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In today's digital age, Michael Hakvoort's quote, “If you don't pay for the product, then you are the product,” has never been more relevant. While we often think of this in relation to social media platforms like Facebook, it also applies to seemingly harmless free resources like YouTube courses.
Sure, the platform earns revenue through ads, but what about the time, energy, and motivation you invest? As data becomes increasingly valuable, it is essential to carefully evaluate the potential impact of free data science courses on your learning journey.
With so many options available, it can be overwhelming to determine which ones will provide real value. That's why it's crucial to take a step back to consider some critical factors before diving into any free resource. By doing so, you'll ensure you get the most out of your learning experience while avoiding common mistakes associated with free courses.
Free courses often offer a one-size-fits-all curriculum, which may not match your specific learning needs or skill level. They may cover fundamental concepts, but lack the depth necessary for a comprehensive understanding or to address complex real-world problems. Some free courses may have all the ingredients needed to solve real-world data problems, but they lack structure, leaving you confused about where to start.
Learning a programming language alone can be challenging, especially if you don't have a technical background. Data science is a field that requires a hands-on approach. Free courses typically offer limited opportunities for interactive learning, such as live coding sessions, quizzes, projects, or instructor feedback. This passive learning experience can prevent you from applying concepts effectively and you will eventually give up on learning.
The Internet is flooded with free courses, making it difficult to discern the quality and credibility of the content. Some may be outdated or taught by people with limited experience (fake gurus). Investing your time in a course that does not offer accurate or up-to-date information can backfire.
Here is a list of free courses that I think are high quality:
- Introduction to programming with Python by HarvardX
- Statistical learning with R by StanfordOnline
- Data science for beginners by Microsoft
- Databases and SQL by freeCodeCamp
- Machine Learning Zoomcamp By DataTalks.Club
Unlike paid courses, free resources don't come with external accountability measures such as deadlines or grades, making it easy to lose momentum and abandon the course mid-stream. The lack of financial commitment means that students must rely solely on their internal drive and discipline to stay motivated and committed to completing the course. The university is a great example of this. Students think 100 times before leaving university because of the costs involved. Most students complete their bachelor's degree because they took out a student loan and need to pay it back.
Networking is an important part of building a career in data science. Free courses often lack the community aspect found in paid programs, such as peer-to-peer interaction, mentorship, or alumni networks, which are invaluable for career growth and opportunities. There are Slack and Discord groups available, but they are typically community-driven and may be inactive. However, in a paid course there are moderators and community managers who are in charge of facilitating networking among students.
Paid courses often provide career services, such as resume review, certification, job placement assistance, and interview preparation. These services are essential for people transitioning into a data science role, but they are typically not available in free programs. It is essential to have guidance throughout the hiring process and know how to handle technical interview questions.
While not always necessary, certifications can improve your resume and your credibility. Free courses may offer certificates, but they often do not carry the same weight as those from accredited institutions (Harvard/Stanford) or recognized platforms. Employers may not value them as much, which could affect their job prospects. Additionally, certification exams assess key skills essential for working with data in any job. They assess your coding, data management, data analysis, reporting, and presentation skills.
While free data science courses can be a valuable resource for initial learning or upskilling, they have certain limitations. It is important to consider these limitations in relation to your personal goals, learning style, financial situation and career aspirations. To ensure a complete and effective learning experience, you should consider supplementing free resources with other forms of learning or investing in a paid bootcamp.
In the end, the most crucial factor that will help you become a professional data scientist is your dedication and focus on achieving your goals. You won't learn anything if you lack the necessary drive, no matter how much money you spend on the course. So before you dive into the world of data, think ten times if this is the right path for you.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
