Data Science News Flash: 08-08-2019

The latest data science articles - algorithmically curated, ranked, and summarized just for you.

News Flash is a weekly publication that features the top news stories for a specific topic. The stories are algorithmically curated, evaluated for quality, and ranked so that you can stay on top of the most important developments. Additionally, the most important sentences for each story are extracted and displayed as highlights so you can get a sense of what each story is about. If you want more information for a particular story, just click the heading or image to read the entire article.

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I wasnt getting hired as a Data Scientist. So I sought data on who is.

Highlights:

What I needed was not another navel-gazing post on the top skills required of a data scientist but actual data on people who have successfully made the transition into data science.
The common conception of a triumvirate of Computer Science, Mathematics and Statistics, and Engineering disciplines forming the bedrock of a data science career is somewhat borne out by the data.
A surprising winner in the data science education stakes is Business Analytics and other Analytics fields, which collectively account for 15% of disciplines.
Interestingly, interns and trainees (11%) are also a viable class of precursors to a full-fledged data scientist role, and they typically take the form of Data Science or Analytics internships.
The most frequent combination of traits would probably be someone with a Masters or Ph.D. in Computer Science, Engineering, Mathematics or Analytics; whos been employed in industry for about 46 years; and was a Researcher, Software Engineer, Analyst or Data Science Intern in a previous life.

How To Effectively Create A Data Science Team

Highlights:

Hiring a data scientist is not an easy task and leveraging talent that is already in-house comes as the best way to fulfil the need of hiring data scientists.
While it requires hiring an experienced data scientist the cost may still be not as high as hiring a specialised data science team.
This comes in handy when there is a requirement of building an all-encompassing machine learning framework more than managing the data.
While it requires the highest cost, all operations from data cleaning and model training to building front-end interfaces, are realized by a dedicated data science team.
Once it is decided if you want to retrain, integrate or have a specialised data science team, it is time for defining the data scientists based on their roles.

Artificial Intelligence is a marketer's dream--will it hurt consumer choice?

Highlights:

Yifrach is the CEO at Market Beyond, a Tel Aviv-based AI company that provides the machine learning analysis eBay used to tweak its pricing.
One of the biggest names in the market is Xineoh, a Johannesburg-based company that uses machine learning to match potential customers with products so retailers know which items to stock.
London-based Black Swan Data uses machine learning algorithms and social media data to predict consumer trends, enabling its clients to design products.
As Xineohs Vian Chinner explained to VentureBeat last year, Netflix is a good example of how data analysis can lead to reduced choice.
In a new world of AI-driven personalized marketing, there will most likely be a narrower band of products being presented to consumers, says Katie King, an AI expert and CEO of AI in Marketing.

AI vs. Machine Learning: The Devil Is in the Details

Highlights:

Just as nuclear or theoretical physics are parts of physics as a whole, machine learning and deep learning are the branches of a colossal tree that is artificial intelligence.
As John McCarthy, the proclaimed godfather of AI technologies put it, Artificial intelligence is the science and engineering of making intelligent machines.
If we were to figure it out from a technical point of view, wed be stuck for years in a philosophical debacle trying to identify what is and isnt intelligence.
Additional definitions of AI state that it is a branch of computer sciences revolving around a machines ability to simulate and emulate human-like behavioral patterns.
If put simply, the AI doesnt need to analyze every car in the stream to predict and help avoid a traffic jam.

From a novice to one of the youngest Kaggle Competition Master and landing in a Fortune 500!

Highlights:

However, if you are familiar with data science and have made into Top 20% of any machine learning competition you will find the above book complete boring.
Another great resource that I added more value than any MOOC is Introduction to Statistical Learning with Applications in R. If I were an interviewer, Iwould look for someone who had understood the ISLR in theory and practice.
Often in a research context and for someone who wants to dig deeper and reach the core of statistics, Elements of Statistical Learning is a recommendation to them.
Gather data from your Android phone, stream tweets and study followers of your favourite actor, apply weird machine learning cases on Avengers Infinity war.
Contributing or working on a project from scratch will let you get experienced in real-world data science tasks that are considered to be most important.

Data Science Minimalism: Less is More!

Highlights:

Having strong judgement skills when it comes to deciding what to do with our time is one of the most important skills a data scientist can have.
The term machine learning doesnt have the same meaning to someone making his firsts steps in the field and someone that has great experience in that field.
Once you read one or two machine learning articles, ads start popping up regarding MOOCs on deep learning and computer vision, offering to provide you with all the skills you need to become a data scientist in one month.
While learning multiple programming languages might be necessary for a software developer, this is usually not the case for a data scientist.
Your job is to provide strong evidence that an idea is worth pursuing, to create knowledge from data, understand its business value and find the perfect way of communicating it.

Professor turned Data Scientist: Why Dr. Guido Maretto left academia for the startup life

Highlights:

I would say that main allure that data science has over conducting empirical research in academic environment is that you are forced to operate in much higher speed, but you get to actually implement the solutions that you find.
But most data science work is done in Python and a bit of R. But there is this skill gap that is necessary to bridge after academia.
Also important in industry is SQL, and anyone who has worked in data science knows that its pretty hard to escape entirely from SQL.
When it comes to the subject and the problems that you are solving like demand estimation, theres little difference between data science and economics.
Because if you throw a Deep Neural Network (DNN) at demand data and then you start setting the prices which is what most companies do in pricing they use machine learning models to do demand estimation youd find that the model would fail because the process that generates the price has changed, it has become you d. Side note 2.

Introduction to Decision Intelligence

Highlights:

Its a vital science for the AI era, covering the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale.
One way to approach learning about decision intelligence is to break it along traditional lines into its quantitative aspects (largely overlapping with applied data science) and qualitative aspects (developed primarily by researchers in the social and managerial sciences).
All technology is a reflection of its creators and systems that operate at scale can amplify human shortcomings, which is one reason why developing decision intelligence skills is so necessary for responsible AI leadership.
Unless you would take different actions in response to different still-unknown facts, theres no decision here though sometimes training in decision analysis helps you see those situations more clearly.
While data engineering is a separate sister discipline and key collaborator to decision intelligence, the decision sciences include a strong tradition of expertise involved in advising the design and curation of fact collection.

HPE Plans To Tie MapR With BlueData, Other Platforms In Analytics Play

Highlights:

Hewlett Packard Enterprises acquisition of the business assets of MapR is targeted at working with other HPE platforms including the company's BlueData container platform to help build out its artificial intelligence, machine learning, and analytics capabilities.
While HPE unveiled the acquisition of MapR's business assets, including the intellectual property, products, support, and customer and partner base, the company has essentially acquired all of MapR as there is nothing left of that company as an entity, said Patrick Osborne, HPEs vice president of big data and secondary storage.
Its products include MapR SD Distributed File and Object Store, which manages both structured and unstructured data at the exabyte scale to support artificial intelligence, machine learning, analytics, and Hadoop.
The company also offers HPE InfoSight predictive analytics technology, which it received with its 2017 acquisition of Nimble Storage, is in the process of becoming part of a wide swath of HPE's data center portfolio.
"From a wide range of HPC/AI hardware options that can process data-crunching, analytics, and algorithm-tuning applications at todays needed speeds, to AI and big data analytics software platforms, to access to Data Scientists via our partnership with HPE Pointnext Services, we are excited to offer complete AI, big data, and business intelligence solutions to our clients that will significantly help them retain or improve their competitive advantage and go to market with innovative offerings much faster than ever before possible," Molina told CRN.

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