9 Common mistakes People usually make while learning Data Science

9 Common mistakes People usually make while learning Data Science

If you are thinking to start your career or give it a new turn then you must be careful about a few mistakes which generally people make while learning data science. It’s always being good to avoid the below mentioned general mistakes to make your learning more effective and useful.

We have broadly categorized these mistakes into 3 headings:



Nine Mistakes while learning data science

MISTAKES WHILE LEARNING DATA SCIENCE


Generally, most of the 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 :


Spending too much on theory

1. Firstly its 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;


2. Secondly, being a human being to retain all the concepts and memorizing 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;


3. Finally, if you won’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 frustrating decision of your life.


Balance your studies with more practical projects

2. Coding too many algorithms from the scratch :


Being too engross 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 have mature machine learning libraries and cloud based solutions making it more important and need for the hour to know how, where and when to apply the required algorithms.


Coding too much algorithms

3. Jumping to the deep end :


If you are committed and desiring 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.


Jumping to dead end

To avoid mistake:

  • Classical machine learning is the mother of all modern and advanced machine learnings. 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 being the best approach to solve any analytical problems with any form of machine learning.


MISTAKES WHEN APPLYING FOR A JOB


Although these set of mistakes won’t going to trouble you in a short time span but will definitively cause in longer terms as missing some great opportunities will be the costliest think.


4. Having too much technical jargon's in a resume:


Avoid too much jargon is your data science resume

Technical jargon look good only in the theoretical exams but suffocates your resume or gives the impression of an exam sheet. You can look our for our another blog Perfect Data Science Resume Tips


5. Overestimating the value of academic degrees


Industries always prefer/choose the candidates who is 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 real world.


Overestimating the value of academic degrees

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 this mistake:

· 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 : like data analytics, data engineer, quantitative analysts.


DURING THE INTERVIEW


You have already done your part of hard work, now its time to get your sweet fruit of this hardship. But one must be smart enough and well acquainted with your projects, jobs, internships you have done.


Common questions for a data science interview

7. Being unprepared to discuss projects :


Applying for the data science roles naturally include elements of project and self-management that means the aspirants/candidate must be well-acquianted with the workflow processes and able to get and use the each piece of information/datasets 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.

Project discussion in a data science interview

8. Underestimating the value of domain knowledge :


Other then you, there are many desirable and hopeful candidates waiting for the right opportunities. So, to make yourself stand out in crowd, you must learn more about the specific industry you’ll be applying your skills to.


Underestimating the value of domain knowledge in a data science interview

9. Neglecting communication skills :


Interviewers will always look for your ability and capability to communicate with the colleagues having non-technical backgrounds. Still being the in growing and emerging stage generally data science teams are very small compared to another managerial teams. So, in short the candidate must be holding this skill.


Neglecting communication skills in a data science interview

Conclusion :


In the above article you have learned about how you can avoid the biggest and costliest 9 mistakes when starting your career in data science as a beginner.


To hit the nail on the head, our course of data science training program at DataTrained is the best place to land on. DataTrained offers you the best course training allowing you to stand out in crowd- don’t be indifferent be the difference.

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