8 common mistakes of an amateur Data Scientist

22nd November 2018

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Coming from a non-technical or non-mathematical background, you may have relied on online courses and books to get a heads-up as a data scientist. This knowledge acquisition process is not completely helpful because the industry requires much more than what these resources teach.

The demand for data scientists is huge but getting a real job will take more than self-education. Le’t investigate how you can avoid the most common mistakes.

1. Juggling Multiple Tools At Once

While having knowledge of most data science tools is a plus, it’s still not a good idea to rush them all in at once. Each tool comes with enough technicalities and unique qualities, learning them at once will complicate your assimilation process.

To be on the safe side, pick one tool and stick to it until you exhaust the knowledge. If you get stuck, ask questions from people who have mastered the skill.

2. Overusing Data Science Terms In Your Resume

Your resume should represent your capability, it should also be as simple as possible. If a large aspect of your resume is filled with unnecessary data science terms like Decile, Ggplot2, LightGBM, you might not get a callback.

Just like any other good resume, let it be simple enough and express its major purpose, which is to get you a job. Emphasise on your interest in the industry and prospective impact you can have on the company as a data scientist.

3. Comparing Machine Learning Competitions With Real-Life Jobs

If you have a sizable interest in how data science works, you should have heard about hackathons and Machine Learning competitions. Hate to break it to you, but what you see outside the data workspace isn’t exactly the same with what happens while doing the real work.

Contrary to data scientist amateur beliefs, there is a work process that involves a bunch of people and you’d mostly be required to work with messy data. This part of the job can be tricky, time-consuming and most especially, less fun. Also, accuracy isn’t always achievable – you will learn this while acquiring experience. You can always reach out to people already in the industry or dig through the internet for more information.

4. Exclusive Dependence On Degrees And Certifications

Degrees and certifications are a good start but will never be enough. What gets you the job is putting in the work, practicalizing what you’ve learned and applying it to the real world. Above all, you need an in-depth understanding of how a data science project lifecycle works and how it can reflect on business success.

5. Learning Theoretical Concepts Without Applying Them

Ever heard of the saying, Practice makes perfect? It totally applies to data science. There’s so much to learn as an aspiring data scientist but you need to find a dataset or a problem, where you can apply everything you learn too.

Practicing helps you retain the knowledge.

6. Partially Ignoring The Practical Aspect Of Exploring And Data Visualization

Data visualization is a distinct feature of data science, it’s a terrible idea to omit it and get to the model building stage. The most important task you have is to understand your data, this will reflect on your result. Data visualization is the best way to present your findings to the client.

Exploring data will require your undivided attention and ability to try out different charts – this will help you get a deeper knowledge of the problem. Curiosity is what broadens your knowledge of data science.

Again, remember that competitions are different from real jobs.

7. Learning Machine Learning Techniques Without Having Knowledge Of The Prerequisites

Whatever got you interested in this field must be enough to keep you going. There’s a lot more attached to getting ahead as a data scientist and missing one step might cost you a promotion.

To quench your curiosity, you need to get a heads up on these fields before launching a Machine Learning spree:

  • Calculus
  • Probability and Statistics
  • Graph Theory
  • Linear Algebra
  • Any programming language – Python, etc.

8. Underestimating Communication Skills

If you can’t explain your analysis to your client, your work is as good as void. Communication skills are vital in any workspace – your relations with team members and clients cannot be ignored.


Read also: Is Your CV Machine Ready?

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Oluwatobi Ogunrinde

A passionate writer hoping to educate people with her work. Oluwatobi enjoys writing about entrepreneurship and work culture. When not writing, you will find her reading about international politics.

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