As a BSc Computer Science program graduate, you can further boost your career prospects by applying for additional courses in this field. Doing so will further develop your specialization and increase your chances of landing a job you love and are qualified to perform.
When looking for a course after BSc Computer Science, consider your career goals, budget, learning needs, and curriculum. This article covers the best courses after BSc Computer Science to make the most out of your education in computer science.
MSc Computer Science
A Master of Science degree in Computer Science is the logical next step for students who wish to continue their academic education in the computer science field. Numerous programs are available depending on your preferred specialization, providing plenty of career opportunities.
Some colleges and institutions require you to have a computer science bachelor before you can sign up for a master’s program. It’s best to check the requirements on the institution’s official website.
Some flexible programs like OPIT’s accredited MSc in Applied Data Science and AI don’t require any prerequisites in the field. The program is also self-paced and fully remote and consists of three terms – two terms of courses and the final term for the project or thesis.
A Master of Science in Computer Science may include the following specializations:
- Robotics
- Graphics
- Computing Systems
- Human-Centered Computing
- Artificial Intelligence
- Machine Learning
- Modeling and Simulations
- Social Computing
- Cybersecurity
- Software Engineering
- Data Science
- Information Systems
These specializations may or may not be available at the university or college where you completed your previous education. If you’re determined to get an MSc in Computer Science and have a particular specialization in mind, do extensive research online to find the best program that matches your needs, budget, and skills. An affordable and valuable education upgrade may just be around the corner.
As you can see from above, you’ll find a Master of Science specialization in just about any computer science field. The same goes for career opportunities following an MSc in Computer Science.
Career Opportunities
- Computer Research Analyst
- Computer Network Architect
- Software Developer
- Database Administrator
- Information Security Manager
- Software Development Engineer
- Computer and Information Systems Manager
- Computer Systems Analyst
- Web Developer
- Mobile App Developer
If you’re ever in doubt about pursuing a Master of Science in Computer Science, remember that the average salary for individuals with this degree is $109,000 per year, according to PayScale.
Master of Computer Applications (MCA)
If you’re wondering “what to do after BSc Computer Science,” another popular path is a Master of Computer Applications (MCA). Experts with this degree can create computer applications, test new programs, offer instructions for software users, and more. With a finished MCA, you’re looking at a career that focuses on the practical aspects of software development.
The master’s in this field takes two to three years to complete and is available in colleges worldwide. A math background or at least having had this subject in high school is often the main requirement to enroll in the program. You may also need to undergo a test and an interview.
The MCA programs usually cover computational theory, in-depth algorithm studying and practice, network management, databases, web design, web development, computer networks, and more. The focus of the classes is hands-on software development, so you need to have strong skills in programming languages.
But what are your career opportunities with an MCA?
Career Opportunities
- Computer Systems Analyst
- Computer Programmer
- Software Developer
Although there are fewer career opportunities for MCA takers, the salary of individuals with this degree goes up to $133,000 annually, making an MCA an excellent investment.
MBA in Information Technology
A Master of Business Administration in Information Technology is an advantageous education program. It trains you to become a leader in the IT industry. Bureau of Labor Statistics data shows that computer and information system managers earn around $151,000 annually or about $76 per hour. These are some of the highest salary prospects of all the after BSc in Computer Science programs.
Better still, all modern-day organizations need a professional in this field to ensure operations run smoothly. An important part of an information technologist’s job is to examine the future prospects of the company’s technology needs, making it a highly rewarding career.
The MBA in Information Technology program primarily focuses on training skilled professionals with high technical and business know-how. Numerous online as well as traditional programs and universities offer this specialization, as it’s one of the most in-demand degrees out there.
Some courses you can expect to find in an MBA in Information Technology program include:
- Information Security
- Database Management
- Business Data Analytics
- Technology Management
- Corporate Financial Strategy
- Marketing Strategies
- Financial Management
- Decision Making
- Project Management
- Human Capital Management
Most schools look for either GMAT or GRE scores as a requirement to enter this program. Also, the average duration of the program is one year. The great part is that you can find self-paced programs you can take according to your schedule.
Career Opportunities
- IT Manager
- IT Director
- Computer and Information Research Scientist
- IT Business Relationship Manager
- Chief Technology Officer
- Data Analyst
PG Diploma in Data Science
A Postgraduate Diploma in Data Science is usually a two-year full-time program that combines economics, science, and information technology. With this specialization, you’re qualified for many roles in the industry that deal with data. You can use the knowledge obtained in this program to contribute to the optimization of most processes in businesses, software, and institutions.
