How is someone with seemingly unrelated or comparable experience supposed to know what data to hack into, how to do it, or even if they should? Schedule a call today if you’re already familiar with the two industries and want to learn more about your alternatives. Organizations are seeking for ways to safeguard enormous volumes of data and use them effectively as technology continues to evolve, particularly in the workplace. Because of the numerous prospects, many students choose to pursue a career in cyber security or data science. Before deciding on a professional path, it’s important to understand the differences between cyber security and data science. So, what’s the difference between data science and cyber security? The primary distinction between cyber security and data science is the goal of each subject. The main goal of cyber security is to keep data and networks safe from unwanted access. Data science, on the other hand, tries to extract valuable information from huge data by processing it into specialised and more organised data sets. Because they must stay up with the various tactics that attackers employ to pose dangers, cyber security experts must never stop studying. To work as a data scientist, you must be proficient in a variety of technical and analytical skills. Choosing a career path can be tough, especially if you are unfamiliar with the various options. If you want to be a data scientist or a cyber security specialist, you must first understand what these jobs entail.

What is Data Science, and how does it work?

When you’re looking for a new job, you could come across several companies that have openings for data scientists. You might be wondering what data science comprises if you want to pursue a data-driven job. Data science is a collection of algorithms, tools, and machine learning techniques that work together to uncover hidden patterns in unaltered raw data. Planning, evaluating, explaining, deploying, and monitoring a model are the five phases involved in data science. Prescriptive analytics, machine learning, and predictive causal analytics are all used in data science to generate predictions and judgments. In business, data science is critical. It has aided in the transformation of company in a variety of ways. The first step in data science is planning; the data scientist or management selects a specific project and defines all potential outputs. The data scientist will then use several open-source libraries or in-database technologies to create a data model. You need the correct tools to access the right data as a data scientist. The data scientist must first examine the model before deploying it, as accuracy is critical. The data scientist might use the evaluation to improve the model or replace it with a better one. The data scientist will then deploy the model after describing it. The task of deploying the model is complex since you must take a decent machine learning model and make it perform properly. The next step is to keep an eye on the model to check that it is operating correctly. Monitoring the model also allows data scientists to be notified if the data is still relevant. For example, cyber criminals are constantly inventing new techniques to hack accounts, so you can’t make future forecasts based on old data. Your duty as a data scientist is to use historical data to explain what’s happening on. A data scientist will have to examine data from a variety of perspectives, including predictive causal analytics and others. Predictive causal analytics can be used to forecast several outcomes for a specific occurrence in the future. A credit company, for example, could hire a data scientist to forecast how their customers will repay their debts. The data scientist will need to create a model that can predict whether or not clients will pay their debts on time. Predictive analysis is another approach, which uses a model with intelligence to make judgments on its own and adapt it to fit the current parameters. Many vehicle firms, for example, are working on self-driving cars; data scientists offer the algorithm based on specific data that the car uses to make decisions. When the self-driving car needs to stop unexpectedly or take a different road because the road ahead is blocked, the data is used to turn and adjust the data. Machine learning can be separated into two types: predictive machine learning and pattern discovery machine learning. Machine learning for prediction refers to the act of analysing data in order to create predictions or discern a future trend, such as constructing a model that detects fraud using historical data of fraudulent purchases. Pattern discovery using machine learning entails studying data to uncover hidden patterns and create meaningful predictions. Many businesses utilize data science to better their products or services by turning their data into a competitive advantage. By studying weather conditions, traffic patterns, and other factors, data science has helped firms improve their efficiency; for example, logistics companies can utilise data science to reduce costs and enhance delivery speed. Data science is also being used by financial institutions to detect fraud by pointing out suspicious tendencies and unusual actions. Data science also aids sales by providing clients with recommendations based on previous purchases.

What Are the Differences Between Cybersecurity and Data Science Jobs?

