Best links of the week #11

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Best links of the week from 18th March to 24th March.

Links

  1. Tips and Tricks to Ace Data Science Interviews – Brand New Podcast Series by Analytics Vidhya! at Analytics Vidhya.
  2. Top 5 Data Science GitHub Repositories and Reddit Discussions (January 2019) at Analytics Vidhya.
  3. The 25 Best Data Science and Machine Learning GitHub Repositories from 2018 at Analytics Vidhya.
  4. Source code and overall data of the Deep Learning Summer School 2018 at Deep Learning Brasil GitHub repository.
  5. How can you set yourself apart when everyone is doing machine learning or data science in 2019? at Quora.
  6. What are some general tips on feature selection and engineering that every data scientist should know? at Quora.
  7. An overview of feature selection strategies by Burak Himmetoglu at Data Science Central.
  8. Selecting features as (network) nodes at Chloe-Agathe Azencott’s GitHub page.
  9. Como anda o metrô de São Paulo? at Paulo Haddad’s GitHub page.
  10. IMD sedia lançamento de chamada pública nacional para financiamento à inovação tecnológica at Notícias do Instituto Metrópole Digital.
  11. Gestão Por segurança e transparência, Receita leva base de CPF para Blockchain at CIO.

Blog/posts

  1. Building a dataset for the São Paulo Subway operation by Douglas Navarro at Towards Data Science.
  2. How People Meet Their Partners by Nathan Yau at FLOWINGDATA.
  3. Shifts in How Couples Meet, Online Takes the Top by Nathan Yau at FLOWINGDATA.
  4. Outlier Detection with Isolation Forest by Eryk Lewinson at Towards Data Science.
  5. Outlier Detection with Extended Isolation Forest by Eryk Lewinson at Towards Data Science.
  6. Introduction to Power Analysis in Python by Eryk Lewinson at Towards Data Science.
  7. Interpreting the coefficients of linear regression by Eryk Lewinson at Towards Data Science.
  8. Explaining Feature Importance by example of a Random Forest by Eryk Lewinson at Towards Data Science.
  9. 5 Ways to Detect Outliers/Anomalies That Every Data Scientist Should Know (Python Code) by Will Badr at Towards Data Science.
  10. What Is Feature Engineering for Machine Learning? by Amit Shekhar at MindOrks’ Medium.
  11. Seven Ways to Make up Data: Common Methods to Imputing Missing Data at The Analysis Factor.
  12. Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood at The Analysis Factor.
  13. How to Handle Missing Data with Python at Machine Learning Mastery.
  14. Missing Value Treatment with R at R Statistics.
  15. Como eu me tornei um Engenheiro de Machine Learning/Deep Learning by Arnaldo Gualberto at ENSINA AI.

Videos

  1. Real Talk with Google Data Scientist (with a PhD in Physics) at Springboard’s YouTube channel.
  2. Inteligência Artificial com Dinossauro da Google at Ivan Seidel’s YouTube channel.
  3. Comparando 10 modelos preditivos diferentes de Séries Temporais at Prof. Fernando Amaral’s YouTube channel.

Podcast episodes

  1. O mundo está se tornando um lugar pior? at Deviante: Spin de Notícias.

Data Science focused and commented version in Portuguese here.