Make Money by Scraping Data, Top Amazon Innovations, Data Analysis using Pandas & more stories

Anusha P -

Introduction


Welcome to the first post of 2022. Moving forward, we will be switching to a new newsletter format by including 'Interesting Jobs for the Data Folks', 'Tutorials', and more.

Dive in for the updates!

Around the web:

  • How to make money out of Data Scraping
    “People out there are looking for jobs in the Data Science field but do we really need jobs if we have the right skill set?” A brief introduction to the open-source framework ‘Scrapy’ and its various applications. Who needs this web scraping service? Who are the pro players in the market in this arena?
  • Amazon: Decoding the Social Effects of Media using ML
    “The media systems that we have now, for better or worse, have become outrage machines and sorting machines that put people into groups of like minds. The business incentive structures of these systems are such that the more outrage there is, the more profit there is. So, the first question that we’re asking is, What’s the scope of this problem? And then, What can we do to solve it?”
  • Begin your NLP journey here!
    The impact Natural Language Processing(NLP) has had on our lives in a few years is unimaginable. Search autocorrect and autocomplete, Chatbots are a few of many applications of NLP. This article introduces NLP in a palatable format and is a good place to start building your NLP basics.
  • Explainability Is in the Mind of the Beholder
    Explainable artificial intelligence and interpretable machine learning are research fields that are growing in importance. "We take steps towards addressing this challenge by reviewing the philosophical and social foundations of human explainability, which we then translate into the technological realm. In particular, we scrutinize the notion of algorithmic black boxes and the spectrum of understanding determined by explanatory processes and explainees' background knowledge."
  • Top of Amazon Science list 2021
    From machine learning models that use encrypted data to silicon-vacancy centers that use different wavelengths of light for quantum networking. Here are the top 10 round-up innovations from Amazon Science in 2021

Interesting Jobs for Data Folks:

  • FLYR Labs : Data Analyst
    Remote, United States
    In September 2021, FLYR Labs closed a $150M Series C Funding round. Currently FLYR Labs extends an opportunity to individuals with 3+ years of academic or professional experience to join the Data Science team in one of the AI Customer Delivery teams where they can play the key role in successfully onboarding new FLYR customers.
  • Credible : BI Data Analyst
    Remote, United States
    Credible believes life's changes create financial needs for people and offers people the choice to assess credit to fulfill those needs. Currently, Credible is looking for a data-driven and result oriented Data Analyst with 3+ years of experience to join the Business Intelligence and Analytics team.  

Tutorials:

  • Analysis of the Spotify Dataset
    Pandas is one of the mandatory libraries to master in your Data with Python journey. You can build a strong foundation through the pandas library by working on the 'Spotify' dataset. This article tutors through some very basic tools that pandas provide to help gain significant insights into any dataset in the music domain.
  • Harvard’s FREE course: Introduction to Artificial Intelligence with Python
    What you’ll learn?
    - Graph search algorithms
    - Reinforcement learning
    - Machine learning
    - Artificial intelligence principles
    - How to design intelligent systems
    - How to use AI in Python programs
  • How to import Kaggle Datasets into Google Colaboratory
    There are two tools that data science enthusiasts have definitely heard of, these are namely Google Colaboratory (also known as Colab) and Kaggle. Learn to import Kaggle dataset into Google Colaboratory using seven easy-to-follow steps.
  • How to sort a dictionary in Python
    Dictionaries are best used for key-value lookups: we provide a key and the dictionary very quickly returns the corresponding value. But what if you need both key-value lookups and iteration? It is possible to loop over a dictionary and when looping, we might care about the order of the items in the dictionary. With dictionary item order in mind, you might wonder how can we sort a dictionary?

Tweets:

Conclusion:

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