Is LinkedIn Learning good for data science?

With the rise of data science as an in-demand career field, many aspiring data scientists are looking for ways to gain the skills needed to break into the industry. LinkedIn Learning has emerged as one of the top online learning platforms for professional development, offering thousands of courses in areas like data science, analytics, programming, and more. But is LinkedIn Learning a worthwhile investment for those interested in pursuing data science? Let’s take a closer look at what LinkedIn Learning offers and whether it provides quality content specifically for data science learning.

Overview of LinkedIn Learning

LinkedIn Learning (formerly known as Lynda) is an e-learning platform that offers video courses taught by industry experts across a variety of professional fields. Acquired by LinkedIn in 2015, LinkedIn Learning integrates directly with LinkedIn profiles to showcase your course history and skills. Some key features of LinkedIn Learning include:

• Over 16,000 courses in business, technology, and creative skills
• New courses added weekly in various languages
• Video-based lectures for flexible on-demand learning
• Downloadable exercise files to supplement video lessons
• Course grouping into customized “Learning Paths” focused on specific skills
• Integration with LinkedIn profile and ability to showcase skills/certifications
• Mobile app available for learning on the go

LinkedIn Learning uses an all-you-can-learn subscription model, with pricing starting at around $30 per month for individual accounts. Many companies also offer employees free access to LinkedIn Learning as a professional development benefit.

LinkedIn Learning’s Data Science Offerings

So what does LinkedIn Learning offer specifically for data science skill building? Let’s take a look at some of the key course categories available:

Core Data Science Skills

For understanding the fundamentals of data science, LinkedIn Learning has various courses that cover topics like:

• Data science workflows and methodology
• Working with data structures and data cleaning
• Exploratory data analysis techniques
• Statistical analysis and modeling
• Machine learning algorithms
• Data visualization best practices
• Productizing models into applications

These courses help build a solid baseline knowledge of the core skills needed to extract insights from data.

Programming Languages

Some of the most popular courses focus on programming languages commonly used by data scientists:

• Python
• R
• SQL
• Scala
• Java

The platform covers everything from introductory syntax and programming to more advanced application and package usage within data science workflows.

Data Science Tools & Software

In addition to programming languages, LinkedIn Learning offers training on key software, frameworks, and cloud platforms used in the field:

• Jupyter Notebooks
• Pandas, NumPy, and other Python libraries
• RStudio and R packages like ggplot2
• Apache Hadoop and Spark
• TensorFlow and Keras for machine learning
• Tableau and data visualization tools
• AWS, Google Cloud, Azure cloud platforms

These courses help you master the variety of tools that are ubiquitous in the day-to-day work of data professionals.

Specialized Data Science Topics

For those looking to go beyond fundamentals, LinkedIn Learning contains more specialized courses like:

• Deep learning and neural networks
• Natural language processing
• Recommendation systems
• Time series analysis and forecasting
• Customer analytics
• A/B testing
• Data engineering and pipelines

These courses allow you to level up or focus your skills in specific data science domains or techniques.

Mathematics & Statistics

Given the highly statistical nature of data science, LinkedIn Learning has a wide array of courses on relevant mathematical and statistical concepts like:

• Calculus
• Linear algebra
• Regression analysis
• Bayesian statistics
• Mathematics for machine learning
• Statistical modeling
• Algorithm design

For many, brushing up on these foundational math skills can provide the necessary grounding for mastering data science techniques.

Quality of LinkedIn Learning Data Science Content

With this extensive course library, LinkedIn Learning certainly covers the breadth of data science topic areas. But how does it fare in terms of depth and quality of content?

Here are some of the key advantages and disadvantages of LinkedIn Learning for data science:

Pros

• Industry expert instructors: Courses are taught by credible instructors who work professionally in data science, analytics, programming, and related fields.
• Hands-on exercises: Many courses provide starter code, sample data sets, and other files to actively work through concepts.
• Relevant examples: Concepts are often taught through real-world examples and case studies applicable to the workplace.
• Concise format: Most classes are structured as a series of short video lessons focused on a single concept.
• Certificates of completion: Finishing courses earns you a certificate that can be showcased on your LinkedIn profile.

