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Data Science Vs Cyber Security Vs Artificial Intelligence
The importance of remote operations and flexibility of the workplace is rapidly increasing in recent times in line with the countless advancements in technology. The whole world is busy in up scaling and altering the existing technologies to fit the current developments and scenarios.
It could be quite challenging for a student who has just completed his Bachelors to choose the perfect course of study with ever evolving technologies and options.
This can be your guide to find the perfect stream of study. The following are the basic questions you ask yourself before applying for an admission in a University:
What is the fundamental difference between them?
Data Science: Data Science is an amalgamation of usage of various algorithms, programs, procedures and systems to interpret the patterns of large volumes of structured and unstructured data. Thereby, extracting meaningful comprehensions to base important business decisions.
Cyber Security: Cyber Security is to secure the computers, mobiles, other electronic devices, networks and the data from malicious attacks. It is usually called as Information ...
... security and can be used in different field like business, network security, application security, mobile computing, etc.
There are various types of cyber threats like cyber-crimes, cyber-attacks and cyber terrorism which are performed through various malwares (virus, Trojans, spyware, Dridex, adware, and botnets), SQL Injection, Phishing, denial-of-service attacks.
1. Artificial Intelligence:
Artificial Intelligence is to make a computer or a computer-controlled robot or a software to adapt, learn and anticipate the human behaviors and think intelligently like a human mind. This can be attained by studying the human behavioral patterns and analyzing using a cognitive approach. The end result will be a smart technology, intelligent software, applications and systems.
The different types of AI are purely reactive, limited memory, theory of mind and self-aware. AI can be applied in various areas such as online customer support, cyber security systems, banking fraud detection software and virtual assistants.
2. What are my strengths?
Self-reflect on your strengths depending on your grades secured in each unit in Bachelors, lab scores, interests, passion, future goals, estimated salary, job prospects, likes and dislikes. Compare the pros and cons for each field of study to arrive at the most desirable field of study for you. Make use of SWOT analysis to arrive at your Strengths, Weaknesses, possible Opportunities and Threats for each interest field of study. Identify your stronger points such as Mathematics, statistics, probability, programming, networks, robots, machine learning, etc.
3. What is the right course for me?
Once you know what your strengths are it is a cake walk to pick the perfect fit for you. Once you pick your path, ensure it is has a great scope in the future to meet your desired goals.
The four common foundation stones for an expert data scientist are business/domain knowledge, statistics, programming and communication. If you think you are interested in all the above areas, you may go for this course.
If your interest lies in programming, python, algorithms, machine learning, Computing structures or autonomous robots, you may go for artificial intelligence.
If your interest lies in defending various platforms from cyber-crimes, information security, cyberspace structures and cryptography, you may choose cyber security.
4. What are the future job prospects and average pay-scales?
Salaries will be based on experience, location, skills and company.
Data Science: $113K per annum.
MIS Reporting Executive
Cyber Security: $95,954 per annum
Chief Information Security Officer
Forensic Computer Analyst
Information Security Analyst
IT Security Engineer
Artificial Intelligence: $114K per annum
AI Data Analyst
Machine Learning Engineer
Business Intelligence Developer
Big Data Engineer
5. How to choose the best country or University for your desired goal?
Understand the local market for your specific field and course.
Research on the strengths of the country and job opportunities in the specific field to become employable after the completion of the course.
Choose the country depending on various factors like location, quality of education, market, future opportunities from the current trend, average expected salary for your field, travel, favorite football team, etc.
Next, choose the University depending on its world ranking (The Times Higher Education rankings and QS World University Rankings), country ranking, TEF (Teacher Excellence Framework) for teaching quality in that University, REF (Research Excellence Framework) for PHD or research courses, location, infrastructure, student support, placements, scholarships, travel, etc.
Some of the best Universities in the world to study DS:
Imperial College London, U. K.
The University of Texas at Austin, U. S. A.
University of Warwick, U. K.
University of Melbourne, Australia.
University of Southern California (USC), U. S. A.
University of Edinburgh, U. K.
Some of the best Universities in the world to study CS:
University of Birmingham, U. K.
University of Adelaide, Australia.
Swinburne University, Australia.
University of Technology, Sydney, Australia.
George Mason University, U. K.
University of Essex, U. K.
Some of the best Universities in the world to study AI:
University of Cinncinati, U. S. A.
RMIT University, Australia.
Deakin University, Australia.
Oregon State University, U. S. A.
University of Surrey, U. K.
University of Stirling, U. K.
Article From https://edmium.com
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