365 Data Science

Discover what ETL is, and see in what ways it’s critical for data science.

Originally from KDnuggets https://ift.tt/3mfTSU7

We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

Originally from KDnuggets https://ift.tt/3uhvy6T

Formula 1 & Machine Learning

There aren’t many things in the universe that can’t be predicted. Anything that can be quantified can be accurately predicted with data processing and artificial intelligence. The world of sports is rich in quantifiable features, making it ideal for the application of artificial intelligence. Artificial intelligence systems in sports have become commonplace in recent years. Given the positive influence they’ve had as their talents have grown, they’ll continue to make inroads into the world of sports.

Formula 1

Sensors, routers, connectivity, centralized communications, and cybersecurity solutions are becoming increasingly important for Formula 1 teams, allowing data processing to improve cars and raceday strategies.

Formula 1 is a data-driven sport: 120 sensors on each car generate 3 GB of data during each race, and 1,500 data points are generated per second. Formula 1’s data scientists are using Amazon SageMaker to train deep-learning models on 65 years of historical race data to extract critical race results statistics, make race forecasts, and provide fans with insight into the split-second decisions and strategies used by teams and drivers.

About 750 million data points will be received and sent by the ECU during a two-hour race. That is twice the number of words that each of us would say in our lifetime.

Succeeding in Formula 1 is now all about the cycle of racing, calculating, analyzing, designing, and then repeating this process, according to Zoe Chilton, head of Technical Partnerships at Aston Martin Red Bull Racing, with the team making about 1,000 new prototypes between each race on the calendar, or 30,000 for the season.

Unpredictability

The weather is arguably the most volatile aspect of a race. Even if teams have access to live weather reports, it is difficult to determine precisely what will happen; the 2020 Hungarian Grand Prix was expected to rain, but it did not, affecting tyre and pit stop plans for each driver.

The consistency of the predictions is often called into question when discussing problems such as:

• Crashes — Since the first World Championship Grand Prix at Silverstone in 1950, where there was no medical backup or protective precautions in the event of a collision, safety conditions have changed. When investigating the latest crash involving Romain Grosjean, it was discovered that Grosjean’s car hit the triple-guardrail barrier at 192kph (119mph) with a peak impact of 67G. The article also went into depth on how the badly damaged Haas car caught fire. As a result, no one can guess when a crash will occur.

• Penalties incurred during the race — Drivers can face penalties for a variety of offences, including starting too soon, speeding in the pitlane, causing an accident, blocking unfairly, or avoiding flags of any colour. The majority of these fines are imposed on the driver. As a result, predicting drivers’ instant decisions is impractical.
• Grid penalties — The penalties were ten grid places for the first use of a sixth of any component and five grid places for the use of a sixth of any other component. If seven or more units were used, the procedure was repeated. Any unused grid penalties were also rolled over to future races.
• Mechanical/technical failures — During the 2006 racing season, the team had a record-breaking zero chassis failures. Nonetheless, the nature of the sport, with production motivated by strong competition, means that our capacity to manufacture pieces has a potential to outpace our ability to properly understand them. Regardless of the procedures in place to ensure the reliability and efficiency of the vehicles, there will still be a slight chance of failure. What the systems do is keep that probability at an appropriate and achievable level while still allowing us to fix faults easily and professionally when they do occur.

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• Pit lane incidents — Pit stops have become an art form for Formula One teams. Red Bull still holds the record for the quickest time to change all four tyres on a car, 1.923 seconds. In 2012, McLaren completed a five-wheel pit stop (all four tires and the steering wheel) in 3.3 seconds. And non-fans are awestruck at how quickly everything is completed. However, in such a high-pressure environment, mistakes are unavoidable.

Deep learning can be used to predict when mechanical failures will occur to solve this problem, but how accurate is it? Pit stops often take 20–25 seconds, which means that a wrongly timed/misjudged stop may cost the driver a podium and valuable championship points; the accuracy must be as accurate as possible.

Tracks Redevelopment

Since races are continually being reintroduced/removed from the schedule, data for a given Grand Prix will not always be applicable. The Dutch Grand Prix at Zandvoort is returning after 35 years — this data would be significantly out of date, particularly because the track is being reconstructed.

