CAPITAL CORP. SYDNEY

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Contact Person: Callum S Ansell
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P: (02) 8252 5319

WILD KEY CAPITAL

22 Guild Street, NW8 2UP,
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E: matilda.uk@capital.com
P: 070 8652 7276

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P: 030 62 91 92

Top 3 Data Scientist Myths

AI-Learning, Industry News, People

Myth 1: Data Scientist/Engineer/Analyst are one and same.

To put things in perspective, a Data Scientist is someone who has experience and knowledge in at least 2 of these 3 fields, Statistics, Programming and Machine Learning. Primary expectation of such an employee is to be able to work on a challenging business problem where he/she can use their knowledge to find solutions. Such a person would love to spend a major portion of their work in building predictive models and performing statistical experiments to obtain a working solution. It’s a mixture of a research and a programming job, and the nature and workload differs depending on the size of the company/team.

Myth 2: Degree in Data Science=Data Scientist.

If you think it just takes a degree to become a data scientist, then think again. A master’s degree will get you closer to your goal, but it ‘s not a final destination. Working with real data, involving real people, is a lot different than working with hypothetical scenarios in school. In order to call yourself a DS, you’ve got to dive into the real world. If you’re lucky, you might step into a DS position right away, but you won’t actually be a real DS until many months later. You probably won’t be a good DS until several years after that, when you know everything about the particular data you’re working with. 

Myth 3: All you need to learn is a tool to become a Data Scientist.

Data science requires a combination of multiple skills. Programming is not at the centre of the data science spectrum – it is just one part of a whole. Let’s divide the spectrum of skills into two parts:

  • Technical qualities
  • Non-technical qualities or soft skills

 

Technical qualities

Understanding how a certain technique works will help you become a better data scientist. This is why we encourage everyone to learn algorithms from scratch. Learn how changing a certain parameter will impact the final model. This will eventually pay off when you’re working on a large-scale project in the industry.

The margin for error and experimentation is slim where stakeholders come into the picture. We have plenty of articles on our blog explaining machine learning and deep learning techniques from the ground up. Go through them and try to understand and replicate the code yourself.

It will be an invaluable addition to your skillset.

 

Soft Skills

Soft skills often get overlooked by aspiring data scientists. They certainly aren’t taught in any online courses or offline classrooms. And yet these are qualities interviewers look for.

  • Problem-solving skills
  • Structured thinking
  • Communication skills

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