Data Science News Flash: 08-29-2019

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 on it to read the entire article.

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Everything a Data Scientist Should Know About Data Management*

Highlights:

Google created GFS, MapReduce, and BigTable; Amazon created DynamoDB; Yahoo created Hadoop; Facebook created Cassandra and Hive; LinkedIn created Kafka.
Graph Databases, such as Neo4J and Amazon Neptune, represent data as a network of related nodes or objects in order to facilitate data visualizations and graph analytics.
Data warehouse supports the flow of data from operational systems to analytics/decision systems by creating a single repository of data from various sources (both internal and external).
Data Lakes allow users to run analytics without having to move the data to a separate analytics system, enabling businesses to gain insights from new sources of data that was not available for analysis before, for instance by building machine learning models using data from log files, click-streams, social media, and IoT devices.
Hadoop is typically used to generate complex analytics models or high volume data storage applications such as retrospective and predictive analytics; machine learning and pattern matching; customer segmentation and churn analysis; and active archives.

How Artificial Intelligence Learns Through Machine Learning Algorithms

Highlights:

While the umbrella term of AI does include machine learning algorithms, it is important to note that not all AI exhibits machine learning.
Programs that are built with the capability of improving and iterating by ingesting data are machine learning algorithms, whereas programs that emulate or mimic certain parts of human intelligence fall under the category of AI.
While explainable AI had already been a problem with machine learning, explaining the actions of deep learning algorithms is considered nearly impossible today.
Deep learning algorithms may hold the key to more powerful AI, as they can perform more complex tasks than machine learning algorithms can.
These general AI will undoubtedly have machine learning algorithms or deep learning programs as a part of their architecture, as learning is integral towards living life like a human.

7 Hot AI Jobs to Flex Your Artificial Intelligence Knowledge

Highlights:

By using machine learning models, a data scientist helps make timely business decisions based on large-scale data analysis.
A machine learning (ML) engineer is a software engineer who specializes in building machine learning applications, data pipelines, and API integrations.
ML engineers should be experienced in statistical models, know how algorithms work, understand what deep learning is and its connection to machine learning, be familiar with object-oriented programming, and know how to develop programs and applications.
Some jobs allow applicants to apply with a Bachelor’s Degree plus a certification in machine learning and artificial intelligence, like the program offered at MIT.
According to Glassdoor, the average base salary for a machine learning engineer in the United States is approximately $121,000.

An interview with Abhishek Kumar, Senior Manager, Data Science at Publicis Sapient

Highlights:

He has worked on various machine learning & deep learning projects involving recommender systems, image recognition, forecasting, optimization, anomaly detection and natural language processing.
He is currently working at Publicis Sapient as Senior Manager, Data Science and focusing on applying methods in machine learning to opportunities in retail, e-commerce, automobile, marketing and operational optimization.
I hope this interview serves a purpose towards the betterment of data science and machine learning communities in general .).
I am currently leading the data science practice at Publicis Sapient, India where we are working on several interesting and cutting edge AI & Machine learning-driven projects across multiple industries such as retail, financial service, energy & commodities, travel & hospitality, and automobile.
My data science and machine learning journey did not kick off from a formal university degree rather it was exploratory.

What’s the difference between analytics and statistics?

Highlights:

Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to lively debate about where to draw the boundary between them.
In fact, elite data scientists are expected to be full experts in analytics and statistics (as well as machine learning)… and miraculously these folks do exist, though they are rare.
While analytics training programs usually arm their students with software skills for looking at massive datasets, statistics training programs are more likely to make those skills optional.
A common blunder among the data unsavvy is to think that the purpose of exploratory analytics is to answer questions, when it’s actually to raise them.
Data exploration by analysts is how you ensure that you’re asking better questions, but the patterns they find should not be taken seriously until they are tested statistically on new data.

How Data Science Empowers Performance Marketing & SEM

Highlights:

Throughout the last decade, the life of the modern-day search engine marketer has become centered around data and artificial intelligence (AI) applications.
Debates and dialogues around AI subsets, machine learning and data science, and how exactly they affect the workings of the industry continue to multiply.
The multi-disciplinary field of data science is chief among them – empowering marketers to combine various data sets and decipher the variables in their campaigns that are having the biggest impact on performance.
Predictive analysis encompasses the use of data science and statistical algorithms to translate this data and segment customer behavior.
Through the introduction of data science into marketing stacks across the world, SEM managers have become empowered with significantly more knowledge about the workings and intricacies of their campaigns.

How to optimize storage for AI, machine learning and deep learning

Highlights:

The resurrection of AI at the hands of machine learning and deep learning has engendered an explosion of research and product development as businesses discover creative ways to use these new algorithms for process automation and predictive insights.
The nature of machine learning and deep learning models, the latter of which often emulate the brain's neural structure and connectivity, requires the acquisition, preparation, movement and processing of massive data sets.
A short digression into the nature of machine learning and deep learning software will reveal why storage systems are so crucial for these algorithms to deliver timely, accurate results.
Deep learning storage system design must provide balanced performance across a variety of data types and deep learning models.
The diversity of deep learning models and data sources, along with the distributed computing designs commonly used for deep learning servers, means systems designed to provide storage for AI must address the following factors.

Why We Find Joy and Value in Creating Data Visualizations

Highlights:

It’s a myth that designing visualizations is only for the end of the data analysis process or when you are ready to communicate some insights.
As a visual metaphor for data points, data visualization has the ability to make ideas more easily digestible and captivating at the same time.
Marketers know that eye-catching data visualizations combined with a powerful narrative can be very shareable and persuasive, as exemplified in the “Data Visualization + Data Storytelling Is Marketing Gold” article making the rounds on the internet.
For dataviz practitioner Wendy Small, the use of more simple data visualizations like line charts has been a healthy and effective way to encourage new approaches to reading data as part of a data literacy initiative.

AutoML Tools Emerge as Data Science Difference Makers

Highlights:

The days of handcrafted algorithms aren’t quite over, but it’s hard to dismiss to impact that automated machine learning (AutoML) is having on the data science field.
As companies look to imbue intelligence into their products and services, AutoML tools will lower the barrier of entry into data science and open the door for data-driven automation on vast scales.
In the past few years, we’ve seen a surge of interest in AutoML tools, which automate a range of tasks in the data science workflow.
Fujimaki says a majority of dotData customers are citizen data scientists who use dotData’s GUI tool to lead them through the process of building machine learning models.
Databricks is hoping to empower three groups — data scientists, data engineers, and citizen data scientists – to help them build machine learning applications.

3 Powerful Uses of Machine Learning in Marketing

Highlights:

If marketers expect to create more meaningful campaigns with target audiences and boost engagement, integrating machine learning can be the tool to unveil hidden patterns and actionable tactics tucked away in those heaping amounts of big data.
The company discovered that artificial intelligence and machine learning allowed the insight division to listen to what was being talked about in the public sphere.
Of course, while the examples above show how machine learning taps into brands’ customer bases more effectively, it’s important not to overlook the real cost-efficiency of such intelligent marketing campaigns.
As the influx of data continues growing uncontrollably, the implementation of machine learning in marketing campaigns will become even more relevant when it comes to striking up engaging conversations with consumers.
Companies like Ben & Jerry’s, Mazda and Sephora have already recognized the positive impact that machine learning can have on their brands, including higher engagement rates and increased ROI.

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