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99% of data scientists do this mistake #shorts #unfolddatascience #datascience



99% of data scientists do this mistake #shorts #unfolddatascience #datascience
Hello ,
My name is Aman and I am a Data Scientist.

About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.

If you need Data Science training from scratch . Please fill this form (Please Note: Training is chargeable)
https://docs.google.com/forms/d/1AcuamjqcAbVkWLN_RWdLMZbLYSGSfWMlJ8wn1VOpp3A/edit

Book recommendation for Data Science:

Category 1 – Must Read For Every Data Scientist:

The Elements of Statistical Learning by Trevor Hastie – https://amzn.to/37wMo9H

Python Data Science Handbook – https://amzn.to/31UCScm

Business Statistics By Ken Black – https://amzn.to/2LObAA5

Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron – https://amzn.to/3gV8sO9

Ctaegory 2 – Overall Data Science:

The Art of Data Science By Roger D. Peng – https://amzn.to/2KD75aD

Predictive Analytics By By Eric Siegel – https://amzn.to/3nsQftV

Data Science for Business By Foster Provost – https://amzn.to/3ajN8QZ

Category 3 – Statistics and Mathematics:

Naked Statistics By Charles Wheelan – https://amzn.to/3gXLdmp

Practical Statistics for Data Scientist By Peter Bruce – https://amzn.to/37wL9Y5

Category 4 – Machine Learning:

Introduction to machine learning by Andreas C Muller – https://amzn.to/3oZ3X7T

The Hundred Page Machine Learning Book by Andriy Burkov – https://amzn.to/3pdqCxJ

Category 5 – Programming:

The Pragmatic Programmer by David Thomas – https://amzn.to/2WqWXVj

Clean Code by Robert C. Martin – https://amzn.to/3oYOdlt

My Studio Setup:

My Camera : https://amzn.to/3mwXI9I

My Mic : https://amzn.to/34phfD0

My Tripod : https://amzn.to/3r4HeJA

My Ring Light : https://amzn.to/3gZz00F

Join Facebook group :
https://www.facebook.com/groups/410222213093826/?ref=bookmarks

Follow on medium : https://medium.com/@amanrai77

Follow on quora: https://www.quora.com/profile/Aman-Kumar-601

Follow on twitter : @unfoldds

Get connected on LinkedIn : https://www.linkedin.com/in/aman-kumar-b4881440/

Follow on Instagram : unfolddatascience

Watch Introduction to Data Science full playlist here : https://www.youtube.com/watch?v=Zkyog5u1OGw&list=PLmPJQXJiMoUWXbjyedFTmXPzzoeMJV4fe

Watch python for data science playlist here:

Watch statistics and mathematics playlist here :

Watch End to End Implementation of a simple machine learning model in Python here:

Learn Ensemble Model, Bagging and Boosting here:

Build Career in Data Science Playlist:

Artificial Neural Network and Deep Learning Playlist:

Natural langugae Processing playlist:

Understanding and building recommendation system:

Access all my codes here:
https://drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M

Have a different question for me? Ask me here : https://docs.google.com/forms/d/1ccglokA_FicF3AirR-xllXauKZrzmngJoUptza1tPRE/edit

My Music: https://www.bensound.com/royalty-free-music

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5 thoughts on “99% of data scientists do this mistake #shorts #unfolddatascience #datascience

  1. Hi Aman, kindly make end to end project video with same train test split in initial state and then performing encoding, handling missing value, outlier basically all feature engineering and selection technique.

  2. Hi Aman

    One doubt here did u meant we need to split it beginning itself

    Like once we read the dataset we need to split it to train and test even before handling missing values ?

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