When a person hears the words data science they may get worried. A person needs to learn about math, programming, and even statistics. Many myths are related to data science and some of them are not true. These are 11 myths that are related to data science that a person does not have to worry about.
1. Ph. D is Mandatory
Getting this advanced degree will require dedication and a lot of effort. To work in data science a person may not need a Ph.D. to work in this field. There are two main roles for the data scientist. There is the applied data science role and there is a role in research.
The Applied data science role will require a person to understand existing algorithms and how they work. A Ph.D. is not required for this role. There are many job openings and most people do not even know about them.
To work in a research role a Ph.D. may be required. In this role, a person will need to create new algorithms and write in technical terms. It will also allow others to take their work seriously.
There is an opportunity cost when it comes to a Ph.D. IT is a huge commitment and it will show dedication to the field. Many well-respect data scientists do not hold this degree.
2. A Full-Time Data Science Degree is Need for Making a Transition in this Field
With all of the research going into this field, a person may be wondering how they can stand out from the others that are going for the same job. In data science, the experience is very important, and in some cases, it is more important than a degree. If a person is passionate about the field they learn a lot. There are plenty of resources online that a person can learn from. Some of them are free to use and are a structured learning program.
While there is not a lot of formal education in their field most people can learn through hard work and discipline. They can also get experience in the field. This is something that hiring managers are going to look at. Some of the leading data scientists do not even hold a college degree.
3. Work Experience
A person may have work experience in some field. They may think that they can bring this experience to the data science field. Not all work will carry over.
A person can change their focus on data science only. They may have some experience but it is not in the field.
If a person is looking to enter data science most of their work experience will not count. A person is going to a new line of work and in a new role. A hiring manager will look at the value a person can add to their company and they may not see any. A newcomer will not have experience in the field. They may not understand the impact their decision has on the field.
Many people think they can switch careers and stay in the same position. This is not the case.
If a person is in a related field and they are going to data science they may have an easier time getting a job. They know the industry and this will work as a benefit. They already have some knowledge and they will have some experience.
4. A Background in Math, Programming or Science is Needed
Most people in data science have a degree in computer science, math, programming, or a related field.
A person does not need a background in this. They will need to learn all of the concepts and some of them are rather technical. If a person is dedicated and hardworking they will be able to get into this field. A person can always learn this field if they are looking to get into data science.
5. Learning Tools is All that a Data Scientist Needs
Some think that learning Python or any related tool is enough to get into this field. If a person can write code they may think they have enough experience.
There are many skills needed to work in data science. There are technical skills and soft skills.
A person needs to understand the code and algorithms.
Some soft skills are needed such as problem-solving, communication, and structured thinking. A person will need the discipline to work on these skills too.
6. People can Only be Learning with Top Companies
Deep learning is a myth that only top companies have these resources and that all of the top resources are needed.
While deep learning models are effective they are not the only way to learn. Google has plenty of resources such as Google Colab that is a free cloud service for coding. There are also free GPU upgrades. This will allow a person to learn even if they do not work for a huge company.
7. AI Systems can evolve on their own
If a person is familiar with the movies they may think that AI systems will act like robots and will be able to evolve on their own. They will be able to grow and meet the needs of the user.
At this time AI cannot evolve on its own. This level of intelligence has not reached them yet.
8. AI and Employment
People often think that only the top scientists can work in AI. There are many different areas when it comes to AI and there is only a limited role that goes with the data scientist.
Some other roles can be used when developing artificial intelligence. There are many different engineering jobs, researchers, domain experts, and even business positons. A person can take some courses online to become familiar with AI and how they can play a role in this field.
9. Data Science is About Building Predictive Models
Data science can help make accurate predictions.
There are many layers to data science and they do follow a scientific model. The problem needs to be recognized and the problem statement is formulated. There is a hypothesis developed followed by the collection and verification of data. Then a model is built and tested. If there is a problem with the model the process restarts. In addition to the predictions, there is a lot of research that needs to be conducted.
10. Competitions is Real Life Experience
While competitions can be helpful they may not help during an interview. They often do not count as experience.
Data science competitions often do not have real-world value. They do not take the same time to collect and look at the data. There are the different methodologies used and this does not count as a real experience.
11. Data Collection is Easy
Data collection and verification takes a lot of time. There are many sources to review and verify. This is a huge piece and will take a lot of time. It is necessary to be successful.
There are many myths associated with data science. Hopefully, this brought some truth to the myths and gave people more clarification.