Data science and Computer science are disciplines that seem to be similar to each other and can be easily mistaken. However, once you go into details, you will easily discover that there are significant differences between them. These differences need to be realized in order to make full use of processes that can be done within each of them. It is also especially important to acknowledge situations when one discipline is required to accomplish the goals of another. In this article, we are going to take a closer look at both these disciplines and examine what their main purposes are. Let’s make a short comparison: Data science vs. computer science.

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The fact is that, in order to become specialized in data science, you usually should also learn the basics of computer science. There are data scientists who first develop skills and knowledge related to machine learning (ML) and artificial intelligence (AI) and, once it is done, they learn programming and computer science.

It is undeniable that good data scientists should possess some computer science skills. While computer science is mostly about software engineering, data science is concerned with machine learning algorithms and their applications that help in finding solutions to real-life problems and obstacles. In this article, we will point out what these differences exactly are, how data science and computer science are similar to each other, and what are the detailed characteristics of each of these disciplines.

Data science

The main purpose of data science is to use various tools, machine learning principles, and algorithms in order to find hidden patterns in raw data. Data Science includes the preparation of data for processing and analysis, the performance of analysis, and the presentation of results to facilitate the decision-making process in organizations.

Preparation of data usually involves cleansing, aggregation, and manipulation so that it is ready for various types of processing and purposes. The analysis is driven by software that digs through data to find repetitive patterns and transform them into useful predictions.

In order to ensure that such predictions are valid, scientifically designed tests and experiments need to be performed on used AI models and algorithms.

The results of this analysis should be understandable for anyone, especially a person without ML and AI-related background. This is why they should be presented with visualization tools that enable effortless understanding and drawing conclusions.

Data science, numbers

Because of the above, professionals working as data scientists need to acquire computer science and data visualization skills. A good data scientist must be able to fulfill the following tasks:

  • Apply mathematics, scientific methods, and statistics into everyday work
  • Use a wide range of tools and technologies necessary for evaluation and preparation of data, such as Structured Query Language (SQL) or data integration methods
  • Extract insights from data with the use of AI, ML, and deep learning
  • Create models which automate data processing
  • Talk about data in a way that clearly conveys the meaning of analysis results to stakeholders with different levels of technical knowledge and understanding
  • Explain how information from data can be used to solve various business issues

In order to create ML and AI models, data scientists need to know how to code. Data scientists usually opt for programming languages that support or include statistical, machine learning, and graphic capabilities such as R or Python. They also should be proficient in using big data platforms such as Apache Spark. Additionally, to properly present analysis results, they may need knowledge of basic graphic tools to create business presentations and spreadsheet applications.

computer, data science, predictions

Use of data science

What is especially tempting about data science is the fact that there is an almost unlimited number of companies and industries which could benefit from its opportunities. Almost all business processes and most types of customer experience strategies can be improved with the use of data-driven optimization.

POSSIBLE USES OF DATA SCIENCE IN BUSINESS

  1. Decision making

As we have previously mentioned, the main purpose of data science is finding patterns and drawing conclusions from the organization’s data. Thanks to data science, answers to important business questions and solutions to problems occurring in the company can be easily found.

  1. Security

The popularity of ML models can be justified by the fact that data scientists adjust them according to the current needs of the company. That is why the majority of organizations create models which process huge amounts of data in real-time and are able to immediately find recurring patterns and detect possible anomalies or errors which would normally go unnoticed by a human eye.

It is especially useful for security purposes. In case of behaviors on users’ accounts that seem alerting, the system can notify the administrator who is able to take immediate action and check whether a danger was real.

cyber security

  1. Sales

Another area that can benefit from data science is sales. Since by analyzing data, you can predict future events, trends, and behavior, it is also possible to forecast sales in upcoming months. Thanks to data science, sellers may avoid overproduction or run out of the most popular supplies.

Naturally, the way in which a company will benefit from data science is dependent on the industry and individual needs of the company. For example, financial institutions are usually most interested in assessing risks of investments and ensuring security, while e-commerce businesses will be focused on controlling stock and marketing purposes.

How can you begin a career in data science?

Both, data science and computer science require similar basic knowledge of computers and technology. That is why data scientists should also master computer science. Each data scientist during the academic course should study fields such as statistics, linear algebra, and calculus.

career in data science

By the end of education, a data scientist should be able to use technical knowledge in order to process complex datasets. The next step is an application of previously possessed skills during on-the-job training. It is quite common among recent graduates to start their careers in smaller companies and start-ups. They constitute a perfect environment to quickly begin the application of the theoretical knowledge and efficiently put it into practice at work. There is no better way to advance a data science career, expand the scope of expertise and secure a high salary than by building practical experience.

THE WAY TO SUCCEED IN DATA SCIENCE

Data scientists usually work closely with key stakeholders and executives so that they are able to understand the key goals of the organization. They examine data to find out how it can be used to achieve corporate goals and develop business. A successful data scientist needs to be flexible, adaptable, open-minded, capable of creating innovative solutions and taking the initiative when the situation requires.

