Mar2018 Apr Opinions, Interviews
All (77) | Courses, Education (4) | Meetings (8) | News, Features (8) | Opinions, Interviews (24) | Top Stories, Tweets (8) | Tutorials, Overviews (21) | Webcasts & Webinars (4)
- Data Exchange and Marketplace, a New Business Model in Making
- Apr 23, 2018.
This article covers how an ever-increasing amount of data will trigger the evolution of a new ecosystem that will spur entrepreneurial activity, offering an opportunity to start a wide range of new businesses.
- Let’s Admit It: We’re a Long Way from Using “Real Intelligence” in AI
- Apr 19, 2018.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
- Presto for Data Scientists – SQL on anything
- Apr 19, 2018.
Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.
- Hedge Yourself From a Risky Data Science Job
- Apr 18, 2018.
This article covers why it's important to consider all the factors when being hired as a data scientist.
- 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
- Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
- Role of IoT in Education
- Apr 17, 2018.
In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.
- When Do We Trust Machines?
- Apr 16, 2018.
We propose a framework of "trust heatmap", show how the trust in machines depends on two key elements: their error rate and the costs of mistakes, and examine the automation frontier.
- Key Algorithms and Statistical Models for Aspiring Data Scientists
- Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
- Are High Level APIs Dumbing Down Machine Learning?
- Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Don’t learn Machine Learning in 24 hours
- Apr 13, 2018.
When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.
- Machine Learning Engineer, Data Scientist among the best US Jobs in 2018
- Apr 12, 2018.
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
- Onboarding Your Machine Learning Program
- Apr 12, 2018.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist
- Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
-
Top 8 Free Must-Read Books on Deep Learning
- Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning. - The Cold Start Problem with AI
- Apr 10, 2018.
Why companies struggle with implementing AI and how to overcome it.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis
- Apr 9, 2018.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Health Care and Fraud Detection are gaining. Anti-spam, Manufacturing, and Social are the fastest growing sectors in 2017, while Oil / Gas / Energy and Social Networks analysis have declined.
- Why so many data scientists are leaving their jobs
- Apr 9, 2018.
We look at some of the big challenges and frustrations that data scientists face on a regular basis.
- Build a Foundation that Supports AI and Machine Learning
- Apr 6, 2018.
In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
- Why Data Scientists Must Focus on Developing Product Sense
- Apr 6, 2018.
Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.
- Are New Technologies Killing Their Ancestors?
- Apr 6, 2018.
Are automatic feature learning models (e.g. CNN) killing their previous manually engineered models? This is an important question that is to be answered in this article.
- Data Science and the Art of Producing Entertainment at Netflix
- Apr 5, 2018.
Each Netflix production is a logistical challenge that consumes and produces a vast amount of data. The tech giant is utilising this data to help them create new content and assist them at every stage, from pre-production to launching the show.
- What Does GDPR Mean for Machine Learning?
- Apr 4, 2018.
This post investigates how the GDPR, which comes into force at the end of May, will effect machine learning.
- How Do I Get My First Data Science Job?
- Apr 2, 2018.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
- A Day in the Life of a Data Scientist: Part 4
- Apr 2, 2018.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.