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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