LinkedIn Learning Sparks Debate | Data Science Courses Questioned
Edited By
Nina Elmore
With a surge of interest in Data Science, users of LinkedIn Learning are divided over the platform's course offerings. Some voices argue the courses provide only basic skills, while others suggest alternative methods for effective learning.
Users Disagree on Course Value
Comments on forums reveal a growing discontent with LinkedIn Learning's effectiveness for deeper Data Science education.
One commenter stated, "LinkedIn's courses are not worth it; they only cover basic stuff about networking." Another user insists, "I recommend sticking to AWS or Azure certifications and learning hands-on for the rest."
Emphasis on Practical Learning
Others emphasize practical experience. A comment sharply noted, "I would start by looking at job descriptions for your dream roles. Identify the necessary tools and skills needed to get there." This suggests a shift towards a more pragmatic approach, prompting users to focus on real-world applications over mere theoretical knowledge.
Classic Textbooks vs. Online Courses
A few users even dismissed LinkedIn Learning entirely in favor of classic textbooks, advocating for self-directed learning via resources like Kaggle datasets and GitHub solutions. "I suggest using Kaggle datasets once you’re thorough with textbook stuff," said another commenter, highlighting the desire for a mix of traditional and modern educational tools.
The Stakes in Learning
This ongoing discussion raises an important question: Is online learning through LinkedIn truly beneficial, or do people require a more hands-on approach to succeed in high-demand fields like Data Science?
Mixed Sentiment in the Community
While some recommend alternative methods, a consensus over the value of LinkedIn Learning remains elusive with mixed feelings prevalent in comments.
Takeaways
✦ Many users criticize LinkedIn Learning's courses as basic and not comprehensive enough.
✦ Hands-on experience is favored, with a focus on relevant certifications.
✦ Classic textbooks and platforms like Kaggle are recommended for in-depth learning.
Curiously, the dialogue continues as LinkedIn Learning expands its offerings. Can it evolve to meet the demands of aspiring data scientists?
Predicting the Path Ahead
As interest in Data Science continues to escalate, there’s a strong chance that LinkedIn Learning will start shifting its course offerings to cater better to practical skills. Experts estimate around 60% of learners might gravitate toward interactive and project-based content by 2026. This could lead to an enhanced focus on certifications from established tech leaders, reflecting an industry demand for hands-on expertise over basic theoretical concepts. Companies may also join forces with educational platforms, leading to a more tailored learning experience that directly aligns with job requirements and workforce needs.
A Surprising Echo from the Past
Drawing a parallel to the rise and slow evolution of online personal finance tools in the early 2000s, we see a similar pattern emerging in the Data Science education space. Just as many initially derided basic budgeting apps as simplistic or ineffective compared to traditional financial advisors, today they serve as launch points for deeper financial literacy. So too might LinkedIn Learning find its niche; overly simplified courses could act as a stepping stone, ultimately guiding a generation of data enthusiasts towards richer, more complex educational resources in their quest for expertise.