Many students want to pursue a successful career in computer science, but determining which sector to focus in can be challenging. When you see firms wanting to hire cyber security professionals or data scientists, you might wonder what the difference is. The individual’s function in any firm is the fundamental difference between cyber security and data science careers. As a cyber security professional, you will be responsible for safeguarding your employer’s data and network. The role of a data scientist is to collect and analyse enormous amounts of structured and unstructured data. Another distinction is that a cyber security expert must have a different level of schooling than a data scientist. Income and future academic ambitions will be among the other distinctions. Cyber security entails safeguarding data and networks, and most commercial and public companies engage professionals to ensure that hackers do not obtain access to their networks or personal information. When you decide to pursue a career in cyber security, you must devise strategies to protect your employer’s computer network or data centre. Monitoring infrastructure, designing cyber security policies, reviewing policies and controls, and implementing new security measures are some of the other jobs you could take on. In comparison to being a data scientist, becoming a cyber security expert requires less education. You can specialise in cyber security by earning a degree in computer engineering, information security, computer science, or any other relevant area. To stay current with key changes in the profession, you must continue to take many authorised programmes and certifications. CEH (Certified Ethical Hacker), CISA (Certified Information Systems Auditor), and Cisco Certified Network Associate are some of the professional credentials that might help you advance in your career (CCNA). A beginning wage in cyber security is around 40,000 US dollars per year, and it can go up to 105,000 US dollars per year, depending on the company and the state you live in. A cyber security engineer is the highest-paying career in data protection, with an annual salary of $120,000 to 200,000 dollars. Salary varies according to the contract and function you are assigned in an organisation. Ethical hackers, for example, are paid based on their contract, and some are hired for specific jobs after the contract expires. A data scientist’s primary responsibility in any organisation is to devise ways for evaluating data and developing models for specific applications. A data scientist creates models with technologies like Python, then deploys and monitors them as they analyse data. Unlike cyber security, where professionals usually operate alone, data scientists always work in groups. Data engineers, business analysts, IT architects, and application developers are among the team’s other members. To work as a data scientist, you must first have a bachelor’s degree in data science or a similar discipline. After that, you’ll need to get a master’s degree in data science before looking for work. In contrast to a position in cyber security, where a bachelor’s degree is considered sufficient, most firms prefer to hire a data scientist with a master’s degree in data science. Because few people have a master’s degree in data science, it can open up additional job prospects for you. A data scientist’s beginning income is slightly greater than that of a cyber security expert, with most data scientists earning 70,000 US dollars per year, with the potential to rise to 180,000 US dollars per year. A senior data scientist can earn up to $250,000 per year in the United States. Assume you wish to improve your salary as a data scientist. In that situation, you should focus on improving your skills in a specific subject such as artificial intelligence, machine learning, or database management.

What Is the Difference Between Data Analytics and Cyber Security?

Many businesses rely on their IT departments to both preserve and analyse their data. To carry out such duties, these firms demand skilled individuals, and you may be considering a career as a cyber security specialist or data analyst. You must first understand the distinction between cyber security and data analytics before making a selection. Data analytics is the science of analysing raw data to draw inferences from the results, whereas cyber security is the science of securing data and computer networks. When a cyber security specialist performs their job, there are no precise protocols to follow, however data analysis has various steps that a data analyst must follow. Defending computer devices, data, and networks from hostile attacks is what cyber security is all about. Application security, network security, operational security, information security, disaster recovery, and end-user education are some of the categories of cyber security. Network security is the process of preventing unauthorised access to computer networks. Application security is concerned with protecting software and devices against cyber-attacks. Information security, which includes data protection both in storage and in transit, is another type of cyber security. An organization’s response to a cyber-attack is disaster recovery and business continuity. Organizations require recovery procedures that outline how to recover lost data as well as how to avoid repeat assaults. End-user education entails teaching people how to avoid cyber-attacks, such as instructing employees in an organisation to avoid opening attachments from questionable emails or connecting computers to the network with unidentified USB drives. Analyzing massive volumes of data and applying it to make informed judgments is what data analytics is all about. Descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics are some of the several forms of data analytics. Descriptive analytics makes use of data to describe what has happened across time. The goal of diagnostic analytics is to figure out why something happened. If a product or service’s sales are poor, the data analyst will utilise the information to figure out why. Predictive analytics is concerned with what is likely to occur in the future. Assume the weather was hot the prior summer, and this had a detrimental impact on sales. In that situation, the data analyst will utilise the information to forecast how the hot weather will effect summer sales. Data is used in predictive analytics to propose a good course of action. A few years ago, predictive analytics was critical in preventing losses by using data to avert mistakes. A cyber security expert does not have a set of rules to follow when performing their work. To prevent or stop a cyber danger, a cyber security specialist will employ the appropriate technologies. When evaluating enormous volumes of data, on the other hand, a data analyst will take multiple steps. The first stage is usually to figure out what data you’ll need and how you’ll categories enormous amounts of data. The data analyst, for example, can categories the information using appropriate categories such as income, demographics, age, and gender. The data must be collected in the second phase. Computers, online sale receipts, cameras, and personnel can all be used to collect data for a data analyst. The data analyst will organise the data using a spreadsheet or any other type of software that can handle statistical data once it has been obtained. Before analysis, the data is cleaned up to remove any extraneous information, mistakes, or duplications. The data analyst will take the necessary action when the data has been analysed. Here’s what you should know to figure out which job path is right for you:

