Apr2018 May Opinions, Interviews
All (78) | Courses, Education (7) | Meetings (12) | News, Features (6) | Opinions, Interviews (17) | Top Stories, Tweets (7) | Tutorials, Overviews (23) | Webcasts & Webinars (6)
- 6 Proven Steps to Land a Job in Data Science
- May 21, 2018.
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.
- Kernel Machine Learning (KernelML) - Generalized Machine Learning Algorithm
- May 18, 2018.
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.
- 9 Must-have skills you need to become a Data Scientist, updated
- May 17, 2018.
Check out this collection of 9 (plus some additional freebies) must-have skills for becoming a data scientist.
- How to build analytic products in an age of data privacy
- May 17, 2018.
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.
- How to Organize Data Labeling for Machine Learning: Approaches and Tools
- May 16, 2018.
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.
- Beyond Data Lakes and Data Warehousing
- May 15, 2018.
We give a comprehensive review of data lakes and data warehouses, and look at what the future holds for total data integration.
- Data Engineer vs Data Scientist: the evolution of aggressive species
- May 14, 2018.
This article looks at how the two "species" - data scientists and data engineers - harmonise and coexist.
- Data Augmentation: How to use Deep Learning when you have Limited Data
- May 9, 2018.
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images.
- Torus for Docker-First Data Science
- May 8, 2018.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
- 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning”
- May 8, 2018.
Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.
- To SQL or not To SQL: that is the question!
- May 7, 2018.
This article looks at the emergence of the NoSQL movement and compares it to a traditional relational database.
- Apache Spark : Python vs. Scala
- May 4, 2018.
When it comes to using the Apache Spark framework, the data science community is divided in two camps; one which prefers Scala whereas the other preferring Python. This article compares the two, listing their pros and cons.
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8 Useful Advices for Aspiring Data Scientists
- May 4, 2018.
I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?" - Deep Conversations: Lisha Li, Principal at Amplify Partners
- May 3, 2018.
Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.
- AI is not set and forget
- May 3, 2018.
Just like a car, AI-based system can tick along in decent shape for a while. But neglect it too long and you’re in trouble. Unfortunately, failing to maintain your AI will destroy the project.
- PrivacyGuide: Towards an implementation of the EU GDPR on Internet privacy policy evaluation
- May 3, 2018.
Studies have shown that only 1% or less of total users click on privacy policies, and those that do rarely actually read them. The GDPR requires clear succinct explanations and explicit consent, but that’s not the situation on the ground right now, and it’s hard to see that changing overnight on May 25th.
- To Kaggle Or Not
- May 2, 2018.
Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.