Changes to tracks between seasons don’t help either, since the track will be “different” any time it changed, particularly if the track distance was affected. To do so, teams will need to build a model of each circuit that integrates the addition/removal of different elements, as well as an algorithm that estimates an average lap time.

Predictions

Some fans are using machine learning to make their own predictions, and others are building visual dashboards to see which factors are more likely to influence the results themselves. Now that the significance of qualifying positions has been identified, the likelihood of winning depending on starting place must be examined, given all other factors are equal — i.e. the driver qualifying first incurs no grid penalties.

The Baku circuit in Azerbaijan is the least predictable, but considering the race’s short history and outcomes, this is not surprising. The driver in pole position has only won once. In 2017, the winner started from the 10th row, demonstrating how chaotic F1 races are offered the possibility of winning from outside the front row. Clearly, this shows the importance of the circuit in reliably predicting the results of a competition, when not all circuits are as straightforward to forecast as some.

Overall, the large quantities of data available to teams enable teams to study different facets of a race separately, but the difficulty of the variables that make up a race means that, for the time being, using ML methods to predict race strategy is inaccurate. Despite these issues with the results, there is no doubt that ML and AI will soon overtake the sport — the only concern is how long it will take and how accurate it will be.

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Formula 1 AND Machine Learning was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/formula-1-and-machine-learning-62d1f7166c41?source=rss—-5e5bef33608a—4

Deep personalization is important for businesses today because customer experiences can influence the completion of a sales transaction. Social listening provides brands with an opportunity to pick customer’s conversations, analyze them and respond to them on social media.

Social listening tools are a way of measuring customer views and carrying out audience research. The cheat sheet for AI and deep learning can allow you to check the documentation and help you understand the customer-related issues so that you can address them.

Brandwatch

Brandwatch is a solution that is highly focused on competitive intelligence and consumer. It focuses on delivering insights into user trends or sentiments on a real-time basis. Brandwatch separates millions of online conversations daily to deliver meaningful insights to the users.

The tool has ML and AI capabilities and avails historical data to its users. Brandwatch is a great tool for people who want trendspotting solutions and consumer intelligence. It is a value-creating tool that offers real-time tracking of conversations and delivers influencer identification.

Brandwatch offers the Brandwatch analytics, audience, and Vizia products to enterprises and SMBs. Brandwatch Analytics is a query engine that gives clients insights such as competitor benchmarking, marketing measurement, and market research. The audience uses a live database and influencer technology to identify brand influencers. Vizia bridges the gap between decision-makers and business analysts, sending insights and data in a story-telling way.

Digimind

Digimind was launched in 2018 after collaborative research work between IBM teams and Digimind lab. This tool responded to markets that got interested in using AI to improve customer experiences, engage in big data analysis and attain some level of personalization.

According to a reviewer on the best essay writing service, Digimind offers real-time AI-powered social media capability. It helps brands to listen, analyze and report based on the influencer marketing hub account. Digimind displays the social conversations regarding the customer’s brand and divides them into categories. Each category is given is rated as negative, positive, or neutral.

Digimind tool also enables you to see the image of your brand on Google. You can follow promptly the information your customers want concerning your brand, people, and products, in addition to tour competitor details.

Socialbakers

Socialbakers tool uses the principle of artificial intelligence to offer solutions with power and ease. This is a user-friendly tool and is robust enough to handle different cases that are needed in a solution and plays a role in every stage of a consumer journey.

Socialbakers simplifies the leveraging of social listening in pre-sale activities and marketing. Thus, you can gain intelligence for audience research, competitive research, content planning, and more. In post-sale agreement, Socialbakers listening solution integrates with customer care and community management tools to improve customer affinity, reduce response time and boost engagement.

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Synthesio

Synthesio offers social media intelligence coupled with next-generation AI. Social media intelligence aims at monitoring a business’s online presence and providing insights. This platform can capture social media information from many countries in various languages.

Synthesio can be tailored and integrated into a company’s dashboard, together with logo recognition, paywall data, and consumer reviews. This platform can also track likes, views, favorites, retweets, responses, and shares from Facebook, Instagram, and Twitter onto the listening dashboard.

Users can include business intelligence with engagement rate widgets and media value. The pre-filtered data can be transmitted using an in-house tool or via API. The performance data can be merged with social listening metrics in diverse business intelligence tools.

Youscan

Youscan is a social media intelligence tool that is powered by AI with image recognition capabilities. It enables businesses to analyze customer opinions and identify insights to manage brand reputation.

