#DataWizard

[datamindsjourney]

Data Science is a dynamic and interdisciplinary field that harnesses the power of data to extract meaningful insights, facilitate informed decision-making, and solve complex problems. At its core, it combines elements of statistics, computer science, and domain-specific expertise to analyze and interpret large volumes of data. The data science process typically involves collecting, cleaning, and transforming raw data into a structured format suitable for analysis. Advanced algorithms and machine learning techniques are then applied to uncover patterns, trends, and correlations within the data. The results of these analyses empower businesses and organizations to make data-driven decisions, optimize processes, and gain a competitive edge. With its ever-growing importance in today's technology-driven world, data science continues to evolve, pushing the boundaries of innovation and shaping the way we approach and understand data.

[ficusofttechnologies]

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[cacmsinsitute]

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[beawarephiladelphia]

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[mediasynced]

Negative uplifts

This article about negative uplifts belongs to a special series of blogposts, written by our own data wizards. It will offer you a glimpse into the engine room of Mediasynced. In these informative blogposts, we shed a light on the complexity of TV performance measurement in realtime and our robust statistical solutions.

One of the primary ways to evaluate the effect of your commercial is by comparing the performance before and after airing your commercial. A baseline is calculated based upon the performance before the commercial and is then subtracted from the performance after airing. The difference is then called the uplift. This process can be done for all kinds of metrics: we can calculate the uplift in the number of sessions on your website, the uplift in conversions and many more.

While this method can give you a good estimation of how well your commercial performed, it is not perfect. One of the most obvious flaws is that it can result in negative uplifts: cases where the performance after airing a commercial is worse than before. It is quite unlikely that users will start boycotting your products after seeing your commercial, so what is going on here? Is it even possible for a commercial to have a negative effect?

What is actually happening is that the commercial did not have a noticeable effect and the results you are seeing are actually due to noise. There is no reason to be alarmed, you did not actually lose any potential customers due to your commercial.

In the example shown above the goal is to calculate the uplift in number of sessions on your website after airing a commercial. The number of sessions is not static, it changes throughout the day in an unpredictable manner. In the graph we can see that right before the commercial aired, the number of sessions started dropping. The effect of the commercial was not enough to compensate for this drop, resulting in your actual performance being lower than the calculated baseline.

So now that we know that these negative numbers are not representative for what is actually happening, what can we do to improve them? As discussed above, the problem is that your commercial did not have a significant effect. One method that some of our competitors use, is to set all negative uplifts to 0. While this sounds like a good solution, it is statistically unsound. There are also cases where the exact opposite is happening such as the example shown below. 

The effect of your commercial is also insignificant in this example. The number of sessions started increasing right after the commercial was aired. This is not an effect of the commercial, but caused by random fluctuation in visitors. However, this situation will result in a large uplift. If you set all negative uplifts to 0, then you must do the same for the positive-uplift case (which is much harder to identify) where the effect of the commercial was insignificant, yet the uplift was still large. If this is not done your results will be positively skewed. Your overall results will be much more positive then what is actually the case. Often this results in surprisingly large uplifts for small channels, as these channels are much more likely to have small effects which may result in incorrect positive and negative results. The negative results are set to 0 so you are left with an average uplift for this channel much higher than what is actually the case. When you leave these negative numbers as they are, incorrect positive and negative results will cancel each other out over a campaign period. This results in a much more accurate estimation for a channel.

So if we can’t ignore these numbers then what can we do? The statistical reality is that we simply can’t know for certain whether a session or conversion is caused by your commercial or has a different reason. As this problem is a fundamental one in all of statistics and is not only limited to the problems in our branch.

What can be done is limiting the effect of this problem by employing methods that filter the real data from these noise and predictive models that can make strong estimations of the real data.

Mediasynced is constantly improving its methods and models using the statistical and Artificial Intelligence tools that we have developed to provide an accurate prediction. Such an example is the method we use to calculate the baseline. You can read about this method here.

Hopefully, you’ve enjoyed reading our second blog post of this special series. Every month we will release a new article, so stay tuned!

