentals. In addition, the considerable number of courses on any single topic can overwhelm the beginner data scientist student, who may not know which course to choose.

Like 365’s Career Tracks, LinkedIn Learning Paths offer structured way of learning the necessary skills to become a data science professional. But LinkedIn’s Data Scientist Learning path goes into less detail on some of the essential statistical concepts required for data science and omits important concepts like hypothesis testing and confidence intervals—all of which are covered in the 365 Data Scientist Career Tracks.
LinkedIn’s Data Scientist Learning Path also focuses more on the theoretical side of machine learning matters and programming in Python. It encourages students to find detailed practical courses on these topics outside of the path—which defeats the purpose of the learning path in the first place.

In contrast, the 365 Data Science Career Path consists of such comprehensive courses as Machine Learning in Python and Python Programmer Bootcamp—which lean heavily on guided hands-on exercises that gradually build students’ skill levels.

Key Features

Learning Platform

Learning platforms play an essential role in the learning experience and can often decide if students study only occasionally or every day.

The 365 Data Science platform introduced the following gamified features to increase student engagement, encouraging students to build positive learning habits.
  • XP Mechanic: Each time you finish a video or complete an exam or quiz, you gain experience points (XPs) that help you level up and climb the rank ladders.
  • Leaderboard: This is where the most ambitious learners compete for the top ranks and get rewarded for climbing divisions with coins and XPs.
  • 365 Card Collections: These are hand-drawn cards of the founding figures of mathematical sciences that grant unique in-game and real-life benefits whenever you unlock them.
LinkedIn Learning, on the other hand, does not offer gamified features. They provide standard platform features that allow you to track your course progress and set weekly goals. And, like Coursera, they have interactive transcripts that enable you to follow along and navigate through videos.

Learning Material

Learning materials help students assimilate the subject matter quicker and in ways that course videos cannot. 365 Data Science and LinkedIn Learning offer downloadable resources that can be stored on your computer and accessed offline.

Besides offering exercise files with videos, 365 Data Science provides plenty of different resources that meet students’ needs, including the following:

Course Notes: free pdf text files that present essential concepts from a given course.

Templates: code-ready templates that accelerate your workflow and save you time from browsing on the internet.

Infographics: pdf files with colorful and impressive visuals that help beginners present the bigger picture of the complex world of data science.

LinkedIn Learning, on the other hand, provides only exercise files that might not meet specific students’ learning needs.

Real Environment Practice

Both LinkedIn Learning and 365 Data Science offer coding in real environments and video instructions on how to set it up.

Although less convenient than in-browser coding—which allows students to transition from video to coding seamlessly—the pros of practicing your skills in a real environment far outweigh the disadvantages. All employers expect you to set up your own program and solve real-world problems. Moreover, this approach teaches you how to solve problems in a real-world environment where coding in the browser isn’t powerful enough for massive datasets.

Practical Case Studies

365 Data Science provides practical case studies where students can practice what they’ve learned in a practical context and highlights the business application of data science in their exercises and case studies. For example, 365 Data Science’s Probability Course features real-world data from Hamilton College, requiring students to apply the Bayesian Law to determine whether the college successfully diversifies its student population.

LinkedIn Learning also features practical case studies. For example, their Python Data Analyses course requires students to analyze the U.S. Social Security Baby Name dataset with the pandas library in Python to find the most common unisex names.

Unfortunately, LinkedIn Learning’s Data Science and Data Analyst paths offer far fewer practical case studies and exercises than 365 Data Science’s curriculum.

Exams

Exams are perfect for students to test their theoretical and practical knowledge accumulated after a particular section or course. Passed examinations indicate to future employers that you have the necessary expertise to do your job well.

365 Data Science heavily focuses on examinations, offering three different types of exams.

Practice Exams are optional and test your knowledge of a particular section/chapter of the course. They’re an excellent way to prepare for the course exams and help you identify potentially critical areas of improvement.

Course Exams are comprehensive (timed) exams that test your level of expertise gained at the end of each course topic.

Career Track Exams are comprehensive (timed) exams that test your knowledge gained from all the courses in each career track and are only accessible once you’ve passed all the course exams in the track.

