The Big Data dilemma


Most of you will have interacted with several algorithms already today. Algorithms are of course simply sets of rules for solving problems, and existed long before computers. But algorithms are now everywhere in digital services. An algorithm decided the results of your internet searches today. If you used Google Maps to get here, an algorithm proposed your route. Algorithms decided the news you read on your news feed and the ads you saw.

Matching While Learning Machine Learning

We consider the problem faced by a service platform that needs to match supply with demand, but also to learn attributes of new arrivals in order to match them better in the future. We introduce a benchmark model with heterogeneous workers and jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The payoff from a match depends on the pair of types and the goal is to maximize the steady-state rate of accumulation of payoff. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs. The platform faces a trade-off for each worker between myopically maximizing payoffs (exploitation) and learning the type of the worker (\emph{exploration}). This creates a multitude of multi-armed bandit problems, one for each worker, coupled together by the constraint on the availability of jobs of different types (capacity constraints). We find that the platform should estimate a shadow price for each job type, and use the payoffs adjusted by these prices, first, to determine its learning goals and then, for each worker, (i) to balance learning with payoffs during the "exploration phase", and (ii) to myopically match after it has achieved its learning goals during the "exploitation phase."

Data Analyst - IoT BigData Jobs


Essential Skills/ Characteristics: • Highly skilled with SQL and writing queries • Expert in data management principles and data normalization • Must be able to recognize patterns in data and remove "noise" from "insights" • Experience in developing reports and charts to depict "data story" • Exceptional excel and pivot table mastery • Exposure to data modeling and understanding of predictive analysis • Ability to propose improvements to existing system/data base/data structures • Keen ability to articulate data concepts in lamens • Ability to collaborate with remote teams • Extensive experience with data analysis • Experience with ETL tools Winning Ways • Focus on the Customer: Know your customers well; add value with a sense of urgency.

Talend Recognized in CRN's Big Data 100 List for Third Consecutive Year -


REDWOOD CITY, Calif., May 22, 2018 (GLOBE NEWSWIRE) -- Talend (NASDAQ: TLND), a leader in cloud data integration solutions, has been named to the 2018 CRN Big Data 100 list, a brand of The Channel Company. This annual list recognizes vendors that have demonstrated an ability to innovate in bringing to market products and services that help businesses work with one of the most dynamic, fastest growing segments of the IT industry - Big Data. As a result of Talend's inclusion in the CRN Big Data 100, Solutions Review selected Talend Open Studio for Data Integration and Talend Cloud among its list of "7 Data Integration Tools We Recommend". The data explosion in recent years has fueled a vibrant big data technology industry. Businesses need innovative products and services to capture, integrate, manage and analyze the massive volumes of data they are grappling with every day.

21 Scary Things Big Data Knows About You


Of course, Google knows what you've searched for. So do Bing, Yahoo!, and every other search engine. And your ISP knows every website you've ever visited. Google also knows your age and gender -- even if you never told them. They make a pretty comprehensive ads profile of you, including a list of your interests (which you can edit) to decide what kinds of ads to show you.