Technology keeps evolving, especially in the workplace; therefore, organizations are looking for ways to secure large amounts of data and use this data properly. Also, many students want to make a career in cyber security or data science because of the many available opportunities. Before you choose which career path to follow, you need to know the difference between cyber security and data science.
So, what is the difference between cyber security and data science? The main difference between cyber security and data science is in the objective of the respective fields. The key objective of cyber security is to protect and secure data and networks from unauthorized access. Whilst data science aims to extract valuable insight by processing big data into specialized and more organized data sets.
For cyber security professionals, learning never stops because they need to keep up with the different ways that attackers use to pose threats. To become a data scientist, you need to be well versed in a wide range of technical and analytical skills.
Picking a career choice can be difficult, especially if you are unsure of what the careers entail. If you want to be a cyber security expert or data scientist, you first need to know what these careers mean.
What is Data Science?
When browsing the internet looking for careers to pursue, you might see some companies advertising vacancies for data scientists. If you are interested in pursuing a data-driven career, you might be wondering what data science entails.
Data science is a blend of different algorithms, tools, and machine learning principles working together to unearth any number of hidden patterns from unaltered raw data. Data science is conducted in five steps, which include planning, evaluating, explaining, deploying, and monitoring a model.
Data science is used to make predictions and decisions using prescriptive analytics, machine learning, and predictive causal analytics. Data science is very important in business. It has helped transform business in numerous ways.
Data science is conducted in steps that start with planning; the data scientist or manager picks out a specific project and outlines all the potential outputs. The data scientist will then build a data model using various open-source libraries or in-database tools.
As a data scientist, you need to have the right tools to access the right data. Accuracy is very important, and the data scientist needs to first evaluate the model before deploying it. Evaluating gives the data scientist a chance to improve the model or replace it with a better one.
After explaining the model, the data scientist will then deploy the model. Deploying the model is a difficult task because you need to take a good machine learning model and get it to work properly. The last step is to monitor the model because you need to ensure that the model is working properly.
Monitoring the model also helps notify the data scientists if the data is still important. For example, cyber criminals always develop new ways to hack accounts, and you cannot keep using old data to make future predictions.
As a data scientist, your job is to explain what is going on by processing the history of data. A data scientist will have to look at data from different angles, such as predictive causal analytics, among others. Predictive causal analytics can be used to predict different possibilities of a particular event in the future.
For example, a credit company can hire a data scientist to predict how their customers will pay the money back. The data scientist will have to develop a model to predict if the customers will be paying their loans on time.
Another angle is predictive analysis, which utilizes a model with the intelligence to make its own decisions and modify it to fit the current parameters. For example, many car companies are developing self-driving cars; data scientists provide the algorithm on specific data which the car uses to make decisions. The self-driving car utilizes the data to turn and modify the data when the self-driving car needs to stop suddenly or use a different road if the road ahead is blocked.
The machine learning angle is divided into two; machine learning for making predictions and machine learning for pattern discovery. Machine learning for making predictions refers to the process of analyzing data to make predictions or determine a future trend, for example, creating a model that uses historical data of fraudulent purchases to detect fraud. Machine learning for pattern discovery involves analyzing data to find out hidden patterns to make meaningful predictions.
Many organizations use data science to turn their data into a competitive advantage to improve their products or services. Data science has helped organizations improve their efficiency by analyzing weather conditions, traffic patterns, and various other factors; for instance, logistics companies can use data science to reduce costs and improve their delivery speed.
Financial institutions are also using data science to detect fraud by pointing out suspicious behaviors and strange actions. Data science also helps improve sales by creating recommendations for customers based on previous purchases.
The Differences in Cyber Security and Data Science Careers?
Many students would love to have a successful career in computer science but picking a specific field to specialize in can be difficult. You will see companies looking to hire cyber security experts or data scientists and wonder what the difference between the two careers is.
The main difference between cyber security and data science careers is the individual’s role in any organization. As a cyber security professional, you will be tasked to protect the data and network of your employer. As a data scientist, your role will be gathering and analyzing large amounts of structured and unstructured data. Another difference is the level of education required for a cyber security professional is not the same as that of a data scientist. Other differences will include income and future academic goals.
Cyber security involves protecting data and networks, and most organizations, both private and public, hire professionals to ensure that hackers do not gain access to their networks or private information. When you choose to become a cyber security expert, you have to develop ways to defend your employer’s computer network or data center. Other roles you might take up include monitoring infrastructure, developing cyber security policies, auditing policies, and controls and implementing new security measures.
Getting a job as a cyber security expert does not require a lot of education compared to becoming a data scientist. You can get a degree in computer engineering, information security, computer science, or any other relevant discipline that offers cyber security specialization.