Numerous online boot camps are available and sponsored by major corporations like IBM.
In the PG Diploma in Data Science study path, you can expect to work with the following:
- R
- Python
- NLP notions
- Machine learning
- Tableau and other data visualization methods
When choosing a PG in Computer Science, go for programs with plenty of projects involving hands-on practice. If you have a love and passion for numbers, new knowledge, and analytics, a PG in Data Science is the right call. Your future is bright in terms of career opportunities too.
Career Opportunities
- Business Analyst
- Big Data Scientist
- Senior Data Scientist
- Data Architect
- Data Administrator
- Business Intelligence Manager
- Research Analyst
- Data Mining Engineer
- Statistician
Certification Courses
Nowadays, people love taking online courses. If you’re active on LinkedIn, you can see how the LinkedIn Learning platform with free courses has taken business social media by storm. Although LinkedIn offers solid programs, most of them aren’t accredited.
You can complete legit certification courses following your BSc Computer Science to boost your career. Certifications prove that you’re skilled in your area of specialization and that you have passed a standardized examination to demonstrate your capabilities.
While preparing for the certification exam, you also have the opportunity to learn new things. Many employers don’t care whether you have a certificate for specific skills, but the computer science world is different.
Given the multitude of highly specialized themes, employers want to be sure you’re suitable for the position you are applying for. Certifications are especially beneficial if you have no prior work experience on your CV.
Finally, by getting a professional certification, you also can increase your future salary prospects. An additional document on your CV validating your skills gives you an edge over other applicants.
Certifications for computer science experts include:
- CISCO – If your area of interest is networking, you can get a professional certificate in various fields like data centers, network design, DevNet, and others.
- CompTIA – This association offers professional certifications related to computer components, software, smartphones, etc. They also have a certification program for security and networking.
- EC-Council – If you’re leaning towards e-commerce and digital businesses, the EC-Council certification can help. They have programs for ethical hacking, computer hacking, and more.
- Microsoft – This corporation has plenty of certification programs to showcase your proficiency in Azure.
- AWS – With arguably the highest number of available certifications, Amazon Web Services is one of the most popular providers of professional certificates.
- ISACA – If you want to advance in the information system and security sector, ISACA certifications are the way to go.
- VMware – This certification is for vSphere V6 specialists in data center visualization.
You can take computer science in just about any field imaginable. Therefore, you unlock numerous career opportunities.
Career Opportunities
- Software Developer
- Web Developer
- Computer Programmer
- Network Administrator
- Software Engineer
- Systems Manager
- Computer Hardware Engineer
- Information Security Analyst
After BSc Computer Science Which Course Is Better?
When you choose courses after BSc Computer Science, it’s important to consider your career goals, skills, and interests. Otherwise, you may feel dissatisfied and unfulfilled while studying and looking for jobs.
The courses featured in this article have high growth potential and are promising in terms of success. With enough effort, stimulation, and support, you can make your next computer science course your best life decision.
To choose the course you are truly interested in, dedicate enough time to research and consult with industry professionals, as they will surely provide valuable insights and advice to help you make the right choice.
Related posts
Source:
- Authority Magazine Medium, Published on September 15th, 2024.
Gaining hands-on experience through projects, internships, and collaborations is vital for understanding how to apply AI in various industries and domains. Use Kaggle or get a free cloud account and start experimenting. You will have projects to discuss at your next interviews.
By David Leichner, CMO at Cybellum
14 min read
Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?
In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.
As part of this series, we had the pleasure of interviewing Zorina Alliata.
Zorina Alliata is an expert in AI, with over 20 years of experience in tech, and over 10 years in AI itself. As an educator, Zorina Alliata is passionate about learning, access to education and about creating the career you want. She implores us to learn more about ethics in AI, and not to fear AI, but to embrace it.
Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?
I was born in Romania, and grew up during communism, a very dark period in our history. I was a curious child and my parents, both teachers, encouraged me to learn new things all the time. Unfortunately, in communism, there was not a lot to do for a kid who wanted to learn: there was no TV, very few books and only ones that were approved by the state, and generally very few activities outside of school. Being an “intellectual” was a bad thing in the eyes of the government. They preferred people who did not read or think too much. I found great relief in writing, I have been writing stories and poetry since I was about ten years old. I was published with my first poem at 16 years old, in a national literature magazine.
Can you share with us the ‘backstory’ of how you decided to pursue a career path in AI?
