what are the issues or challenges as data scientist?
Did working as data analyst help you in your data scientist career?
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finding real sense out of data
data was not in standard format
DS do experiment
analyst need to give
key for DA
stat n probab shud b gud strong fundamentally
SQL
experiment
good hypothesis
visualization
DA is more client facing
DS shud experiment,maths, decision making
maojrity of time is spend on obersving the data.
major difference is maths
DS shud have great maths skills
Joined Scaler Nov 2021
Worked before as DA then applied scientist now.
Crack Interview
DA – Numpy, panda, python and sql, sql window function.
Think question in terms of company’s service or product you are apply.
SqL – helps to extract data and connect with visualization
Practice your coding on your notepad.
show your dashboard in interview by picking unique dataset and solve great problems.
prob and stats must.
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DS interveiw
Fundametnal of ML modeling
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Book Recommendation – Atomic Habit
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Swtiched off distraction
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Make learning habit..
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create your coding in such a way that can be easily portable in any project.
EDA
it saves lots of time in coding.
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come up with bullet journal..
why do you want to do this ?
why you are doing it ?
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makes notes of all classes
being consistent
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Technical round –
basic of numpy, panda, sql
rapid fire question based ML
try to ask more relevant to answer right specifc question which is expected by interviewer.
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sometimes its not about right answer, its about approach to answer in multiple way.
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mock interview helped in communication, got to know the shortcoming, even if we know answer we should be able to communicate well in structured way.
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notes will help in revising fast
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ready portable code
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What did you answer when asked why do you want to pursue data scientist path or career?
Withdraw My Question
Asked 5 minutes ago
1
You
Did working as data analyst help you in your data scientist career?
Withdraw My Question
Asked 16 minutes ago
1
You
What are the issues or challenges faced by a data scientist?
Arun MV
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for someone who is transitioning into data science and wants to become a data scientist, would you recommend getting into an analyst role first? or aim for a data scientist internship ?
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level.fyi – check salary
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maang – dsa seriously
startup – dsa is secondary
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real in free time
create own project
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setup git hub profile
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dont wait for experience and showcase or brag in interview
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presentation skill
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day to day problem can be solved in project
participate hackathon or leetcode and get good rank and show and tell in interview
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90% – done by data engineer
10% done by DA and DS
But the work needs to be impactful
DA is client facing and more spotlight
DS – in the corner of office and nobody will notice you
you need to think a lot
you need to experiment
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some company give more value to DA and some to DS.
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you can crack DS role as a fresher also.
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collect own data set using web scrapper and make dashboard based on that.
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Networking through slack and linkedin for referrals
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Look at the JD carefully.
Apply for experienced jobs even if you don’t have experienced, they might pick you up.
If you have enough projects to gain experience then number of years experience won’t matter much.
Gain experience equavalent to high number of years experience.
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AI can’t remove job, we will just move to more skillful jobs.
You need to constantly upgrading yourself to be relevant to the industry.
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Wikipedia is a good source to learn statistics and maths.
Medium blogs will also help you.
Find new concept in stats or maths and apply.
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Working as DA helped me in DS job.
You can become better DS if you have worked as DA before.
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dATA has a story
you need to be patience to listen ot the story.
you are tied with the data
data is the next oil.
There are lots of open ended statement, you need to interpret the data,
take judgemental call
be risky
have maths behind you
its not very hard to master
everything is done t hroug coding
be strong in your fundamental
and try to experiment a lot.
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MLOP – combination of data scientist and software engineer.
data scientist – only building the model
MLOP – ML Engineer- putting the model into production
Ml gets more than DS
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write efficient code
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connect with me on LinkedIn