Computer Science is 21st-century mathematics. It is one of the vast fields around the globe. It is getting crazier day by day like crazistan.The movies are getting more dramatized. People make an array of computer science in which hacking in top archives and complex games only fall.

Apart from that, I started my undergrad in computer science. I spent my first half endlessly and worrying about where am I good at. However, people might work as a content writer for crazistan like I do. Whatever, If you are being paid good money no one really cares. It is a blessing in disguise.

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My whole experience sums up in these lines: The greatest enemy of knowledge is not ignorance, it is illusion of knowledge.

Secondly, every one of you has some major key like DJ Khaled for your future. Myself wants to shop Harrods and Billionaire. I crave to accelerate supercars. I like to party like the Caribbeans on my yacht with some Cuban cigars in the Pacific ocean.

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I get disappointed, who’s gonna hire me. To achieve goals of my life, I have to select my niche and for you as well. Faster you select better you feel. Now things have fallen into place. There are some major keys for your future I listed down.

But still, we would suggest you not lose hope in the first place. Because, being “genius” is not the requirement of this field, but being “determined” is the key. These are some of the fields you should consider opting.

1. Next Generation Robotics

 

Now, the trend of manufacturing lines has changed to small industry almost consist of a room. Just like the smartphones, using GPS technology, robots are going to work as an investigator, in agriculture for harvesting and weed control.

Moreover, in Japan, robots are being trialed in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs. Similarly, Smaller and more dextrous robots, minimizing the human labor with the help of simple controllable programs.

 

2. Artificial Intelligence

The most recent example, related to Artificial Intelligence is Sophia

In simple words, its the science of doing things that human can, using a computer. Sophia is the latest example of AI in front of us. In the meantime, when we rewind a little back our smartphones were AI enabled, like speech recognition. Automatic cars are also the demonstration of AI. Mercedes will play a roll in brighter future (Click here).

Drones based on AI, are in testing phase.

3. “Sense and Avoid” drones

Unmanned Aerial Vehicles (UAVs) are now used in various military fields including reconnaissance, environmental monitoring, border patrol, search and rescue operations, disaster relief, tracking, and monitoring. Furthermore, these applications require the small and agile UAVs to fly at a lower altitude or operating inside buildings, which are exposed to many hazards and obstacles. However, current UAVs technology in automatically sensing, detecting and avoiding fixed obstacles such as power line, building, tower, tree, and moving obstacles of birds and other aircraft is still immature compared to manned aerial vehicle. In the next decade, UAVs’ market is expected to be rapidly growing in a handful of civilian and commercial industries such as film production, news and media, utilities, agriculture, energy, mining, construction, and real estate.
In January 2014, Intel and Ascending Technologies showcased prototype multi-copter drones that could navigate an on-stage obstacle course and automatically avoid people who walked into their path. The machines use Intel’s RealSense camera module, which weighs just 8g and is less than 4mm thick. With reliable autonomy and collision avoidance, drones can begin to take on tasks too dangerous or remote for humans to carry out: checking electric power lines, for example, or delivering medical supplies in an emergency.

4. Neuromorphic technology

The neuromorphic technology works as biological neural networks as analog or digital copies on electronic circuits.

Goal:

  1. Offering a tool for neuroscience to understand the dynamic processes of learning and development in the brain and
  2. applying brain inspiration to generic cognitive computing.

Gradually, the idea of neuromorphic chips has been around for decades, but the technology may finally be ready to find its commercial niche. Across the tech industry, progress in AI has inspired new research into hardware capable of using machine-learning algorithms more efficiently.

 

5. Digital Genome

Genome, as a matter of fact, would seem to be familiar with DNA kind of a thing. Yes, its the genetic material present in organisms, which vary person to person. Presently, we would appreciate the Computer Science experts who took the step to make it so easy for the medical department, to take better decisions for the purpose of saving one precious life.

Working:

Additionally, it is possible to save your DNA in your USB drive. Doctors can edit your genome to fix certain issues. For example, if someone has cancer, with this new technology doctors can look at the genetic makeup of the tumor and figure out what treatment will be most effective for that patient. Therefore, editing the genome has risks if something goes wrong though. So, it won’t be something you will see often in the next couple of years. Nevertheless, the pros definitely outweigh the cons overall.

 

6. Data Mining (Big Data)

craziest computer science fields

On the other hand, the study of algorithms for finding patterns in large data sets. The analytic process designed to explore data (usually large amounts of data – typically business or market-related – a k a “big data”) in search of consistent patterns and/or systematic relationships between variables. From time to time,  validate the findings by applying the detected patterns to new subsets of data.

Goal

The idea behind data mining is prediction – and predictive data mining is the most common type of data mining.

Steps of Collecting Big Data:

The process of data mining consists of three stages:

  • The initial exploration,
  • Model building or pattern identification with validation/verification, and
  • Deployment (i.e., the application of the model to new data in order to generate predictions)

7. Mobile Computation

Mobile Computation is, sometimes, referred to as “human computer interaction”. It is basically the transportation of data (photos, videos, documents, etc) over a network via mobile devices. That allows people to access their information and data from anywhere. However, this has removed the boundary of devices to be connected physically. Since the media is unguided/unbounded, the overlaying infrastructure is basically radio wave-oriented (RWO). Therefore, the signals are carried over the air to intended devices that are capable of receiving and sending similar kinds of signals.

Benefits:

  • Personalization: can alter your mobile computing to your individual needs.
  • Connectivity: stay connected to all sources at all times.
  • Social Engagement: interact with a variety of users via the Internet.

Technologies

  • Mobile Devices
    • Laptops
    • Wearable computers: Apple Watch
    • Smart-phones
    • Tablets
  • Wi-Fi (or hot-spot)
  • Cloud Computing

8. Data Analyst

The job is all about collecting, organizing, and interpreting statistical information to make it useful to a range of businesses of an organization. Secondly, a data analyst is someone who scrutinies information using data analysis tools. The meaningful results they pull from the raw data helps their employers or clients make important decisions by identifying various facts and trends. Moreover, This is supposed to be the most important person in the organization, which either can make the organization like a shining star.

Skills

  • Strong skills in code like SQL and Oracle
  • The ability to analyze, model and interpret data
  • A high level of mathematical ability
  • Strong problem-solving skills
  • Excellent IT skills

 

9. BioInformatics

As clear from the name, the field is related to biology. Similarly, in a very interesting manner that develops methods and software tools for understanding biological data. It combines Mathematics (Statistics), Computer Science, Biology, and Engineering to analyze and interpret biological – data.

Use:

  • Identification (a goal of better understanding the unique adaptations, desirable properties (esp. in agricultural species), or differences between populations) of candidate genes
  • Single nucleotide polymorphisms (SNPs).

Analyst:

They are skilled in the use of complex algorithms, computer databases, and software. Study information received from the Human Genome Project (HGP), a research project that determines the pairs that create DNA, in an effort to develop cures for human diseases like cancer etc.

10. Computer Vision

Last but not the least, the field is just like raising a child. Secondly, whatever human sees or visually sense using brain, analyze and take a decision on that basis, is now has transferred to Computers. Following, it is mainly concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

Application:

  • industrial quality inspection
  • face recognition
  • gesture analysis
  • agriculture
  • augmented reality
  • autonomous vehicles
  • biometrics
  • character recognition
  • forensics
  • geoscience
  • image restoration
  • medical image analysis
  • pollution monitoring
  • process control
  • remote sensing
  • robotics
  • security and surveillance
  • transport

Softwares:

  • OpenCV
  • CVIPtools
  • Integration Vision Toolkit

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