Data Science Pulse @datascpulse

Welcome to the Data Science Pulse, a one-stop resource for understanding what is happening in the Data Science world at large. datasciencepulse.com Joined December 2019

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Data Science Pulse @datascpulse

6 years ago

Python 2 will retire from its 20 years of service in April 2020 python.org/psf/press-rele… #DataScience #MachineLearning #Python

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Data Science Pulse @datascpulse

6 years ago

AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH AHHHHHHHH #RickandMorty #EvilMorty #MortyTheme

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Data Science Pulse @datascpulse

6 years ago

Help Us Rescue Our Animal Friends in Andros, Greece #crowdfunding @FundRazr Support and Retweet fundrazr.com/AnimalShelterA…

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Data Science Pulse @datascpulse

6 years ago

Extended sequence assignment in for loops >>> a, b, c = (1, 2, 3) # Tuple assignment >>> a, b, c (1, 2, 3) >>> for (a, b, c) in [(1, 2, 3), (4, 5, 6)]: # Used in for loop ... print(a, b, c) ... 1 2 3 4 5 6 #Python #DataScience #MachineLearning

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Data Science Pulse @datascpulse

6 years ago

Python Dictionaries are: 1. Accessed by key, not offset position 2. Ordered collections of arbitrary objects 3. Variable-length, heterogeneous, and arbitrarily nestable 4. Tables of object references (hash tables) 5. Mutable #DataScience #MachineLearning #Python

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Data Science Pulse @datascpulse

6 years ago

Understanding Numpy Axis axis 0 means collapse all rows. axis 1 means collapse all columns. #DataScience #MachineLearning

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Andrew M. Webb @AndrewM_Webb

6 years ago

To count an animal population if you can't catch them all: capture some, mark them, release, and capture again. The ratio of new vs already seen tells you something about the total number. Shown here is iterated mark-and-recapture with a Bayesian updates to belief about pop size

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Data Science Pulse @datascpulse

6 years ago

Knowing how to code in Jupyter Notebook is not enough; you need to understand how to write modular code, which follows the time tested software engineering concepts. Moreover, it comes in handy while building machine learning pipelines as well. #DataScience #MachineLearning

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Data Science Pulse @datascpulse

6 years ago

The ability to write Pythonic code, to compose modular and loosely-coupled architectures are essential skills. In short, they are as important as knowing machine learning algorithms. #DataScience #MachineLearning

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Data Science Pulse @datascpulse

6 years ago

SQL and Data Warehousing concepts are some of the most critical skills that you need to develop. Most people make the mistake of underestimating SQL. It is, therefore, always better to build a solid understanding of these concepts. #DataScience #MachineLearning

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Data Science Pulse @datascpulse

6 years ago

Learn Data Science in 2020 with 8 stages. datasciencepulse.com/general/data-s… #Welcome2020 #happynewyear2020 #DataScience

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Data Science Pulse @datascpulse

6 years ago

Python 2.7 will retire in another 3 hours and 9 minutes. #DataScience #programming #MachineLearning pythonclock.org

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Data Science Pulse @datascpulse

6 years ago

Good read. Also, have a look at the Data Science Roadmap datasciencepulse.com/general/data-s…

KDnuggets @kdnuggets

6 years ago

Good read. Also, have a look at the Data Science Roadmap datasciencepulse.com/general/data-s…

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Data Science Pulse @datascpulse

6 years ago

Today is the day of Python 2. Hurray. #Python #DataScience

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Data Science Pulse @datascpulse

6 years ago

Are you getting confused about where to start their Data Science journey? How to proceed in the Data Science journey? How to make sense of the latest developments happening in the Data Science space? Find your answers below datasciencepulse.com/general/data-s… #DataScience #MachineLearning

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Ajay Kumar Nagaraj @ajknzhol

6 years ago

Do you think Data Warehousing skills are important for a Data Science stream of work? I think SQL and Data Warehousing are not only important but the key pillars. #DataScience #MachineLearning