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7 Data Science Skills for Your Career in Cyber Security

7 Data Science Skills for Your Career in Cyber Security

Data Science is a broad field encompassing many sectors from analytics to artificial intelligence. One of the more modern applications of the field includes the integration of concepts for use in cyber security. Having a foundational knowledge in data science will allow you to carve out your own space in the ever-evolving IT world. Here are seven essential data skills you should pick up to enhance your ability to predict, detect and counter network attacks.

1. Coding Languages

Programming is the future, and a majority of middle and upper-level jobs in cyber security require coding knowledge. Therefore, an understanding of how coding works and how it’s applied to a script or application is essential. Depending on your specialty, data science training in coding languages can prepare you for the evolving nature of the cyber world.

2. Security Analytics

Security analytics is an approach to cyber security fixated on data analysis for the purpose of activating safety measures. Before a true threat occurs, you can use data from monitored traffic to pinpoint indicators of network compromise. Then, you can effectively counter the potential danger. No person or organization can predict the future, but training for azure cloud practitioner certification provides the foundation, tools and insights to help you advance your security posture in the cloud.

3. Statistics and Applied Mathematics

Math is applicable to most industries, and cyber security is no exception. In addition to basic binary math, a statistics background gives you the foundational skills for analyzing complex and high-dimensional datasets, so you can implement strategies to solve real-world problems. Most successful information scientists are well versed in applied mathematics, statistics, computer science or economics.

4. Hadoop and Spark

The most used big data processing frameworks today are Apache Hadoop and Apache Spark. Hadoop handles large datasets in a distributed manner, by using MapReduce to split data into blocks, assigning the chunks to nodes across a cluster. Then, the data is processed and stored. Spark is an open-source tool that can run independently or on other platforms. It performs different types of large data jobs, including batch processing, stream processing, machine learning, graph computation and interactive queries and can integrate with deep learning framework libraries like TensorFlow and PyTorch.

When it comes to cyber security, these platforms allow you to collect all the big data your company generates. You can then accurately analyze the information for anomalies and odd behaviors that could threaten your network.

5. Deep Learning Frameworks

Deep learning frameworks provide tools for designing, training and validating deep neural networks via a top programming interface. Deep learning is particularly important to understand when you’re trying to define patterns from unstructured data. One of the most critical applications for deep learning algorithms is to improve solutions in cyber security, some of which include the following:

•    Malware

•    Data breach

•    Phishing

•    Insider threats

•    Denial-of-Service (DOS) attack

Network security applications work to detect these malicious activities and prevent cyber criminals from accessing and attacking systems.

6. Databases

There are two types of databases: SQL (relational) and NoSQL (non-relational). The difference between the two has to do with the type of information and how it’s stored.

Relational databases are written entirely in structured language like phone books, whereas non-relational databases are document-oriented, like personnel files. Because there’s no comprehensive solution for data storage, many businesses and organizations rely on both SQL and NoSQL platforms for different tasks. Cyber professionals work to protect and secure databases from deliberate attacks, as well as data misused by those accessing these systems.

7. Machine Learning and Neural Networks

Machine learning refers to developing algorithms to make decisions from analyzed data to improve the accuracy of predictive models. Neural networks refer to groups of algorithms in machine learning that perform calculations similar to neutrons in the human brain, so computers can be trained to sort poorly defined problems. A prime example of a neural network is shown in face and speech recognition software. This modern data science enhances predictive and active intrusion detection systems by feeding historical and present data into a machine learning algorithm and using that information to detect potential threats. Eventually, the algorithm can predict future attacks.

Data science is a rapidly growing industry, and as technological advances continue, the demand for skilled data professionals in cybersecurity roles will continue to increase.

I am Lalitha Part time blogger from India . I Love to write on latest Tech Gadgets , Tech Tips , Business Ideas , Financial Advice , Insurance and Make Money Online

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