I studied Computer Science at university. By then, communism had fallen and we actually had received brand new PCs at the university, and learned several programming languages. The last year, the fifth year of study, was equivalent with a Master’s degree, and was spent preparing your thesis. That’s when I learned about neural networks. We had a tiny, 5-node neural network and we spent the year trying to teach it to recognize the written letter “A”.
We had only a few computers in the lab running Windows NT, so really the technology was not there for such an ambitious project. We did not achieve a lot that year, but I was fascinated by the idea of a neural network learning by itself, without any programming. When I graduated, there were no jobs in AI at all, it was what we now call “the AI winter”. So I went and worked as a programmer, then moved into management and project management. You can imagine my happiness when, about ten years ago, AI came back to life in the form of Machine Learning (ML).
I immediately went and took every class possible to learn about it. I spent that Christmas holiday coding. The paradigm had changed from when I was in college, when we were trying to replicate the entire human brain. ML was focused on solving one specific problem, optimizing one specific output, and that’s where businesses everywhere saw a benefit. I then joined a Data Science team at GEICO, moved to Capital One as a Delivery lead for their Center for Machine Learning, and then went to Amazon in their AI/ML team.
Can you tell our readers about the most interesting projects you are working on now?
While I can’t discuss work projects due to confidentiality, there are some things I can mention! In the last five years, I worked with global companies to establish an AI strategy and to introduce AI and ML in their organizations. Some of my customers included large farming associations, who used ML to predict when to plant their crops for optimal results; water management companies who used ML for predictive maintenance to maintain their underground pipes; construction companies that used AI for visual inspections of their buildings, and to identify any possible defects and hospitals who used Digital Twins technology to improve patient outcomes and health. It is amazing to see how much AI and ML are already part of our everyday lives, and to recognize some of it in the mundane around us.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?
When you are young, there are so many people who step up and help you along the way. I have had great luck with several professors who have encouraged me in school, and an uncle who worked in computers who would take me to his office and let me play around with his machines. I now try to give back and mentor several young people, especially women who are trying to get into the field. I volunteer with AnitaB and Zonta, as well as taking on mentees where I work.
As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?
I think one major challenge in AI is the speed of change. I remember after spending my Christmas holiday learning and coding in R, when I joined the Data Science team at GEICO, I realized the world had moved on and everyone was now coding in Python. So, I had to learn Python very fast, in order to understand what was going on.
It’s the same with research — I try to work on one subject, and four new papers are published every week that move the goal posts. It is very challenging to keep up, but you just have to adapt to continuously learn and let go of what becomes obsolete.
Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?
1. Creativity
Generative AI brought us the ability to create amazing images based on simple text descriptions. Entire videos are now possible, and soon, maybe entire movies. I have been working in AI for several years and I never thought creative jobs will be the first to be achieved by AI. I am amazed at the capacity of an algorithms to create images, and to observe the artificial creativity we now see for the first time.
2. Abstraction
I think with the success and immediate mainstream adoption of Generative AI, we saw the great appetite out there for automation and abstraction. No one wants to do boring work and summarizing documents; no one wants to read long websites, they just want the gist of it. If I drive a car, I don’t need to know how the engine works and every equation that the engineers used to build it — I just want my car to drive. The same level of abstraction is now expected in AI. There is a lot of opportunity here in creating these abstractions for the future.
3. Opportunity
I like that we are in the beginning of AI, so there is a lot of opportunity to jump in. Most people who are passionate about it can learn all about AI fully online, in places like Open Institute of Technology. Or they can get experience working on small projects, and then they can apply for jobs. It is great because it gives people access to good jobs and stability in the future.
What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?
1. Fairness
The large companies that build LLMs spend a lot of energy and money into making them fair. But it is not easy. Us, as humans, are often not fair ourselves. We even have problems agreeing what fairness even means. So, how can we teach the machines to be fair? I think the responsibility stays with us. We can’t simply say “AI did this bad thing.”
2. Regulation
There are some regulations popping up but most are not coordinated or discussed widely. There is controversy, such as regarding the new California bill SB1047, where scientists take different sides of the debate. We need to find better ways to regulate the use and creation of AI, working together as a society, not just in small groups of politicians.
3. Awareness
I wish everyone understood the basics of AI. There is denial, fear, hatred that is created by doomsday misinformation. I wish AI was taught from a young age, through appropriate means, so everyone gets the fundamental principles and understands how to use this great tool in their lives.
For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?
I think maybe the right question is: what are you passionate about? Do that, and see how you can use AI to make your job better and more exciting! I think AI will work alongside people in most jobs, as it develops and matures.
