Are Data Classes Worth It?

Before comparing different data classes, you’ll want to consider whether a data class is really worth your time and investment. Advanced data classes can take weeks of full-time training and cost thousands of dollars, so before taking one, look at all the options. 

This article aims to help you consider the many factors determining whether a data class is worth it. Simply put, data classes are worth your time and money if you're committed to starting a career in data science, data analysis, or a related field that requires professional data skills. Learning these skills in a data class is the best way to develop the required professional tools and acquire knowledge in a structured environment.

Consider the Costs

Enrolling in a data course can be expensive, particularly if you want to do it at a professional level. These courses can cost several thousand dollars, and while the training is worth it if you're serious about committing to a career in data analysis or data science, cost will be a big factor in deciding whether to take the plunge. There are less expensive options, including on-demand classes, free tutorials, and short introductory classes, but all have their pros and cons. Since so many options are available, you’ll want to judge what is and isn’t worth the cost when comparing different offerings.

Advantages of a Data Class

While classes can be expensive and time-consuming, there are many reasons why students prefer guided, live instruction to learn data skills.

General Benefits

All live training courses give students the benefit of working with a real instructor who can guide them through difficulties step-by-step and personalize their instruction and feedback to their students' needs. Students often find that enrolling in a class helps them better understand the material and remain focused on their training. Regardless of the class you choose, having access to a live instructor and a guided schedule will help you retain information, overcome difficulties in the learning process, and become better equipped to study advanced concepts.

Access to Software and Datasets

One of the immediate hurdles to learning data skills is getting the necessary software and tools to start experimenting with the process. While getting the tools and datasets is far easier than in the past, you still need access to data analysis software, like Python, R, or SQL, which can require significant investment in time and resources to learn. Enrolling in a class is likely to address both these problems, as limited licenses for software may be available for students, and the instructor will provide datasets needed to start practicing analysis exercises and learning hands-on skills through practical tasks. While this is not a major issue, getting access to the necessary equipment can often be the most significant stumbling block for new students looking to learn a skill like data analysis.

Learning Theory and Practice

Data analysis is fundamental in many fields, and understanding the theory behind data practices and decisions is crucial. While you need to learn the tools of the trade to analyze data, without knowing the theories behind data practices, you won’t be able to do more than simple calculations and basic visualizations. Data classes tend to be taught by experienced and skilled data scientists who can provide students with an understanding of why specific analyses and methods are used. This makes taking a data class an excellent place to pick up theoretical knowledge without enrolling in a complete data science program.

Personalized Feedback

Even a slight mistake can make a difference in how the analysis looks and feels when undertaking a data project. This can be almost impossible to parse on your own, especially for new analysts without a data science background. Enrolling in a data class can provide students with personalized feedback from experienced instructors. You’ll have a set of eyes on your work from someone who understands the subtle cues and decisions that make a successful data project. This feedback can help you avoid making common and avoidable mistakes early on in your data career.

Collaboration and Project-Based Training

Except for small projects, data analysis is typically a collaborative process that takes multiple professionals to complete. This includes projects with various analysts working together and the collaboration that analysts have with stakeholders, project managers, and other team members. Enrolling in a data class (particularly one taught at a specialized training program) will introduce you to the complex nature of the collaborative process of data analysis, as you’ll work with your classmates on real-world projects that mirror the kinds of work you’ll need to master in a professional context.

Building a Portfolio

If you're looking to work in data analysis professionally, enrolling in a class will provide you with job support and practical career training, and the exercises you complete during your training can become a part of your professional portfolio. The most important part of your job materials as a prospective data analyst will be a collection of your work that shows potential employers that you’re an accomplished and trained analyst, which can be hard to produce if you’re self-taught. In a classroom setting, you’ll be able to take the work that you have done and further refine it into a professional-quality portfolio that will show off the specific kind of analyst that you are, helping companies better understand if you’re the right fit for their project.

Considerations When Looking at Data Classes

Data skills classes may not be suitable for everyone. Beyond the general considerations, such as cost, time commitment, and personal learning preferences, you will want to account for the specific objectives and career goals that drive your interest in enrolling in a live training course. Additionally, it's important to assess the class content, instructor expertise, and the support resources available to ensure the class aligns with your educational needs and professional aspirations.

Cost

Data classes can be expensive, so it's important to evaluate whether the investment aligns with your budget and potential return on investment.

Time Commitment

Assess whether you have the time to commit to the course, especially if it requires several weeks of full-time training.

Course Content and Practical Skills

When evaluating course content, ensuring that the class covers the specific skills and knowledge you need to achieve your goals is essential. Look for classes that include instruction in relevant programming languages like Python or R, data analysis techniques including statistical methods and machine learning, and essential software tools such as SQL databases, data visualization platforms, and data manipulation libraries.

Instructor Expertise

Check the qualifications and experience of the instructors to ensure you will be learning from knowledgeable and experienced professionals.

Learning Format

Determine if the class format (live, online, self-paced) suits your learning style and schedule.

Support Resources

Look for classes that offer additional support, such as access to software, datasets, mentorship, and career services.

Career Goals

Consider how the class will help you achieve your career goals, whether it’s transitioning to a new role, advancing in your current position, or acquiring specific skills for a project.

Student Reviews and Testimonials

Read reviews and testimonials from former students to gauge the effectiveness and quality of the class.

Is It Worth Enrolling in a Data Class?

Enrolling in a data class is worth it if you’re committed to developing your skills for a career in data science or analysis, as these courses provide essential technical knowledge and practical experience. However, if you prefer self-paced learning or don’t foresee using data skills in your career, alternative learning resources might be more suitable.

Who Will Find It Worthwhile

Enrolling in a data class is particularly worthwhile for those committed to pursuing a career in data science, data analysis, or related fields. Professionals looking to transition into data-driven roles or those aiming to enhance their existing skills will benefit greatly from structured learning and the comprehensive curriculum offered by these classes. Data classes provide the technical expertise, hands-on experience, and industry-relevant knowledge necessary to succeed in a competitive job market, making them an excellent investment for people who are dedicated to advancing their careers in data.

Who Might Find It Worthwhile

Those with a casual interest in data or are unsure about committing to a full-fledged career in data science may find data classes beneficial. These classes can provide a solid foundation in data skills, which are increasingly valuable in various professions. For instance, business professionals, marketers, and researchers can gain significant advantages by understanding data analysis principles, enabling them to make data-driven decisions in their respective fields. However, they should carefully consider the course content and time commitment to ensure it aligns with their broader professional goals.

Who May Not Find It Worthwhile

On the other hand, those who are not inclined toward analytical thinking or those who don’t foresee using data skills in their current or future roles may not find enrolling in a data class worthwhile. Additionally, those who prefer self-paced learning through free or low-cost resources might not see the value in investing in a structured and often expensive data class. Suppose the primary motivation is to acquire a basic understanding of data concepts without a deep dive into the technical aspects. In that case, alternative learning methods may be more suitable and cost-effective.