Youscan offers detailed searches for subjects across all its plans. It gives you a page that has all your brand mentions based on your preferences. These features can enable you to learn about the people interacting with you or using your brand. You can discover the feedback about your brand and act fast to deal with the negative feedback.

It detects objects, logos, scenes, and demographic information. It provides a detailed description of images, enabling agencies and brands to instantly access consumer insights. You can see the consumption patterns, conduct competitor analysis, and understand the customer persona better.

Mention

Mention facilitates real-time searches and sets an alert that provides results continuously. It also has custom plans that can let you access historical data upon request. You can search for influencers on Instagram and Twitter by conducting a keyword search. Mention lets you track the influencers and send them to the CRM tool that you adopt.

It can enable you to create tailored reports regarding the content performance using the tool’s insight center. Mention is compatible with all the major social media platforms as well as YouTube and blogs. It offers you a means of connecting with your audience and boosts your online presence. You can take advantage of the free trial upon request before subscribing to a paid option.

Brand24

Brand24 collects social insights regarding the online people’s opinions about your brand. It reflects the timelines of the brand mentions allowing you to act on opportunities and respond to issues promptly.

Brand24 has a discussion volume chart through which your brand mentions can be represented graphically. It also has an influencer score that can enable you to connect with your brand influencers on social media. The automated sentiment analysis enables you to see the ranking of your brand mentions.

Through the Brand24 tool, you can receive instant alerts for any negative mentions and be able to engage in key conversations on time. Learning to know what people like or don’t like about your brand can enable you to improve communication and this is a pre-requisite for your business growth.

Conclusion

The above social listening tools are powered by artificial intelligence to enable businesses to understand their brands, customers, and market dynamics. The ability to listen to your customers and listen to what they are saying about your brand is a critical aspect that can promote your business growth. Get started with your preferred tool and generate positive customer experiences in your business.

Don’t forget to give us your ? !

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Best Social Listening Tools for Deep Personalization using Artificial Intelligence was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/best-social-listening-tools-for-deep-personalization-using-artificial-intelligence-4ecd25289e30?source=rss—-5e5bef33608a—4

In today’s times, data has emerged to be the new oil. The more you understand your data, the better business decisions you are able to make.

As the name suggests, big data analytics refers to managing and analysing large data sets. Big data analytics helps in extracting insights and uncovering patterns from large pools of complex data.

Not only large corporations, small and medium enterprises are also leveraging big data analytics to obtain the best possible outcomes for their businesses. Let us explore how small businesses can benefit from data analytics and learn about the technologies that enable big data analytics for businesses.

HOW CAN SMALL AND MEDIUM BUSINESSES BENEFIT FROM DATA ANALYTICS?

Small businesses lack the resources to go all in on their big data investments. Therefore, SMBs require a smarter strategy for joining in the big data trend. Here are a few tips –

• Instead of worrying about using big or small data sets, SMBs should start by investing in small scale analytics and lay focus on employing data technology analytics for enterprise decision making by optimal business datasets.
• Also, rather than collecting all sorts of business data in anticipation of future usage, SMBs should utilise data sets which help them solve immediate problems.
• Since most of the SMB executives rely on personal experience and beliefs instead of business data-driven results –an organisational change becomes a prerequisite for introducing big data culture in smaller organizations.
• Using cloud computing is also elemental for implementing big data solutions effectively in SMBs. Cloud has a two-fold benefit — one; it helps connect all services via a unified platform. Two, SMBs can derive significant cost benefits by employing cloud-based big data processing solutions.
• SMBs operate at a much smaller scale, therefore investing too much in operation analytics, R&D analytics, etc. makes little sense for them. Instead, they can benefit more by focusing on customer analytics. With better product marketing, personalised services and targeted offers, SMBs can gain a significant cost to income advantage.
• Lastly, SMBs should not hesitate from leveraging data outside their organisation for more insights into customer behaviour, operations and financial management.

SMBs can benefit a lot more from big data implementation if they clearly define their goals and do not get sidetracked by the market hype. However, the successes of businesses — large or small — in implementing big data solutions depends requires two things. First, the availability of data, and second, the implementation of right processing technologies.

