5 LPA to 50+ LPA in 2 YEARS ๐Ÿ”ฅ! Ultimate DATA SCIENTIST @ Microsoft ๐Ÿ”ฅ FRESHER Cracked SENIOR Role โค๏ธ

    12
    30



    ๐Ÿ”ด Register for CodeKaze test, 30 Lakh Prize – https://bit.ly/3sL1LG5

    ๐Ÿ”… Follow Me On Instagram – https://www.instagram.com/_shashank_219/

    ๐Ÿ“ฑ ๐—–๐—ผ๐—ป๐—ป๐—ฒ๐—ฐ๐˜ ๐—ช๐—ถ๐˜๐—ต Prashant :
    ๐Ÿ”ด LinkedIn – https://www.linkedin.com/in/prashantg445/

    ๐—๐—ผ๐—ถ๐—ป ๐—บ๐—ฒ ๐—ผ๐—ป ๐—ฆ๐—ผ๐—ฐ๐—ถ๐—ฎ๐—น ๐— ๐—ฒ๐—ฑ๐—ถ๐—ฎ:๐Ÿ”ฅ
    ๐Ÿ”…Shashank LinkedIn – https://www.linkedin.com/in/shashank219/
    ๐Ÿ”…Shashank Instagram – https://www.instagram.com/_shashank_219/
    ๐Ÿ”…Telegram Group – LearningBridge
    โญ•Discord Server – https://discord.io/shashankELB
    ๐Ÿ”ดJoin for 1×1 Mentorship, Mock Interviews, Career Guidance, Resume Building, Interview roadmap and resources to crack tech, BigData/Data Engineering tutorials from scratch :
    https://www.youtube.com/channel/UCBGcs9XTL5U34oaSn_AsHqw/join

    ๐——๐—ถ๐˜€๐—ฐ๐˜‚๐˜€๐˜€๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€:

    โœ… 0:00 — Inspiring Podcast Precap
    โœ… 1:13 — CodeKaze Test for 30 Lakh Prize
    โœ… 2:01 — Prashant’s Introduction
    โœ… 3:05 — Data Science Journey From College
    โœ… 6:16 — Microsoft Interview Experience For Data Science Role
    โœ… 10:27 — Data Science Guide For Freshers
    โœ… 14:34 — Best resources to follow for Data Science
    โœ… 16:42 — is Coding important for Data Science Role?
    โœ… 19:19 — How to get more interview calls?
    โœ… 20:43 — Myths about Data Scientists
    โœ… 23:13 — Data Scientists Salary

    #microsoft #datascientist #InterviewExperience

    source

    Previous articleCidade Inteligentes: big data e seguranรงa de dados – parte 1
    Next articleQual devo usar para minhas automaรงรตes ? CloudFormation, Terraform, AWS SDKs ou AWS CLI ?

    30 COMMENTS

    1. Curating all the resources that I mentioned in the video:

      Prerequisites:
      Basic level Math (mainly probability, linear algebra, differential calculus, statistics)
      Basic level Python (can learn from anywhere & practice few coding problems on it)

      Tools to play with data:
      (Kaggle will be really helpful here not just in terms of datasets, but it's EDA discussions)
      SQL
      Numpy
      Matplotlib
      Pandas (for tabular data)
      Opencv (for image data)
      Satosa (for understanding image kernels)
      NLTK (for text data)

      ML:
      Statquest YT channel (for understanding algorithms)
      Scikit-learn package (for all essential preprocessing and ML implementations with a nice doc)

      Deep Learning:
      Deep learning specialization (5 courses) by Andrew NG
      TF playground (for understanding basic working of neural networks)
      Stanford CS 231 course by Andrej Karpathy (I did it at that time, maybe some better courses are available now)
      Stanford 224 (NLP)

      I generally rely on blogs for everything. You may follow blogs like:
      Jay Alammar
      Colah (best for lstm)
      Sebastian ruder (word embeddings and gradient descent optimizers)
      MLWhiz (ML fundamentals)

      YT channels for Interview practice:
      Krish Naik
      Data Science Jay
      Nick Singh (on linkedin)

      For finding best available model in any field: Paperswithcode

      I can go on and on but this list will never end. And once you become familiar enough, you don't need any further suggestions. Keep hustling !!