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7 Data Science Myths That Should be Avoided at all Costs


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7_Data_Science_Myths_That_Should_be_Avoided_at_all_Costs

Table of Content

 

Data science has grown into a lucrative field for many techies, data scientists, and those who love to dabble in coding. In fact, Stitch suggests that in LinkedIn alone, about 11,400 data scientists are employed in companies known to the professional social media giant.

But what is data science?

Well, data science is what IBM calls a combination of the following to “uncover and explain the business insights buried in data”:

  1. The scientific method
  2. Math and statistics
  3. Specialized programming
  4. Advanced analytics
  5. Artificial intelligence (AI), AND
  6. Storytelling

With that said, there are many myths that can stem from this miraculous part of technology. But don’t panic! We’re here to dispel 7 of the most common myths in data science:

Myths

Myth #1: You need to know how to code to work in Data Science:

Data science is (wrongly) assumed to be synonymous with coding. Although it is definitely an important skill to try and learn how to code, proficient coding is not a necessary skill in order to get started in Data Science. There are options available however in the field that do not require those previous skills; even though prior coding knowledge is helpful.

 

Myth #2: You have to be nerdy and studious in order to go into Data Science:

Data Science is more than just having your nose in a book all the time. Similar to not needing to know coding, you do not need to have grown up as the nerdy or geeky kid to progress into data science as a career. Data science is centred around teamwork and using different people’s strengths and learning from each other. A lot of it is about learning as you go from the people around you.

 

Myth #3: Data Science is not crucial in the world:

Believe it or not, Data science is not a well-known industry, and some people can think it is not crucial in the world we live in today. Data and information have become the most important currency in the 21st century. The ability to break down, analyse, and understand data and information as well as extract it is a crucial skill in the world today.

   

Myth #4: You need a PhD

Getting a PhD is an incredible thing. Although the achievement of a PhD will definitely propel your career (especially in data science); it is not necessary for an applied data science role. Harley Rebecca, a technical writer at State of writing and Boom Essays, noted that, “If you are wanting to start a career in Data Science research, it is almost impossible to get into a research role without holding a previous qualification from that standard of education or a lot of experience in the field prior.” You are more likely to need a PhD if you are wanting to get into the research side of data science. However, roles available in applied data science do not necessarily require a PhD.

 

Myth #5: Deep Machine Learning can only be done in Big Companies

There are a lot of myths and stories (true and false) surrounding Deep Machine Learning. It is widely assumed that to achieve perfect deep machine learning tasks, you need to have big and expensive pieces of hardware, just like in the many sci-fi movies that you would see on a regular basis. But the good news is, all of that technological mumbo-jumbo isn’t needed in your quest to become a data scientist!

Lots of money and technology is not necessarily needed to achieve effective big machine learning and other data science achievements. It is possible to achieve machine learning and deep data science tactics even with a small to medium size business without billions of dollars.

 

Myth #6: AI Systems Adapt Instantly Once They are Built

The general public seems to assume (largely due to Hollywood films perpetrating this); that artificial intelligence systems become these ‘all knowing’ beings that will always be able to adapt to new situations without any prior knowledge. Jess Ursula, a tech blogger at Paper Fellows and Lia Help, commented, “Robots are not humans, and they are not able to adapt to new information and behave in the way that we want them to without us teaching them how we want them to behave.” Every time a new situation arises, the relevant code must be inputted to help the systems we use to adapt the way we want them to. Artificial Intelligence systems do not adapt instantly because they are not all-knowing and do not have general intelligence.

 

Myth #7: It is Necessary to have Previous Experience in Computer Science or Similar Industry:

This is the biggest lie amongst many industries today, and why many people are too afraid to jump industries without prior experience. The only way you are going to gain experience is to take a chance, and data science does not require background knowledge or experience all the time. A lot of knowledge can be gained on the job when it comes to Data Science, and it is absolutely not necessary to have prior experience to succeed in a data science role. The transition is absolutely possible between industries and can be achieved.

 

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Wrapping Up

So, there you have it!

As we’ve mentioned before in this article, there are many myths that can hold you back from pursuing one of the most lucrative fields in technology. So, remember:

  1. You don’t have to be a coding wiz to understand data science.
  2. You don’t have to be “nerdy” to understand data science.
  3. Data science is super important in this world.
  4. A PhD isn’t necessary to get a job as a data scientist.
  5. Big Companies aren’t the only ones reaping the benefits of data science.
  6. AI systems take time to adapt, since they process data that are fed to them. AND,
  7. No previous experience in computer science (or a related field) is necessary.

With these 7 myths debunked, we hope that you take the time to learn more about data science, and to embrace it as a career! Good luck!