Data scientist often works with different teams. That is why it is necessary to maintain awareness of key business decisions taken on the various levels in diverse departments. It is also important to pay attention to what kind of data the company is willing to keep or remove. With time, you will gain experience and will be able to develop a strong understanding of consumers’ needs and behavior and discover ways of effective usage of data to ensure customer satisfaction. As we all know, any business will go downhill without customers. That is why all data-related processes at the end of the day should be beneficial to the operating of the company.

data scientist, woman, calculations

Computer Science

Now, let’s focus on computer science. According to the most general definition, it is a study of hardware and software computer technology. The hardware aspect is related to electrical engineering as it consists of basic computer designs and the way they work. This type of knowledge is a basis for understanding more complicated and advanced concepts, such as comprehending how a computer adds, subtracts, or performs any other operations.

The software part is mostly concerned with programming concepts and languages. These include functions, algorithms, and coding. The subjects with which computer science is concerned have expanded over time and together with technological development. For example, disciplines such as graphics, visual computing, or network-centered computing have not emerged in the standard computer science academic curriculum until the late 2000s. Traditional, long-standing issues in computer science include algorithms, characteristics of various programming languages, human-computer interaction, and intelligent systems (such as artificial intelligence and machine learning).

The most popular roles of computer scientists are associated with software engineering, database administration, system analysis, web development, hardware engineering, and network architecture.

computer science, laptop, hands

Since the everyday work of data scientists can include tasks from ML operations to web development and data engineering, it is daunting to clearly define the role computer science can play in a company. Usually, it will be dependent on the nature and needs of your firm. Below, we have listed skills any computer science role will probably involve:

  • Conceptualization of computational and math-related issues and challenges
  • The development of products that will solve practical computing problems
  • Doing research with programmers, software engineers, or IT professionals in order to find solutions to existing problems and create new products
  • Improvement of performance of existing systems and software
  • The development of new hardware, computing techniques, and materials
  • Problem analysis, anticipation, and diagnosing

How can you begin a career in computer science?

A bachelor’s degree in computer science is a necessity for an aspiring computer scientist. Completing a master’s degree in the same field can be an additional benefit that will give you a strong position in this competitive industry. A solid foundation for this career can be grounded quite early since anyone can focus on subjects such as IT, mathematics, and computer science.

career in computer science

What is helpful is the fact that many companies are willing to hire recent bachelor graduates who want to apply their new skills before starting pursuing the master’s degree. This will automatically guarantee a more grounded senior position and a higher salary.

HOW CAN I BECOME A SUCCESSFUL COMPUTER SCIENTIST?

It is quite common to perceive computer scientists as kind of mathematician. It is mostly due to the fact that their work involves thinking through and conceptualizing the math-related and computational problems and challenges. Computer scientists also write codes, create websites, and develop web/mobile applications. They need to know how to develop mathematical models that can be treated as interactive tools joining humans and computers.

To develop a career as a computer scientist, one needs to be able to translate theory into applications in any possible case. It is also necessary to be proficient in programming. That is why every computer scientist should keep programming language knowledge up to date and practice it as much as possible.

data base, computer scientist, servers

Apart from advanced computer and technological skills, computer scientists should also develop teamwork and communication skills, as their work is related to the majority of fields operating within the company. The computer science environment is highly competitive and developing at an extremely fast pace. That is why, in order to ensure a safe position in this market, computer scientists should continuously develop skills and always strive for excellence.

Key differences

It’s time to sum up our data science vs. computer science comparison. As you probably have noticed so far, both disciplines are quite similar to each other and require not so different skill sets. However, there are few differences that do not let anyone mistake them.

  1. Computer science gives knowledge on how computers are built and operate, while data science is concerned with data, especially ways of storing, processing, and manipulating it so that it can be used for the company’s inside purposes.
  2. Computer science is the main branch, while data science is a subfield of computer science.
  3. The main purpose of computer science is building and utilizing computers in the most efficient way, while data science is about handling and analyzing data.
  4. Computer science is more focused on software engineering and development. Data science is a combination of math, statistics, and engineering.
  5. Computer science encompasses more variety than data science.
  6. The education path differs in both fields. There are different degrees and positions for data science and computer science.
  7. Background in statistics is necessary for data scientists.

Data scientist vs. Computer scientist

The conclusion

In our data science vs. computer science comparison, we discovered that data science and computer science are two different fields that go together when we get to apply them in day-to-day work. Computer science is the basis for the use of technologies in computing, while data science enables handling existing data to make it more clear and available for different business purposes. Nowadays, both these fields are extremely important. Especially in environments where they are used to advancing and creating new opportunities and technologies which will change sophisticated processes performed by the human workforce into easily-automated tasks.

Undoubtedly, each field has its advantages, especially when we consider matters such as growth and development in areas of science, technology, business, and the creation of outstanding inventions and solutions. It is no wonder that more and more young people decide to take up a career in one of these two disciplines. The tech industry is currently booming with possibilities of working on world-changing models and applications.

There is a huge need for professionals in both fields. Big data is collected in various departments. That is why data scientists are needed in all industries, from marketing agencies and banking institutions to pharmaceutical companies and production plants. Computer scientists have a lot of employment options as well. Computer science is very well established when it comes to positions that are needed in almost every company to deal with tasks like hardware engineering or web design.

If you are interested in data science, we encourage you to get in touch with us! Addepto is an AI consulting company. We deal with data science every day and will gladly help you master big data in your company!

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