Cons

• Variable depth: Some courses provide a quicker overview, while others offer more comprehensive instruction.
• Limited assessments: There are typically no graded assignments or projects to test your proficiency.
• No peer interaction: Learning is self-directed without any student-instructor interaction.
• Moving parts: You must leverage outside tools like IDEs, cloud platforms, etc. to work with sample code/data.

So in summary, LinkedIn Learning provides very solid high-level training in data science concepts and tools, though some courses may lack the rigor and depth of a true academic program. Supplementing with hands-on projects and peer feedback is recommended.

How to Get the Most from LinkedIn Learning

Here are some tips to help maximize your learning with LinkedIn if pursuing data science skills:

• Take curated Learning Paths that structure courses into a comprehensive program on a focused skill.
• Apply concepts by doing outside hands-on projects using tools like Jupyter, RStudio, and GitHub.
• Leverage course exercise files and scripts provided by instructors.
• Watch at an accelerated pace for lighter topics, but replay at normal speed for complex coding.
• Test skills by competing in data science challenges, hackathons, or analyzing open datasets.
• Cross-reference more in-depth textbooks or online documentation when needed.
• Repeat key courses annually as a refresher of core concepts and best practices.

The right learning mindset and application of skills can help boost the impact of LinkedIn’s training content.

How LinkedIn Learning Compares to Other Options

LinkedIn Learning is certainly not the only game in town when it comes to e-learning platforms for data science. How does it compare to some other popular options?

LinkedIn Learning vs. Udemy

Udemy has a wider range of course quality but enables you to pick and choose only the highly-rated ones. LinkedIn Learning offers a more consistent experience backed by brand credibility.

LinkedIn Learning vs. Coursera

Coursera provides a more academic experience with verified credentials, while LinkedIn Learning focuses on professional training at your own pace.

LinkedIn Learning vs. DataCamp

DataCamp provides a much more immersive, coding-focused data science program, while LinkedIn Learning covers data science as one area out of many professional skills.

As you can see, LinkedIn Learning occupies an interesting middle ground between the free-for-all of Udemy, academic nature of Coursera, and the intensive specialization of DataCamp. Depending on your learning needs and budget, it can represent a solid standalone option or complement other platforms.

Usefulness for Data Science Career Development

Beyond just gaining data science skills, how helpful is LinkedIn Learning specifically for advancing your career as a data professional?

Here are some of the key benefits:

• Demonstrate skills: Courses completed can be showcased on your LinkedIn profile and act as credibly validated skills.
• Build a narrative: The platform gives you a body of work that demonstrates your commitment to self-directed learning.
• Continue lifelong learning: You can evolve your skills over time as new courses are added to keep pace with the field’s progression.
• Transition careers: LinkedIn Learning provides a structured way to reskill into data science from other backgrounds.
• Showcase certificates: Certificates can be presented on resumes and portfolios as verified credentials.

The tight integration with LinkedIn can provide good supporting evidence of your data science chops in a job search. But hands-on experience with real data likely matters more.

Some things to keep in mind if relying on LinkedIn Learning for career advancement:

• Completing courses is likely not enough on its own to get a job without applied experience.
• Basic courses may only cover the surface of highly complex topics.
• Skills can become dated compared to the rapid evolution of languages and tools.
• You still need to pass technical interviews testing your hands-on competency.
• A traditional master’s degree or bootcamp may provide deeper knowledge and career prep.

So LinkedIn Learning is best leveraged as a piece of the puzzle in career development alongside other practical training and resume-building opportunities.

Conclusion

To wrap up, here are some key takeaways on whether LinkedIn Learning offers worthwhile training content for aspiring data scientists:

• Provides broad coverage of key data science topics and tools through thousands of courses.
• Video-based learning from industry experts offers solid high-level overviews.
• Not as comprehensive as a full academic program or intensive bootcamp.
• Quality and depth can vary somewhat between courses.
• Hands-on projects and peer learning are needed to supplement video content.
• Can be a beneficial training asset as part of a larger career development strategy.

For many professionals, LinkedIn Learning offers an affordable and flexible way to continue adding data science skills to their portfolio over time. But some additional hands-on training will likely be necessary to land that first data scientist role.