7 Data Science Myths That Should be Avoided at all Costs

7_Data_Science_Myths_That_Should_be_Avoided_at_all_Costs

Table of Content

 

Data science has grown into a lucrative field for many techies, data scientists, and those who love to dabble in coding. In fact, Stitch suggests that in LinkedIn alone, about 11,400 data scientists are employed in companies known to the professional social media giant.

But what is data science?

Well, data science is what IBM calls a combination of the following to “uncover and explain the business insights buried in data”:

  1. The scientific method
  2. Math and statistics
  3. Specialized programming
  4. Advanced analytics
  5. Artificial intelligence (AI), AND
  6. Storytelling

With that said, there are many myths that can stem from this miraculous part of technology. But don’t panic! We’re here to dispel 7 of the most common myths in data science:

Myths

Myth #1: You need to know how to code to work in Data Science:

Data science is (wrongly) assumed to be synonymous with coding. Although it is definitely an important skill to try and learn how to code, proficient coding is not a necessary skill in order to get started in Data Science. There are options available however in the field that do not require those previous skills; even though prior coding knowledge is helpful.

 

Myth #2: You have to be nerdy and studious in order to go into Data Science:

Data Science is more than just having your nose in a book all the time. Similar to not needing to know coding, you do not need to have grown up as the nerdy or geeky kid to progress into data science as a career. Data science is centred around teamwork and using different people’s strengths and learning from each other. A lot of it is about learning as you go from the people around you.

 

Myth #3: Data Science is not crucial in the world:

Believe it or not, Data science is not a well-known industry, and some people can think it is not crucial in the world we live in today. Data and information have become the most important currency in the 21st century. The ability to break down, analyse, and understand data and information as well as extract it is a crucial skill in the world today.

   

Myth #4: You need a PhD

Getting a PhD is an incredible thing. Although the achievement of a PhD will definitely propel your career (especially in data science); it is not necessary for an applied data science role. Harley Rebecca, a technical writer at State of writing and Boom Essays, noted that, “If you are wanting to start a career in Data Science research, it is almost impossible to get into a research role without holding a previous qualification from that standard of education or a lot of experience in the field prior.” You are more likely to need a PhD if you are wanting to get into the research side of data science. However, roles available in applied data science do not necessarily require a PhD.

 

Myth #5: Deep Machine Learning can only be done in Big Companies

There are a lot of myths and stories (true and false) surrounding Deep Machine Learning. It is widely assumed that to achieve perfect deep machine learning tasks, you need to have big and expensive pieces of hardware, just like in the many sci-fi movies that you would see on a regular basis. But the good news is, all of that technological mumbo-jumbo isn’t needed in your quest to become a data scientist!

Lots of money and technology is not necessarily needed to achieve effective big machine learning and other data science achievements. It is possible to achieve machine learning and deep data science tactics even with a small to medium size business without billions of dollars.

 

Myth #6: AI Systems Adapt Instantly Once They are Built

The general public seems to assume (largely due to Hollywood films perpetrating this); that artificial intelligence systems become these ‘all knowing’ beings that will always be able to adapt to new situations without any prior knowledge. Jess Ursula, a tech blogger at Paper Fellows and Lia Help, commented, “Robots are not humans, and they are not able to adapt to new information and behave in the way that we want them to without us teaching them how we want them to behave.” Every time a new situation arises, the relevant code must be inputted to help the systems we use to adapt the way we want them to. Artificial Intelligence systems do not adapt instantly because they are not all-knowing and do not have general intelligence.

 

Myth #7: It is Necessary to have Previous Experience in Computer Science or Similar Industry:

This is the biggest lie amongst many industries today, and why many people are too afraid to jump industries without prior experience. The only way you are going to gain experience is to take a chance, and data science does not require background knowledge or experience all the time. A lot of knowledge can be gained on the job when it comes to Data Science, and it is absolutely not necessary to have prior experience to succeed in a data science role. The transition is absolutely possible between industries and can be achieved.

 

Planning to Hire Custom Software Development Company ?

- Enquire Today

 

Wrapping Up

So, there you have it!

As we’ve mentioned before in this article, there are many myths that can hold you back from pursuing one of the most lucrative fields in technology. So, remember:

  1. You don’t have to be a coding wiz to understand data science.
  2. You don’t have to be “nerdy” to understand data science.
  3. Data science is super important in this world.
  4. A PhD isn’t necessary to get a job as a data scientist.
  5. Big Companies aren’t the only ones reaping the benefits of data science.
  6. AI systems take time to adapt, since they process data that are fed to them. AND,
  7. No previous experience in computer science (or a related field) is necessary.

With these 7 myths debunked, we hope that you take the time to learn more about data science, and to embrace it as a career! Good luck!

Author Bio

Author

Author Name: Elizabeth Hines

Elizabeth Hines is a digital marketing expert and content writer at Assignment writer and Academized. In her articles she writes about latest tech and marketing trends, innovations and strategies. She also writes for online magazine and blogs, such as OX Essays.