But for those who are looking to work in AI, they can choose from a variety of roles as well. We have technical roles like data scientist or machine learning engineer, which require very specialized knowledge and degrees. They learn computing, software engineering, programming, data analysis, data engineering. There are also business roles, for people who understand the technology well but are not writing code. Instead, they define strategies, design solutions for companies, or write implementation plans for AI products and services. There is also a robust AI research domain, where lots of scientists are measuring and analyzing new technology developments.
With Generative AI, new roles appeared, such as Prompt Engineer. We can now talk with the machines in natural language, so speaking good English is all that’s required to find the right conversation.
With these many possible roles, I think if you work in AI, some basic subjects where you can start are:
- Analytics — understand data and how it is stored and governed, and how we get insights from it.
- Logic — understand both mathematical and philosophical logic.
- Fundamentals of AI — read about the history and philosophy of AI, models of thinking, and major developments.
As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?
Engaging more women in the AI industry is absolutely crucial if you want to build any successful AI products. In my twenty years career, I have seen changes in the tech industry to address this gender discrepancy. For example, we do well in school with STEM programs and similar efforts that encourage girls to code. We also created mentorship organizations such as AnitaB.org who allow women to connect and collaborate. One place where I think we still lag behind is in the workplace. When I came to the US in my twenties, I was the only woman programmer in my team. Now, I see more women at work, but still not enough. We say we create inclusive work environments, but we still have a long way to go to encourage more women to stay in tech. Policies that support flexible hours and parental leave are necessary, and other adjustments that account for the different lives that women have compared to men. Bias training and challenging stereotypes are also necessary, and many times these are implemented shoddily in organizations.
Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?
Machine Learning and AI learn from data. Unfortunately, lot of our historical data shows strong biases. For example, for a long time, it was perfectly legal to only offer mortgages to white people. The data shows that. If we use this data to train a new model to enhance the mortgage application process, then the model will learn that mortgages should only be offered to white men. That is a bias that we had in the past, but we do not want to learn and amplify in the future.
Generative AI has introduced a new set of fresh risks, the most famous being the “hallucinations.” Generative AI will create new content based on chunks of text it finds in its training data, without an understanding of what the content means. It could repeat something it learned from one Reddit user ten years ago, that could be factually incorrect. Is that piece of information unbiased and fair?
There are many ways we fight for fairness in AI. There are technical tools we can use to offer interpretability and explainability of the actual models used. There are business constraints we can create, such as guardrails or knowledge bases, where we can lead the AI towards ethical answers. We also advise anyone who build AI to use a diverse team of builders. If you look around the table and you see the same type of guys who went to the schools, you will get exactly one original idea from them. If you add different genders, different ages, different tenures, different backgrounds, then you will get ten innovative ideas for your product, and you will have addressed biases you’ve never even thought of.
Read the full article below:
Source:
- Il Sole 24 Ore, Published on July 29th, 2024 (original article in Italian).
By Filomena Greco
It is called OPIT and it was born from an idea by Riccardo Ocleppo, entrepreneur, director and founder of OPIT and second generation in the company; and Francesco Profumo, former president of Compagnia di Sanpaolo, former Minister of Education and Rector of the Polytechnic University of Turin. “We wanted to create an academic institution focused on Artificial Intelligence and the new formative paths linked to this new technological frontier”.
How did this initiative come about?
“The general idea was to propose to the market a new model of university education that was, on the one hand, very up-to-date on the topic of skills, curricula and professors, with six degree paths (two three-year Bachelor degrees and four Master degrees) in areas such as Computer Science, AI, Cybersecurity, Digital Business; on the other hand, a very practical approach linked to the needs of the industrial world. We want to bridge a gap between formal education, which is often too theoretical, and the world of work and entrepreneurship.”
What characterizes your didactic proposal?
“Ours is a proprietary teaching model, with 45 teachers recruited from all over the world who have a solid academic background but also experience in many companies. We want to offer a study path that has a strong business orientation, with the aim of immediately bringing added value to the companies. Our teaching is entirely in English, and this is a project created to be international, with the teachers coming from 20 different nationalities. Italian students last year were 35% but overall the reality is very varied.”
Can you tell us your numbers?
“We received tens of thousands of applications for the first year but we tried to be selective. We started the first two classes with a hundred students from 38 countries around the world, Italy, Europe, USA, Canada, Middle East and Africa. We aim to reach 300 students this year. We have accredited OPIT in Malta, which is the only European country other than Ireland to be native English speaking – for us, this is a very important trait. We want to offer high quality teaching but with affordable costs, around 4,500 euros per year, with completely online teaching.”
Read the full article below (in Italian):
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