Don’t do these DATA SCIENCE Mistakes

Beginner Mistakes :

Spending a lot of time on theory.
Jumping directly into coding ML algorithms without learning the prerequisites.
Thinking to build the future without knowing the basics.
Not spending enough time on exploring and visualiz…


This content originally appeared on DEV Community and was authored by SOMYA RAWAT

Beginner Mistakes :

  • Spending a lot of time on theory.
  • Jumping directly into coding ML algorithms without learning the prerequisites.
  • Thinking to build the future without knowing the basics.
  • Not spending enough time on exploring and visualizing the data.
  • Focusing on accuracy over understanding how the model works.
  • Assuming the algorithm is more important than domain knowledge.
  • Not having a structured approach to problem-solving.
  • Learning multiple tools at once.
  • Not learning/working consistently.
  • Less communication.

Intermediate Mistakes :

  • Data Leakage.
  • Sampling Bias.
  • Too many redundant features.
  • Bad Coding style.
  • Unavailability of features in future.
  • Not performing proper testing.
  • Not Choosing the Right Model- Validation Frequency.
  • Choosing the wrong tool to visualize.
  • Paying Attention Only to Data.
  • Building a Model on the Wrong Population.
  • Ignoring the probabilities.
  • Data Analysis without a Question/Plan.
  • Don't Sell Well.

Mistakes to avoid while applying for jobs :

  • Do Not Lie.
  • Using too many Data science Terms in your Resume.
  • Overestimating the value of academic degrees.
  • Do not narrow your search.
  • Competitions are not Real-Life.
  • Your LinkedIn profile is sacrosanct.
  • Being unprepared to discuss projects.

Mistakes to avoid during Interviews :

  • Not asking enough questions.
  • Discussing the old projects.
  • Not considering the business impact.
  • Not good at technical skills.
  • Not being a problem solver.
  • Not thinking from the interviewer perspective.
  • Not supporting your statements with stats and facts.
  • Failing to convey how you will help the company.
  • Forgetting The Requirement.
  • Not Using “I don't know" Judiciously.
  • Focusing on Answer Rather than Approach.
  • Not Taking the Opportunity to go into Details.
  • Taking Failure Personally.

10 wrong reasons to become a DATA SCIENTIST :

  • You Think Its Easier to get a Job.
  • No Interest in Coding or Programming.
  • Your Primary Reason is Money.
  • You Hate Math.
  • An Overall Lack Of Passion.
  • You Find Working With Data Annoying.
  • Consistent Learning Is Boring.
  • Lack of Communication Skills.
  • Hate Collaboratively Working in a Team.
  • Exploration And Working On Newer Projects does not really appeal to you.


This content originally appeared on DEV Community and was authored by SOMYA RAWAT


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