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Sanayya (she/her)

@_sanayya

I like coding amongst other things | Data Scientist •✉️DMs are OPEN to anyone interested in learning and growing together

 New Delhi, India
github.com/sanayya
Joined January 2019
  • Tweets509
  • Following219
  • Followers449
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How to learn Data Science from Beginners to Masters in just 1 year (my personal experience) First, introspect why do you want to become a data scientist.
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Opening the pages of 'Ikigai'- ready to learn the secrets of longevity and happiness from the people of Okinawa. #NewRead
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🔍#DataScienceTip:The field of data science is constantly evolving. Stay updated with the latest trends like AI, machine learning advancements, cloud computing, and big data technologies. Continuous learning is vital in this ever-changing landscape #MachineLearning #BestPractices
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🔍 #DataScienceTip: A key skill in data science is translating complex analyses into clear, actionable insights. Don't just present data; tell a story with it. This helps stakeholders make informed decisions. #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: Complex models aren't always better. Sometimes, a simple model can be more interpretable, faster, and just as accurate. #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: Your future self will thank you for good documentation. Whether it's code, data, or processes, always document your steps. #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: Data science isn't a solo journey. Collaborate with domain experts, engineers, and other stakeholders. Together, you can create impactful solutions. #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: The tech world evolves rapidly. Dedicate time every week to learn about the latest trends and tools in data science. Stay curious! #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: Be wary of biases in your data. An unbiased model begins with unbiased data. Always check for representation and fairness. #MachineLearning #DataAnalytics #MachineLearning #BestPractices
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🔍 #DataScienceTip: Remember, garbage in equals garbage out. Always prioritize data cleaning before diving into analysis. Clean data is the foundation of good results. #DataAnalytics #MachineLearning #BestPractices
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📊 Just visualized the correlation between features in my latest dataset. It's fascinating how some variables move in sync! Check out this heatmap. #DataViz #Python
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🔍 #DataScienceTip: Always normalize your data before feeding it into a machine learning model. It helps in faster convergence and better performance. #MachineLearning #BestPractices
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Python Pop Quiz 🐍 What is the output of the following code, and why? #ArtificialIntelligence #MachineLearning #DeepLearning #Python3 #Python #pythonprogramming #code #100DaysOfCode #100daysofcodechallenge
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Here a and b are two separate lists with the same values, but they are not the same object. This means that if you modify one list, the other list will not be affected. The correct option is 1.
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Daily programming question for interview preparation👩‍💻 This problem was asked by Spotify. #ArtificialIntelligence #MachineLearning #DeepLearning #Python3 #Python #interview #code #100DaysOfCode #100daysofcodechallenge #MAANG
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Python Pop Quiz 🐍🐍 What is the output of the following code, and why? #ArtificialIntelligence #MachineLearning #DeepLearning #Python3 #Python #pythonprogramming #code #100DaysOfCode #100daysofcodechallenge
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The == operator compares the values of the two lists and returns True if they are equal, while the is operator compares the identities of the two lists and returns True if they are the same object. The output of [] == [] is True, but the output of [] is [] is False.
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Daily programming question for interview preparation👩‍💻 This problem was asked by Palantir. #ArtificialIntelligence #MachineLearning #DeepLearning #Python3 #Python #interview #code #100DaysOfCode #100daysofcodechallenge #MAANG
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Ramadan Kareem 🌙
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the late night ultimate comfort food.
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Daily programming questions for interview preparation👩‍💻 This problem was asked by Google. Given an array of integers where every integer occurs three times except for one integer, which only occurs once, find and return the non-duplicated integer.
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For example, given [6, 1, 3, 3, 3, 6, 6], return 1. Given [13, 19, 13, 13], return 19.
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