1. How do you distinguish between data science and cyber security?

In a word, data science’s main goal is to extract useful information from large amounts of data by processing it into smaller, more structured data sets. Big data pools and networks are protected and secured by cyber security from unauthorised access. To put it another way, cyber security is the process of securing electronic data systems from criminal or unauthorised activity. Finally, the discipline serves as a preventative tool to protect confidential data and data processes from unauthorised access. Individuals that are interested, have a strong desire to learn, and like creative problem solving are ideally suited to work in cyber security. Working in the cyber business has a number of obstacles, including quickly expanding technologies, the constant appearance of new trends, and society’s growing reliance on digital networks. This means that cyber security experts must constantly study and adapt in order to keep their skills current and remain ahead of emerging dangers. Data Scientists, on the other hand, play a more abstract function because their work isn’t solely focused on analytics or engineering; rather, it’s a multidisciplinary profession that involves collecting, retrieving, and analysing enormous volumes of big data from a variety of sources. Artificial intelligence and machine learning techniques such as support-vector machines, regression, cluster analysis, and neural networks are required in this field. Furthermore, data scientists must be able to make big data decisions in order to reach end goals and solve complicated challenges in order to perform their jobs effectively. This necessitates data workers mastering a wide variety of technical and analytical abilities. People who appreciate mathematics and statistics, as well as analytics, machine learning, AI, and consulting, are excellent candidates for a career in data science.

2. What is big data, exactly?

If there’s one phrase you’ll hear over and over again as you make your way into the data sector, it’s ‘big data.’ Most structured and unstructured data pools are so chaotic that organisations are swamped and bombarded with it on a regular basis. The technologically and globally connected society we live in generates an almost incomprehensible amount of data, with a wealth of opportunities and solutions hidden within it. It’s there, waiting to be discovered, whether it’s the cure for the common cold, how to live on the moon, or the most effective government model ever devised. This is where data science and cyber security come into play, since how data is collected, analysed, stored, secured, and used has given rise to and influenced these fields.

3. What qualifications do I require to work as a data scientist or a cyber security expert?

Because data science and cyber security are highly competitive and demanding fields, it is critical for people interested in pursuing a career in these fields to not only educate themselves on the theory behind them, but also to gain hands-on experience. This will aid in the development of the necessary abilities, mindset, and agility to succeed in these industries. Aspiring professionals in any field of data require practical skills training so that they can begin working on the job right away. Unlike more traditional approaches to education, which take years and have a heavy theoretical foundation, the quickest way to get trained is through modern industry-focused courses that streamline their content and are geared toward giving students the tools and experience they need to get started working right away. With the data and cyber business being so new and developing, it’s critical for companies to be able to standardise and trust a potential employee’s experience. Those who want to get into the data sector will require an industry certification to do so. Employers will see that you have the required practical skills to address their demands and provide value right away if you hold an industry certification in data science or cyber security. It’s crucial to have end-to-end project experience in addition to understanding how to utilise in-demand technologies and approaches on the job. Completing a project reveals to employers that a person is accountable and capable of handling real-world work. The capstone projects of the Institute of Data, for example, give a concrete reference for professionals to use during interviews and networking events. In particular, professionals working in the field of data science must be able to understand big data sets, extract data insight, detect trends and patterns using machine learning techniques, and creatively tackle data challenges. To tackle the highly complicated business problems they’ll be entrusted with on the job, data professionals must have a good grasp of statistics, computational mathematics, machine learning, python programming, data mining, data wrangling, and data analytics and visualisation. Intrusion detection, incident response, risk assessment, governance, and compliance are all competencies that a cyber security expert should have. Working as a cyber security specialist necessitates a lot of ‘black-hat,’ or judgment-based, thinking. Cyber experts with a mix of hard and soft talents, such as the ability to strategically plan, produce, perform, test, improve, and solve company security problems, are also in great demand by potential employers. Cyber security is ideal for you if you have a more well-rounded approach to data, appreciate complex problem solving, are interested in security analytics, and want to protect data and systems from unauthorised access. Data intuition and the ability to speak with data are advantageous in both disciplines. In addition, knowledge with in-demand data tools and technologies such as Python, Linux, C++, Java, SQL, Qlik Sense, Splunk, Yellowfin, Tableau, or Microsoft Power BI is often expected. Finally, those considering a career in data science or cyber security should focus on job outcomes (such as pay negotiation and interpersonal skills), seek career coaching, and expand their industry network. If you don’t have access to job outcome support, this can seem difficult. All Institute of Data alumni benefit from a Job Outcomes programme that includes one-on-one career coaching, special hiring events, and reverse recruitment opportunities to get them job-ready and working.

