The world of technology is advancing at a breakneck pace. Professional roles in IT have become increasingly specialized and picking a career among the countless choices before you can be a daunting task. So, if you’re confused between Data Science and Computer Science, we’ve got your back.
Both fields of work have the potential to open up a world of opportunities for your budding career. While computers have been around since the early 19th century, and so has computer science, data science is a relatively new field.
Let’s get down to the basics and understand the similarities and differences between data science and computer science, the skills and qualifications needed to become either, and which is more suitable for you.
What is Data Science?
Data Science can be considered a subset of Computer Science, but it’s wildly different in application. Data Science is the holistic collection, maintenance, and analysis of large data sets. It’s a discipline that emerged from statistics to find insights in massive data files using scientific and computational methods.
We live in an age of information deluge, where it is necessary to find relevant information for decision-making and risk predicting. You could probably guess that most scientific fields work with data to postulate and predict.
Our access to big data has increased exponentially over the years, and so have the disciplines within data science. They include data analysis, data mining, visualization, machine learning, and more.
What is Computer Science?
The mother of digital technology, computer science has been around for much longer. The basic tools to design a computer include hardware and software. But its study involves much broader concepts like algorithms, computation, and information technology.
Manipulation and organization of data structures is a major part of computer science. However, it’s not limited to that. While computer science creates and computes data, data science processes existing data sets.
Computer Science is a multipronged field that includes complex embedded systems (motherboard), networks, computer security, graphics, encryption, etc. A computer scientist is usually a jack of all trades. You have to develop a technical understanding of one of the components down the line.
How did Data Science begin?
Data Science effectively emerged from the field of statistics. John Tukey was the first to write the paper ‘The Future of Data Analysis’ about the potential of data in 1962. Pioneering figures like William Cleveland and Leo Breiman paved the way for scientific data analysis.
With the entry of big data in the early 2000s, the importance of a cohesive science to analyze data has dawned upon big organizations. Since then, data science has been considered a science in its own right. Deep learning and machine learning are ultimately interdependent on advancements in data science.
History of Computer Science
The first computers began in the early 1800s with Charles Babbage and Ada Lovelace. In about 200 years, we have gone from the bulky ‘analytical machine’ and ‘turning machine’ to handheld smart devices and laptops with processors as small as a thumbnail.
Pioneers like Claude Shannon, with his ‘information theory’ and John von Neumann (of Von Neumann architecture fame), have transformed information technology.
From automation of simple tasks like addition and multiplication in the past to Artificial Intelligence today, computers have been performing more complex cognitive functions and saving human labour.
What are the uses of Data Science?
We are constantly putting out data through our social media channels, and the banking and healthcare sector deals with a ton of data every day. The same goes for finance, education, manufacturing, and every imaginable industry. The main application of data science is inefficiently storing all this data, using it to make better decisions, and ultimately improve upon existing data structures.
Data Science finds its application in predictive analysis, targeted advertising, business decision-making, machine learning, deep learning, etc.
What are the uses of Computer Science?
Computer science is a crucial part of our lives. We see it playing out every day, from entertainment to education to our professional lives. Computer science has a vast range of applications.
How to Become a Data Scientist?
Data Science is an interdisciplinary field that involves multiple designations. As data science is a further specialized field within computer science, there is an overlap between the coursework.
Data Scientists will usually have to complete additional certifications or a master’s degree in addition to a generalized degree in software engineering or computer science. The growth of data science has prompted universities to offer bachelor’s degrees in data science.
How to be a Computer Scientist?
Computer Science is a highly competitive field, and hence, you have to pick a specialization. The broader field of computer science would require a minimum bachelor’s degree.
Self-taught engineers and software developers are not new. We have seen many computer prodigies, but they do not minimize the importance of systemic education. Developing skills like programming languages (Java, Python), network security systems, encryption will come in handy.
What kind of jobs can you land in Data Science?
Data science has permeated many data-driven industries. Data Scientists can make themselves useful in any industry where data analytics, mining, and database administration are required.
While Data Scientist is a more generalized role, more specific designations like Business Analyst, Data Engineer, Database Administrator, Machine Learning Engineer, Data Architect have become popular.
What designations does Computer Science entail?
Computer Science has branched off into various disciplines and components from a single domain. The job profiles in this field will include web developer, software engineer, computer hardware engineer, cybersecurity specialist, cloud engineer, etc. It will also include the roles of a data scientist as a subset of computer science.
In addition, the emergence of artificial intelligence has made computer science roles all the more high-functioning and sophisticated. Robotics is an up-and-coming field of work, but it would require deeper study of computer systems, and ultimately you’d be spending more time in school and internships as a full-stack developer.
What does a Data Scientist do?
The responsibilities of a data scientist naturally depend on what specific role they take on within an organization. Some general responsibilities include:
- Collection of data and maintenance of databases
- Automation of data collection and storage
- Refining collected data and classifying it
- Processing data sets for preliminary analysis
- Analysis of large data sets to find critical insights
- Compare and combine insights with other decision-making factors
- Present the data with the aid of data visualization tools
- Collaborate with stakeholders and management to integrate data insights
- Create advanced solutions after further refinement from feedback
General roles and responsibilities within computer science:
As mentioned earlier, computer science is a vast field of work with several different job profiles. A few generalized responsibilities are given below:
- Analyzing organizational and consumer needs to drive software development
- Engineering hardware systems and refining their components
- Working towards automation of repetitive tasks
- Debugging and coding programming languages
- Introducing new frameworks and libraries for existing programming languages
- Creating and innovating software architecture
- Improving upon encryption and cybersecurity systems
- Improving network stability and security
- Web development and maintenance
- Writing stable code to host multimedia on games and websites
- Working on enhancing data storage capacities
- Creating AI that mimics human reasoning
The above list is hardly exhaustive. A broad field like computer science brings with it many other responsibilities.
How to choose and build a sustainable career?
Zeroing down on data science as your career path is not a decision you would regret. If you have an analytical mind, a keen eye for detail, and are comfortable handling large data sets, you’d surely make an excellent data scientist with the right pedigree.
With the growth of machine learning and deep learning, data scientists are playing a pivotal role in building a more automated future. A career in computer science is suitable for ingenious, innovative minds who are driven to create new systems to advance machines’ computing and reasoning powers.
In any case, you can’t go wrong with a degree in either data science or computer science. A good attitude and a hunger for knowledge will pave the way for a brilliant career in both fields.