Now comes the question about how your competitors might be using big data to boost their operations and sales. Well, let’s start with a few prevalent usage scenarios of big data in operations, marketing and sales –

1) Implementing price differentiation strategies: Companies are using customer-product level pricing strategies with the help of big data analytics to achieve targets. According to an estimate, a 1% increase in price can raise operating profits by almost 8.7%. Thus, working out the correct pricing strategy with big data can significantly improve profit margins.

2) Increasing customer responsiveness: B2C marketers are using big data to get greater insights into customer behaviour by using data mining techniques and big data analytics. Proper use of data analytical techniques is necessary in this case. This will help them develop more relationship-driven marketing strategies, prompting greater customers responsiveness and consequently better sales.

3) Big data integration into sales and marketing process: Companies are increasingly investing in customer analytics, operational analytics, fraud and compliance monitoring, R&D and enterprise data warehouses. Nowadays, these are all considered as part of sales and marketing. While customer analytics remains the key area of this investment, evidence shows that developing the other four areas has led to increased revenue per customer and improvement in existing products and services.

4) Embedding AI into big data and its related technologies: The evolving needs of clients and the natural changes brought by big data analytics in sales and service channels has left existing systems gasping for bandwidth while managing tasks. Companies are now turning to artificial intelligence and automation technologies to meet these new challenges. Insights from big data have helped in creating smart and scalable systems which can be used for automated contextual marketing.

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5) Using geo-analytics to go after targeted audiences: Many companies are now relying on geo-analytical data to focus on their go-to-market strategies. Doing this, they are able to capture territories which have greater sales potential and reduce their go-to-market costs.

6) Search Engine Optimisation and Search Engine Marketing: SEO and SEM remain the two areas where the effect of big data analytics is the most apparent. Data analytical techniques have played a very crucial role in this case. Marketers are betting big on SEO, SEM, email marketing, social media marketing and mobile marketing, and believe that these strategies are the key to long-term success.

7) Pan organisational big data insights: Companies are now switching to big data insights for increasing revenue and reducing working capital costs. Big data analytics is helping organizations become agiler in their operations by introducing scalability at an organisational level.

Despite the belief that big data is only beneficial for larger corporations — which are actively generating massive amounts of data — the fact that big data in itself is useless without data analytical techniques makes a case for the use of data analytical techniques in small and medium businesses as well.

10 KEY TECHNOLOGIES THAT ENABLE BIG DATA ANALYTICS FOR BUSINESSES

The big data analytics technology is a combination of several techniques and processing methods. What makes them effective is their collective use by enterprises to obtain relevant results for strategic management and implementation. Here is a brief on the big data technologies used by both small enterprises and large-scale corporations.

1) Predictive Analytics

One of the prime tools for businesses to avoid risks in decision making, predictive analytics can help businesses. Predictive analytics hardware and software solutions can be utilised for discovery, evaluation and deployment of predictive scenarios by processing big data.

2) NoSQL Databases

These databases are utilised for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings.

3) Knowledge Discovery Tools

These are tools that allow businesses to mine big data (structured and unstructured) which is stored on multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and utilise the information to their benefit.

4) Stream Analytics

Sometimes the data an organisation needs to process can be stored on multiple platforms and in multiple formats. Stream analytics software is highly useful for filtering, aggregation, and analysis of such big data. Stream analytics also allows connection to external data sources and their integration into the application flow.

5) In-memory Data Fabric

This technology helps in distribution of large quantities of data across system resources such as Dynamic RAM, Flash Storage or Solid State Storage Drives. Which in turn enables low latency access and processing of big data on the connected nodes.

6) Distributed Storage

A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. Sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.

7) Data Virtualization

It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms, data virtualization is one of the most used big data technologies.

8) Data Integration

A key operational challenge for most organizations handling big data is to process terabytes (or petabytes) of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to streamline data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB and Couchbase.

9) Data Preprocessing

These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. A limitation of data preprocessing is that all its tasks cannot be automated and require human oversight, which can be tedious and time-consuming.

10) Data Quality

An important parameter for big data processing is the data quality. The data quality software can conduct cleansing and enrichment of large data sets by utilising parallel processing. These softwares are widely used for getting consistent and reliable outputs from big data processing.

