[unable to retrieve full-text content]This article analyses scientific debt – what it is and what it means for data science.
Original Post: Scientific debt – what does it mean for Data Science?
[unable to retrieve full-text content]Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.
Original Post: Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis
[unable to retrieve full-text content]Can logic be used to make chatbots intelligent? In the 1960s this was taken for granted. Now we have all but forgotten the logical approach. Is it time for a revival?
Original Post: If chatbots are to succeed, they need this
[unable to retrieve full-text content]The traditional concept of ETL is changing towards ELT – when you’re running transformations right in the data warehouse. Let’s see why it’s happening, what it means to have ETL vs ELT, and what we can expect in the future.
Original Post: ETL vs ELT: Considering the Advancement of Data Warehouses
[unable to retrieve full-text content]What are the critical steps to get a job in data science? We share the proven formula that helped many data enthusiasts secure job offers as data scientist/analyst, data engineer and machine learning engineer.
Original Post: 6 Proven Steps to Land a Job in Data Science
[unable to retrieve full-text content]This article introduces a pip Python package called KernelML, created to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients.
Original Post: Kernel Machine Learning (KernelML) - Generalized Machine Learning Algorithm
[unable to retrieve full-text content]Check out this collection of 9 (plus some additional freebies) must-have skills for becoming a data scientist.
Original Post: 9 Must-have skills you need to become a Data Scientist, updated
[unable to retrieve full-text content]Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.
Original Post: How to build analytic products in an age of data privacy
[unable to retrieve full-text content]The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
Original Post: How to Organize Data Labeling for Machine Learning: Approaches and Tools
[unable to retrieve full-text content]We give a comprehensive review of data lakes and data warehouses, and look at what the future holds for total data integration.
Original Post: Beyond Data Lakes and Data Warehousing