Telstra has used open source machine learning technology to answer the age-old question that plagues every marketer: how effective is my ad spend? The telco wields one of the biggest marketing budgets in Australia, but that doesn't stop Telstra from wanting to track the performance of every dollar spent. The company previously faced a six-month lag to get visibility into the effectiveness of its marketing spend; that is now down to five weeks using new marketing mix modelling developed in partnership with Accenture, Deakin University and Servian. The telco previously used a traditional econometric model to assess the performance of its marketing spend, pulling together 800 variables – which took two-and-a-half months to assemble – and then modelling this using regression techniques. "Six months after the marketing period had ended I could tell the CMO [chief marketing officer] and the marketers how effective their marketing was... six months ago," Telstra's director of research, insights & analytics Liz Moore told the recent Big Data & Analytics Innovation Summit in Sydney.
DUBLIN, IRELAND--(Marketwired - October 17, 2017) - RecommenderX today announced that it won the Best Use of Data Science In A Start Up Award at the DatSci event held in Dublin on September 21, 2017. DatSci is an annual event that brings together and recognizes the best and brightest that Ireland has to offer in the expanding world of Data Science. RecommenderX is a technology company, focused on helping customers and partners improve productivity, performance, customer engagement, sales and profitability, by transforming Artificial intelligence (AI) to Business Intelligence (BI). RecommenderX is the top spin out of Europe's largest Centre for Data Analytics Insight, with deep domain knowledge in Data Analytics, Artificial Intelligence (AI), Machine Learning (ML), Personalization Technology, Recommender Systems and Explainable AI. "We are thrilled to be an award winner at DatSci 2017," stated Kevin McCarthy, Co-Founder & CTO of RecommenderX. "It is a fantastic validation of the efforts that our world-class team have been making helping companies all over the world harness their data by developing cutting edge applications and solutions that leverage data science and AI technologies."
Editor's note: See also part 1 of 17 More Must-Know Data Science Interview Questions and Answers. Overfitting is when you build a predictive model that fits the data "too closely", so that it captures the random noise in the data rather than true patterns. As a result, the model predictions will be wrong when applied to new data. We frequently hear about studies that report unusual results (especially if you listen to Wait Wait Don't Tell Me), or see findings like "an orange used car is least likely to be a lemon", or learn that studies overturn previous established findings (eggs are no longer bad for you). Many such studies produce questionable results that cannot be repeated.
In 2016 it got exponential growth over 2012 and 2017 figures till Sept equally critical is delivering this performance with reduced silicon (all grey & thin areas) area and industry power consumption. Machine learning helps in reducing the required efforts bandwidth between the buyer, seller and manufacturers such bandwidth reductions also reduce the cost and required time. In eCommerce AI based technologies like Big Data, Machine Learning, Neural Networks, Data Science, Bots and Deep Learning (mainly for secured online payments) are currently buzzwords. To safe guard the business from anti social elements deep learning helps in fraud detection, prevention, velocity measure and makes better business decisions with deep understanding of entity resolution (avoid multiple accounts of same person), Image recognition and understanding, Concept extraction, sentiment and trend analysis makes buyers life easy to choose and buy.
According to a patent application Walmart filed, it seems like its next step is integrating IoT tags to products in order to monitor product usage, auto replace products as necessary and monitor expiration dates or product recalls. These sensors would rely on a variety of technology such as Bluetooth, barcodes, radio frequencies and RFID tags and would provide Walmart with an incredible amount of data including the time of day products are used to where the products are kept in the house. In another example, a RFID system could monitor how many times you pick up your laundry detergent and predict how much is left. This info could be added to your shopping list and fed to Walmart data vaults to illustrate consumer behavior.
With the help of Microsoft, last year Toyota created a new data analytics division called Toyota Connected to bring Internet-connected services into the car. Earlier this year, Renault-Nissan inked a deal to leverage Microsoft's Connected Vehicle Platform and its Azure cloud architecture to collect vehicle sensor and usage data in order to develop "connected driving experiences." Ford recently invested $182 million in Pivotal, a cloud-based software company, in part to create analytics tools and a cloud platform to support the automaker's Smart Mobility initiative. Cadillac introduced the first production vehicle-to-vehicle communication system on its 2017 models, and last year, Audi launched a Traffic Light Information vehicle-to-infrastructure system that lets its cars know how long a light will stay red or green to help improve traffic flow.
The new Oracle Management Cloud suite combines Oracle Management Cloud, Oracle Application Performance Monitoring Service, and Oracle Infrastructure Monitoring Cloud Service. The new Oracle Management Cloud suite includes the Standard Edition services, as well as Oracle IT Analytics Cloud Service and the new Oracle Orchestration Cloud Service. The Oracle Management Cloud has an analytics engine that is constantly updated with real-world data, providing it with evolving analytics. Oracle has also expanded its Oracle Log Analytics Cloud Service to monitor and analyze security and operational logs from a wide variety of both on-premises and cloud technologies, providing unified monitoring.
This is a programming oriented, hands-on training for starting a career in Data Mining and Machine Learning, and to acquire the necessary skills in statistical and inferential thinking. After this course, many of the things you read and hear about Data Science, Artificial Intelligence and Machine learning would make a lot more sense. The applications of this field span from marketing analysis and forecasts, predicting demands for products, making intelligent business decisions, cyber security and threat detection, predicting poll and survey results, and too many others to mention here. This course will enable participants to learn the foundation skills through programming, in arguably the most popular Data Science language today--Python.
Predictive analytics and cognitive messaging enable ERICA to remind customers about making payments, monitoring balances and managing debt. Chatbots will not necessarily replace human consultants, but they offer many advantages in terms of customer relations: saving time and money and allowing continuous customer service improvement thanks to the ability of the AI to learn from past interactions. Given that adoption rates of AI technologies are relatively low, developing countries in Africa and Latin America will likely only see a maximum of 6% of AI-driven growth. ", Accenture says AI has the potential to help developed countries double their GDP growth rates by 2035.
Smart grids, connected to each other via the cloud, and utilising the IoT, big data analytics and machine learning, can significantly increase the energy efficiency of the existing grid. The result is advanced production optimised for resource consumption and cost including energy, raw materials and water, whilst also enabling connection with customer devices to optimise lifespan performance. Wider 4IR technologies incorporated by the IIoT platform include Virtual Reality product simulators to optimise smart product design, sensor-driven computing, industrial big data analytics, energy efficient robotics, and intelligent machine applications. IoT, sensors, AI and cloud-enabled'precision agriculture' can use on-farm sensors and connected machinery to access real-time data for farmer smart devices that can optimise how much water, energy, fertiliser and feed to use, increasing productivity whilst reducing energy use and product waste.