ORGANISATIONAL GAINS FROM A TECHNOLOGY DRIVEN BIG DATA STRATEGY

Big data analytics plays a significant role in organisational efficiency. The benefits that come with big data strategies have allowed companies to gain a competitive advantage over their rivals — generally by virtue of increased awareness which an organisation and its workforce gains by using analytics as the basis for decision making. Here is how an organisation can benefit by deploying a big data strategy –

1. Reducing organizational costs

Big data solutions help in setting up efficient manufacturing processes, with demand-driven production and optimum utilisation of raw materials. Automation and use of AI to reduce manual work is another way of achieving cost efficiency in production and operations. Further insights into sales and financial departments help managers in developing strategies that promote agile work environments, reducing overall organisational costs.

2. Increasing workforce efficiency and productivity

Data-driven decision making is helpful in boosting confidence among the employees. People become more pro-active and productive when taking decisions based on quantifiable data instead of when asked to make decisions by themselves. This, in turn, increases the efficiency of the organisation as a whole.

3. Setting up competitive pricing

As evidenced earlier in this post, creating differentiated pricing strategies are known to help develop competitive pricing and bring in the associated revenue benefits. Also, organizations can tackle competing for similar products and services by using big data to gain a price advantage.

4. Having demographics based sales strategies

Demographics divide most markets, but there are even deeper divides that exist in customer classification. Big data analytics can help categorise customers into distinct tiers based on their likelihood of making a purchase. This gives sales reps more solid leads to follow and helps them convert more. Furthermore, when sales and marketing are based on big data insights, it is likely that the sales reps are intimated with a potential customer’s tendencies and order histories — driving up the rep’s advantage.

5. Driving brand loyalty

Customers are likely to respond more to relationship-driven marketing. Using data analytics, organizations can leverage their prior knowledge of a client’s needs and expectations and offer services accordingly. Thus, significantly increasing the chances of repeat orders and establishing long-term relationships.

6. Hiring smarter people for smarter jobs

Using big data technologies has become a useful tool for HR managers to identify candidates by accessing profiled data from social media, business databases and job search engines. This allows companies to hire quickly and more reliably than traditional hiring techniques which always have an element of uncertainty. Also, when organizations are using analytics across all platforms, it becomes imperative for them to hire candidates who are in sync with their policy.

7. Re-calibrating business strategies

Big data strategies not only provide better decision-making powers to organizations but also give them the tools to validate the results of these decisions. Organizations can recalibrate their strategies or scale according to newer demands using these tried and tested business strategies.

CONCLUSION

As the rate of data generation increases, even smaller enterprises will find it hard to maintain data sets using older systems. Data analytics services, more than anything, will become the guiding principle behind the business activity. Moreover, companies will need to be more automated and data-driven to compete and survive. The evolution of artificial intelligence with technologies like machine learning and smart personal assistants is also heavily reliant on big data. The role they will play in the future of business management, manufacturing processes, sales and marketing, and overall organisational remains to be seen.

It is not too late for businesses to start investing in data analytics technologies and ready themselves for the future. As the technology becomes more common it will certainly become less expensive to implement. But considering the rewards, early adopters of the technology will surely become its major beneficiaries too.

Don’t forget to give us your ? !

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10 Key Technologies That Enable Big Data Analytics For Your Business was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/10-key-technologies-that-enable-big-data-analytics-for-your-business-e0485e3c17f2?source=rss—-5e5bef33608a—4

Admit it all you wanna-be, newbie, and old-old-school Data Scientists on the planet, whether you like it or not, you’ve probably behaved like one of these types. Or two. Or all eight.

Originally from KDnuggets https://ift.tt/3m9FkFz

In this article, we’ll take an interview-driven approach by linking some of the most commonly asked interview questions to different components of A/B testing, including selecting ideas for testing, designing A/B tests, evaluating test results, and making ship or no ship decisions.

Originally from KDnuggets https://ift.tt/3cHlf6l

Hiring or looking to get hired in Data Science / Analytics? The INFORMS Virtual Career Fair, April 13, is for you. Register today!

Originally from KDnuggets https://ift.tt/3sEctvj

Are you looking to continue your learning of natural language processing? This small collection of 3 free top notch courses will allow you to do just that.

Originally from KDnuggets https://ift.tt/3rEFq9p

Via https://becominghuman.ai/are-there-too-many-data-science-competitions-hackathons-why-the-market-is-saturated-2d8977f70d70?source=rss—-5e5bef33608a—4