What I Learned in My Online BSc Computer Science Degree (University of London)

Summary

In this video, the speaker shares their experience of balancing a full-time data science career while pursuing a second bachelor's in computer science from the University of London. Offering insights into the online program's structure, workload, and time commitment, they discuss key modules covering programming, algorithms, AI, and cybersecurity. The speaker reflects on the value of learning computer science for data scientists, highlighting the flexibility and hands-on learning approach of the degree. They also share practical advice for managing study and work commitments, and whether this path is worth it for professionals in tech.

Takeaways

• 😀 Taking a second bachelor's degree in Computer Science while working full-time as a data science consultant was a challenging yet rewarding decision.
• 😀 The program is offered online by the University of London through Coursera, making it flexible for students who are working full-time or have other commitments.
• 😀 The program consists of 22 modules and a final project, taking a minimum of three years to complete if pursued at maximum pace (four modules per term).
• 😀 The degree is designed for flexibility, allowing students to take fewer modules per semester or even take breaks when needed.
• 😀 The program is significantly more affordable than traditional computer science degrees, with costs ranging from £11,000 to £17,000, depending on location.
• 😀 Key foundational subjects covered in the first year include programming, computational mathematics, discrete math, web development, and algorithms.
• 😀 The second year dives deeper into object-oriented programming, software design, databases, network security, and computer security.
• 😀 The third year includes modules on more advanced topics such as graphics programming, intelligent signal processing, and machine learning/AI.
• 😀 Specializations available in the final year include machine learning, AI, data science, web development, and user experience, allowing students to tailor their education.
• 😀 Practical coding skills and hands-on projects are emphasized, but there is some redundancy between modules that could be streamlined for better efficiency.
• 😀 While the degree may not carry as much weight as a Master's in computer science for job applications, it is ideal for those transitioning into data science or AI fields, providing broader, transferable skills.

Q & A

Why did the speaker decide to pursue a second bachelor's in computer science while working full-time as a data science consultant?

The speaker felt there was a lot they didn't know about computer science, and as someone who enjoys creating things, they wanted to expand their knowledge and skills. They also believed that if they didn't take the opportunity to study, they might never have the chance again.

What is the structure and duration of the University of London’s online computer science program?

The program consists of 22 modules and one final project. It is designed to take three to six years to complete, depending on the student's pace. The maximum pace involves completing four modules per term, allowing completion in three years.

How flexible is the program for working professionals?

The program is quite flexible. Students can choose to study online, and they can also take fewer modules per semester or take breaks. The two hard deadlines per module (midterm and final exam) allow students to manage their time around work and personal commitments.

What is the financial cost of the program, and is there any financial support available?

The cost of the program ranges from £11,000 to £17,000 depending on the student's location. It's a fraction of the cost of traditional computer science degrees, but still significant. The speaker recommends asking employers for sponsorship or support, as they did.

What were the key foundational modules covered in the first year of the program?

In the first year, the speaker learned modules such as Introduction to Programming, Computational Mathematics, Discrete Math, How Computers Work, Fundamentals of Computer Science, Web Development, and Algorithms and Data Structures.

What was the most useful skill the speaker gained from the first year of the program?

The speaker gained confidence in their coding skills and learned how to structure clean, robust, and well-organized code. They also became comfortable with reading API documentation, which was a valuable skill for solving programming assignments.

What did the speaker learn in the second year that was most impactful for their work in data science?

In the second year, the speaker focused on Object-Oriented Programming, Software Design, Databases, Networks, and Computer Security. These topics helped them think more critically about how to organize and scale code, which is crucial for data science projects.

How did the third-year modules contribute to the speaker’s learning experience?

In the third year, the speaker explored more advanced topics such as AI, machine learning, and intelligent signal processing. They also engaged in hands-on assignments related to speech recognition, computer vision, and creating generative art. These modules deepened their technical skills and sparked creative ideas for future projects.

Why did the speaker choose machine learning and AI as their specialization in the program?

The speaker chose this specialization because machine learning and AI are integral to data science. They felt that understanding the principles of these fields in depth would benefit them as a data scientist, especially in areas like natural language processing and intelligent signal processing.

How did the speaker feel about the quality of the modules and the teaching in the program?

The speaker found the teaching to be good overall, with lecturers being passionate and knowledgeable. However, they did mention that some modules had redundant content, like data structures being covered in multiple modules, and felt that certain topics, such as web development, were too basic.

What are the pros and cons of pursuing a bachelor's in computer science instead of a master's for someone working in data science?

The speaker emphasized that a master's degree might carry more weight for job applications, but a bachelor's offers more time to learn at a slower pace and the opportunity to build a diverse portfolio through hands-on projects. A bachelor's degree may be a better fit for someone already working in the field and looking to deepen their knowledge rather than quickly advance in academia.

Is pursuing a full degree in computer science necessary for data scientists, according to the speaker?

No, the speaker believes that for data scientists, a full computer science degree may not be necessary. Instead, they recommend learning key topics like data structures, algorithms, software design, object-oriented programming, and discrete math. These foundational skills can be self-taught or learned through shorter courses if a full degree feels like an overcommitment.