BUILDING A DATA SCIENCE PORTFOLIO

| November 22, 2016

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Kaggle is a crowd-sourcing platform for data science. Real companies post their data science problems and Kaggle hosts competitions to the public in an effort to solve the problem and rank the participates against one another. The famous $1 million dollar Netflix challenge was originally a Kaggle competition. When large companies need help with their data science challenges, they turn to Kaggle and its community for help. Companies such as: Allstate, Bosh, State Farm, Red Hat,  Facebook,  Expedia, Home Depot, Yelp, Airbnb, Walmart, and Liberty Mutual.

Spotlight

Quantta Analytics Plc.

Quantta Analytics is a spatial and predictive analytics company based in Bangalore. The company was founded by Malvika and Vinay Bawri. We use analytics to answer simple but powerful questions - who will buy from you, what will they buy from you and where will they buy it and how can you reach them using Deep Learning. We work for the energy, banking and financial, retail, FMCG, and fast food industries.We have built a unique Software as a Service (SaaS) to help clients understand their data effectively. We work with Professors and Phd's from some of the leading Universities in the United States. Our vision is to lead 21st century innovation by nurturing top talent to incubate ideas that bring elegance, simplicity, and productivity to everyday life. From better functionality to beautiful interfaces, our innovations will change the way you work.

OTHER ARTICLES

Taking a qualitative approach to a data-driven market

Article | February 18, 2021

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

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Data privacy concerns over Internet of Things

Article | February 25, 2020

Internet of Things, according to congressional research service (CRS) report 2020, is a system of interrelated devices connected to a network and/or to one another, exchanging data without necessarily requiring human to machine interaction.The report cites smart factories, smart home devices, medical monitoring devices, wearable fitness trackers, smart city infrastructures, and vehicular telematics as examples of IoT.

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GOVERNMENTS LEVERAGING BIG DATA INNOVATIONS TO TACKLE CORONAVIRUS

Article | April 2, 2020

The outbreak of coronavirus has taken many countries under its hood. Most of them are suffering from economic loss and a higher mortality rate. Amid this, governments are in a great dilemma how to handle the circumstances around the falling economy and upsurging coronavirus infections. In order to get better hold onto situations across their countries, they are moving towards innovative technology adoption. Out of all the new-age technologies, big data and data analytics can serve with a great opportunity, where governments across various nations can understand the outbreak analytics.

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How can we democratize machine learning on IoT devices

Article | February 12, 2020

TinyML, as a concept, concerns the running of ML inference on Ultra Low-Power (ULP 1mW) microcontrollers found on IoT devices. Yet today, various challenges still limit the effective execution of TinyML in the embedded IoT world. As both a concept and community, it is still under development.Here at Ericsson, the focus of our TinyML as-a-Service (TinyMLaaS) activity is to democratize TinyML, enabling manufacturers to start their AI businesses using TinyML, which runs on 8, 16 and 32 bit microcontrollers.Our goal is to make the execution of ML tasks possible and easy in a specific class of devices. These devices are characterized by very constrained hardware and software resources such as sensor and actuator nodes based on these microcontrollers.Below, we present how we can bind the as-a-service model to TinyML. We will provide a high-level technical overview of our concept and introduce the design requirements and building blocks which characterize this emerging paradigm.

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Spotlight

Quantta Analytics Plc.

Quantta Analytics is a spatial and predictive analytics company based in Bangalore. The company was founded by Malvika and Vinay Bawri. We use analytics to answer simple but powerful questions - who will buy from you, what will they buy from you and where will they buy it and how can you reach them using Deep Learning. We work for the energy, banking and financial, retail, FMCG, and fast food industries.We have built a unique Software as a Service (SaaS) to help clients understand their data effectively. We work with Professors and Phd's from some of the leading Universities in the United States. Our vision is to lead 21st century innovation by nurturing top talent to incubate ideas that bring elegance, simplicity, and productivity to everyday life. From better functionality to beautiful interfaces, our innovations will change the way you work.

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