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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 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.

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