LinkedIn Learning, conversely, does not offer comprehensive exams on most of their courses, nor do they provide any equivalent of a career track exam—only optional practice quizzes that are the equivalent of practice exams.

Certificates

Perhaps, one of the most significant value propositions an online platform can offer to its students—besides quality content—are industry-recognized certificates. They signal to employers that candidates have the necessary technical skills and character traits for the job.

Therefore, 365 Data Science replaced their Certificates of Completion—which required students to view the entire course content—with Certificates of Achievements, which requires students to successfully pass their course exam with a minimum grade of 60%.

Career Track Certificates are granted to students who have successfully passed all their course exams in the track and the career track exam itself.

This rigorous approach to earning a certificate gives them their job-market value and prepares students for the real-life problems they’re expected to solve.

The majority of LinkedIn Learning Certificates are Certificate of Completion for most of their courses—the equivalent of a Certificate of Completion, which is granted upon completing the video content.

LinkedIn Learning does not provide any equivalent to the Career Track Certificates. Some of their courses offer a CPE-accredited certificate where students must pass a course exam with a 70% or above. But it’s essential to note that completing the learning paths for data science, data analytics, and business analytics provides academic credits.

Q&A support

Studying online such a technical field as data science can be challenging and confusing. Therefore, providing a Q&A section where you can turn to instructors and peers whenever you get stuck on a concept or exercise is essential.

Both 365 Data Science and LinkedIn Learning provide a Q&A Hub to their students. But 365 students get help from their peers and actively participating instructors in the hub, making it the better alternative.

Student Ratings

Both LinkedIn Learning and 365 Data Science have positive reviews on G2. But LinkedIn Learning has significantly more reviews, with a 4.4/5 rating from 565 reviews, while 365 Data Science boasts 4.8/5 from four reviews.

Trustpilot gives LinkedIn Learning a surprising 2.0/5 from 50 reviews, with some customers complaining about its cancellation and money refund policy.

365 Data Science features an impressive 4.9/5 from 669 reviews, with positive reviews on its course structure and beginner friendliness.

Pros and Cons

LinkedIn Learning
Pros:
  • Consistent, high-quality video production
  • Extensive library of courses on different topics
  • Academic Credits and CPE Accreditation
Cons:
  • Lack of graded exams
  • Some outdated courses
  • No instructor feedback/help
  • Lack of Certificates of Achievement
365 Data Science
Pros:
  • Industry-recognized certificates
  • Beginner-friendly
  • Comprehensive career tracks
  • Plenty of learning resources
Cons:
  • Lack of Portfolio Projects
  • Less business-related courses

Pricing

365 Data Science offers free content to students who wish to try the platforms’ teaching styles before deciding on a plan. The platform permits students access to the first chapters of each course and the resources and practice exams without a credit card—allowing them to upgrade.

By now, you are probably wondering how much LinkedIn Learning costs? In terms of LinkedIn Learning’s pricing, they have a premium plan, but people can access a free trial for one month. When the trial expires, students’ credit cards are charged.

LinkedIn Learning Pricing
  • Free subscription: One-month free access to all courses
  • Premium: $19.00/month billed annually or $21.00/month
365 Data Science Pricing
  • Free subscription: Access to the first few lessons of all courses
  • Premium: $36/month or $29/month billed annually

365 Data Science vs LinkedIn Learning: Which one to choose?

Choosing between the two platforms ultimately comes down to your needs and preferences. Is LinkedIn Learning worth it? First, it might be a good option due to its vast library of thousands of courses if you think you need to learn a particular skill related to data science or business. And if you wish to go down the academic path, then some of the academically accredited courses might be best for you.

If you’re a beginner looking for an alternative to LinkedIn Learning, 365 Data Science might be the better platform for your career goals. 365 will help you build job-ready skills that will land you an entry-level position in data science. Each career track is built systematically, training your skills from beginner to advanced, with a heavy emphasis on practical skills. And as a testament to your expertise, you can earn an industry-recognized certificate at the end of the career track, providing you with a competitive advantage over other job candidates.