You also have to keep taking several accredited programs and certifications to stay updated with significant changes in the field. Some of the professional certifications that can help you go further in your career include CEH (Certified Ethical Hacker), Certified Information Systems Auditor (CISA), and Cisco Certified Network Associate (CCNA).
A starting entry salary in cyber security is around 40,000 US dollars in a year, and it can go up to 105,000 USD per year, depending on the organization and the state you live in. A cyber security engineer is the highest paying job in data protection, earning 120,000 US dollars and increasing to 200,000 US dollars per year. The salary also changes depending on the contract and role you get in an organization. For example, ethical hackers earn depending on their contract, and some are hired for a particular job once the contract ends.
The main role of a data scientist in any organization is to develop strategies for analyzing data and building models for specific applications. A data scientist builds models using tools such as Python, and then deploys and monitors the model as it analyses data. Unlike cyber security, where most of the time the experts work alone, data scientists are always in teams. The team includes other data professionals such as data engineers, business analysts, IT architects, and application developers.
If you want to become a data scientist, you first need an undergraduate degree in data science or any other related field. You then need to earn a master’s degree in data science before you look for employment opportunities. Most organizations prefer to employ a data scientist with a master’s degree in Data Science, unlike a job in cyber security, where a bachelor’s degree is deemed sufficient by most organizations. A master’s degree in data science can also create more employment opportunities for you because very few people own it.
Also, the starting salary of a data scientist is slightly higher than that of a cyber security professional, with most data scientists getting 70,000 US dollars, which can increase to 180,000 US dollars per year. A senior data scientist can earn up to 250,000 US dollars per year. Suppose you want to increase your earning power as a data scientist. In that case, you should specialize in a particular field such as artificial intelligence, machine learning, or database management and improve your skills.
What Is the Difference Between Cyber Security and Data Analytics?
Many organizations are using their IT departments to protect their data as well as analyze them. These organizations require skillful personnel to carry out those roles, and you might be mulling over the decision to become a cyber security expert or data analyst. Before you make a decision, you need to know the difference between cyber security and data analytics.
While cyber security involves protecting data and computer networks, data analytics is the science of analyzing raw data to make conclusions from the results. There are no specific steps when a cyber security professional is carrying out their job, while data analysis has several steps a data analyst has to follow.
Cyber security involves defending computer devices, data, and networks from malicious attacks. Cyber security is divided into several categories, including application security, network security, operational security, information security, disaster recovery, and end-user education. Network security involves securing computer networks from unauthorized access.
Application security focuses on keeping the software and devices from cyber threats. Another category of cyber security is information security involving data protection, both in storage and in transit.
Disaster recovery and business continuity are how an organization responds to a cyber-attack. Organizations need recovery policies on how they can recover lost data and how to prevent future attacks. End-user education involves informing people how to prevent cyber-attacks, for example, informing workers in an organization to avoid opening attachments from suspicious emails or using unidentified USB drives on computers connected to the organization’s network.
Data analytics involves analyzing large amounts of data and using them to make sound decisions. There are several data analytics types, including descriptive analytics, diagnostics analytics, predictive analytics, and prescriptive analytics. Descriptive analytics uses the data to describe what has been going on for a specific period. Diagnostic analytics focuses on why something happened. If the sales of a product or service are low, the data analyst will use the information to find out why it happened.
Predictive analytics focuses on what will happen in the future. For example, suppose the weather was hot in the previous summer, and it negatively affected sales. In that case, the data analyst will use that data to predict how the hot weather will affect sales in the coming summer. Prescriptive analytics is using the data to suggest a good course of action. Prescriptive analytics was very important in avoiding losses by using data to avoid mistakes a few years back.
When carrying out their duties, a cyber security professional does not have a specific guideline to follow. A cyber security expert will use the necessary tools to prevent or stop a cyber threat. On the other hand, a data analyst will follow several steps when analyzing large amounts of data.
The first step is usually to determine the data requirements and how the large amounts of data will be classified. For example, the data analyst can classify the data using relevant categories such as income, demographic, age, and gender.
The second step is to collect the data. A data analyst can collect data using computers, online sale receipts, cameras, or personnel. Once the data is collected, the data analyst will organize it using a spreadsheet or any other form of software that can take statistical data. The data is then cleaned up to remove unnecessary data, errors, or duplications before analysis. After the data is analyzed, the data analyst will take the necessary action.
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When you think of a data-driven career, you have numerous options, but they all depend on your knowledge, skills, and long-term goals. A career in cyber security means you will not stop learning because hackers keep coming up with new ways to hack computers and networks.
If you want to be a data scientist or analyst, you need to get a master’s degree and have excellent skills in Python, Yellowfin, SQL, and Qlik Sense. If you are yet to make a career choice, you can talk to your teachers or reach out to a data science or cyber security alumni.