Introduction To Data Science Mistakes
If you are thinking about starting your career or giving it a new turn then you must be careful about a few data science mistakes that generally people make while learning data science. It’s always good to avoid the below-mentioned general data science mistakes to make your learning more effective and useful.
Read our blog on Data Science Interview
We have broadly categorized these data science mistakes into 3 headings:
DATA SCIENCE MISTAKES WHILE LEARNING DATA SCIENCE
Generally, most people don’t know how to approach their learning to make it more precise and effective. So, they are just inclined more to the traditional ways of learning that are just time and energy-consuming.
1. Spending too much time on theory
- Firstly it’s very time-consuming to learn each and every line and understand them as well. so going for just the theory is very slow and daunting. These are very common data science mistakes.
- Secondly, being a human being to retain all the concepts and memorize them is next to impossible. Wherein, data science is more of a practically applied field and the best way to get hands-on experience in this field is just by practice. That’s where we make data science mistakes.
- Finally, if you don’t get what, when, and how it can be applied in the real world or what are its practical uses you will hardly get motivated to learn it further and you will find it a frustrating decision of your life. Last but not least, data science mistakes we tend to make.
2. Coding too many algorithms from the scratch
Being too engrossed in the theoretical world makes the students just a bookworm who doesn’t know the applicability. Today’s world of technology is more advanced and has mature machine learning libraries and cloud-based solutions making it more important to know how, where, and when to apply the required algorithms. It’s another common data science mistake.
3. Jumping to the dead end
If you are committed and desire to master your data science-specific field, then, you just not only have to make your fundamentals strong but also have to master these techniques of doing.
To avoid data science mistakes:
- Classical machine learning is the mother of all modern and advanced machine learning. This served as the building block for all advanced topics. So, at first, make sure to master the techniques and algorithms of this;
- Mature and advanced machine learning is already well developed but always remember classical machine learning still has incredible potential;
- Opt for a systemized way of learning as this is always the best approach to solve any analytical problems with any form of machine learning.
DATA SCIENCE MISTAKES WHEN APPLYING FOR A JOB
Although this set of data science mistakes isn’t going to trouble you in a short time span but will definitely cause longer terms as missing some great opportunities will be the costliest thing.
4. Having too much technical jargon in a resume
Technical jargon looks good only in the theoretical exams but suffocates your resume or gives the impression of an exam sheet. You can look for our other blog Perfect Data Science Resume Tips.
5. Overestimating the value of academic degrees
Industries always prefer/choose the candidates who are more experienced over the candidates who hold several degrees. As what we learn in an academic setting is simply too different from the machine learning applications of the real world.
6. Searching too narrowly
Make your search more optimized by searching not just for data science jobs or internships as many organizations are still evolving to accommodate the growing impact of data science.
To avoid these data science mistakes:
- Search by skills: like machine learning, data visualization, SAS, etc
- Search by job responsibilities: like A/B testing, Data Analysis, etc
- Search by technologies used: like Python, R, etc
- Expand your searches by job title: data analytics, data engineer, quantitative analysts.
DATA SCIENCE MISTAKES DURING THE INTERVIEW
You have already done your part of hard work, now it’s time to get your sweet fruit of this hardship. But one must be smart enough and well acquainted with the projects, jobs, and internships you have done.
7. Being unprepared to discuss projects
Applying for the data science roles naturally include elements of project and self-management that mean the aspirants/candidate must be well-acquainted with the workflow processes and able to get and use each piece of information/dataset in the required way.
Mentioning your projects in your resume avoids the interview questions like “how would you”. If the aspirant is asked this question and he is not able to explain what he/she has done in their previous projects then his position is at risk.
8. Underestimating the value of domain knowledge :
Other than you, there are many desirable and hopeful candidates waiting for the right opportunities. So, to make yourself stand out in a crowd, you must learn more about the specific industry you’ll be applying your skills to.
9. Neglecting communication skills :
Interviewers will always look for your ability and capability to communicate with colleagues having non-technical backgrounds. Still being in the growing and emerging stage, generally data science teams are very small compared to other managerial teams. So, in short, the candidate must be holding this skill.
Conclusion
In the above article, you have learned about how you can avoid the biggest and costliest 9 data science mistakes when starting your career in data science as a beginner.
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