4. What are my options for finding a career in data science or cyber security?

It’s one thing to upgrade your skills, but how can you get a job? It all boils down to being visible and being competent, in addition to a job outcomes programme and the assistance of a professional career coach. Here are four things you can do to speed up your job search in data science or cyber security:

  1. Attend networking and hiring events: In-person and virtual networking and hiring events are meant to bring certified professionals face-to-face with future employers, recruiters, and colleagues. Joining the Institute of Data community and professional network, or visiting sites like Meetup or Eventbrite, is a good method to get access to these events, many of which are free. Attend these events with confidence, dress professionally, and take advantage of any possibility for an interview or simply an introduction. Take measures to connect socially and professionally from here; LinkedIn is a wonderful place to start.
  2. Independent research: One of the most critical distinctions between those who thrive in the data market and those who do not is keeping your finger on the pulse of new data and cyber trends and what is happening in the sector. Read the newest data science and cyber security articles and blogs, subscribe to newsletters, and listen to podcasts to stay on top of the hottest developments. Don’t feel obligated to study everything at the same time. Instead, emphasise your capacity to keep up with current events. Communication will demonstrate your enthusiasm for the sector and your readiness to begin a successful career.
  3. Attend data conferences: Conferences are a fun and unique way to share critical information and build relationships. During conferences, you’ll get the chance to hear from industry experts as well as network with individuals who have similar career goals and prospective mentors who are industry leaders. This is a fantastic approach to form a support network with whom you can later interact! Employers will praise your attendance because it demonstrates initiative and commitment.
  4. Keep your LinkedIn page up to date: LinkedIn should be utilised to promote your talents, interests, and abilities. It’s more than simply an online resume, though. LinkedIn is also a useful networking tool and a source of industry news. Connect with and follow people and firms in your field who you admire or want to work for, and start interacting and growing your professional online network while you’re looking for work!

5. What roles can I apply for in Asia-Pacific once I’ve completed my data science or cyber security training?

Data and cyber experts that can fulfill employer needs will have rich work prospects, rapid career advancement, and will be at the cutting edge of technology innovation. Trained data and cyber professionals are in high demand around the world, and the fields offer a lot of flexibility, as professionals with prior industry experience and domain knowledge can work in a variety of industries, and freshers who can demonstrate their practical and soft skills to potential employers are in high demand for entry-level positions. In today’s job market, data and cyber professionals may advance their careers by outperforming and exceeding employer expectations with job-ready abilities. As a data scientist, your options are more diverse and imaginative than you might think. With every organisation striving to remain ahead of their competition, understand their consumers, and improve operational processes, consumer products, and experiences utilising real-time data insight, there is a great demand for practically trained data experts. Employers are hunting for data experts that possess job-ready data abilities. Data scientist, data analyst, statistician, machine learning engineer, data architect, data engineer, or data consultant are all jobs in data science that you may apply for. These positions may lead to more senior positions such as senior AI architect, senior-level director, chief data scientist, or chief information officer. To keep up with expanding company needs and increased security risks, new opportunities in cyber security are developing. It’s also worth noting that the industry is still in its infancy, and when corporations realise how far technology and data hacking have progressed, more organisations will begin to safeguard and future-proof their business operations, creating new employment in the process. Cyber security experts currently have specific employment possibilities in fields such as government, security, finance, e-commerce, and privacy law, allowing them to pursue a more specialised career path. There are different jobs available in cyber security analysis, incident response, governance, risk assurance, and compliance. Cyber security analyst, security consultant, IT security specialist, incident responder, systems engineer, pen-tester, vulnerability analyst, computer forensics analyst, or cryptographer are all jobs you might apply for. These positions can lead to more senior positions such as cyber security manager, information technology director, or cyber security officer.

So, should you get into data science or cyber security as a career?

When considering a data-driven career path, it’s critical to consider your current talents, your willingness to gain new ones, and your long-term career ambitions. Consider whether data science is a good fit for your interests in analytics and statistics, as well as your desire to solve business problems with data insight and machine learning technology, or whether cyber security is a good fit for your desire to assess and mitigate security risks in novel ways to protect business data and processes.