[bestblockchainby-blog]

Changelly крадет у своих пользователей Monero, но это не точно

Некоторые пользователи Changelly обвиняют криптовалютную биржу в воровстве Monero (XMR). Платформа Changelly, получившая широкую популярность, благодаря удобной опции ускоренной конвертации криптовалюты, не имеет недостатка в пользователях: в последнее время их число только увеличивается. Однако, появившиеся впервые еще в конце прошлого года на Reddit обвинения в воровстве пользовательских XMR снова дали о себе знать. Пользователь Reddit под ником @datawizard недавно снова обратил внимание на эту важную проблему, опубликовав официальный пост Changelly, в котором говорится: «Повторяем, что наша система управления рисками может приостановить некоторые подозрительные транзакции для проверки. Отдел безопасности прилагает все усилия, чтобы обработать такие операции за минимальное время. Когда клиент отказывается предоставить требуемые данные, мы не можем вернуть ему криптовалюту, так как не уверены в том, что она не была украдена или добыта мошенническим путем» Пользователь @datawizard выражает недовольство тем, что Changelly считает своим правом не возвращать XMR, просто назвав транзакцию «подозрительной». Для такой свободной криптовалюты, как monero, которая гарантирует неприкосновенность частной жизни, ярлык «подозрительная транзакция» может оказаться слишком тяжелым. Четыре месяца назад на Reddit также появился пост пользователя @moneroexchange13, который был разочарован, что биржа признала так называемые требования «Знай своего клиента» (KYC) и рекомендации по борьбе с отмыванием денег (AML). Он также был недоволен отсутствием возможности  дать транзакции обратный ход и неосведомленностью службы поддержки Changelly в данных вопросах. Приводя доводы в свою защиту, Changelly объясняет, что её система управления рисками не предусматривает «п��ревод транзакций за предел синей линии» (включение в следующий блок) без предварительной проверки, а, из-за анонимности XMR, чаще всего проблемы случаются именно с ней. Биржа заявляет, что не имеет предвзятого отношения или особенного недоверия к XMR, просто биржа следует принятым ею нормам KYC, которые призваны защитить платформу участия в операциях по отмыванию денег. Некоторые пользователи, однако, выступают в защиту Changelly, советуя тем, кто не согласен с KYC, держаться подальше от платформы или использовать её для конвертации меньших сумм, так как обсуждаемая проблема связана с большими транзакциями. Однако, остается открытым вопрос, почему Changelly не уточняет, по каким параметрам она отмечает сделку как подозрительную, хотя подтверждает, что, если транзакция не проходит проверку безопасности, то биржа удерживает все средства. Напоминаем, что недавно Японское агентство финансовых услуг (FSA) объявило о полном запрете криптовалют с высокой степенью анонимности, под который попала и XMR, наряду с Dash (DASH), Augur (REP) и ZCash (ZEC). Источник Read the full article

[programming-wizard]

WHAT MAKES YOU FEEL BETTER WHEN YOU ARE IN A BAD MOOD?

I love messing around with the coding on the programs on my personal MANA. Since I’m head of the programming department, I get the only MANA that’s easily customizable without jailbreaking it. I have access to some pretty freaking broken spells of my own design, good thing these aren’t available to the general public (or M1NDH4CKZ0RZ INC) or we might be in a lot of trouble. Hahaha.

[mediasynced]

The baseline

This article about the baseline belongs to a special series of blogposts, written by our own data wizards. It will offer you a glimpse into the engine room of Mediasynced. In these informative blogposts, we shed a light on the complexity of TV performance measurement in realtime and our robust statistical solutions. Hopefully you will enjoy reading the very first blogpost of this special series. 

The common way to measure the performance of TV spots, is to measure your website in the minutes following a commercial and comparing these results to the performance that we expected without a commercial. This process is called benchmarking. Your first step would be to measure the amount of sessions your website has after the commercial. The next step is to create a benchmark, also called a baseline to compare your results with.

Linear Baseline

The simplest method would be to measure the amount of sessions on your website before you aired the commercial and take that number as the baseline. While this solution may sound intuitive and adequate in theory, in reality it is just not accurate enough. The first problem is that your benchmark is static. You assume that it won't change during the period after your commercial up to the point you stop measuring.

To fix this problem you can use two measure points instead of one. In addition to the measure point before airing the commercial you now also measure the point after the effect of the commercial has faded. Now you can simply draw a line from the first measure point to the second and use this line as your benchmark. The problem here is identifying when the effect of your commercial has faded. We will discuss this problem in a future blog.

Another problem is that this benchmark is based on a short snapshot of your website’s traffic. In such a short timeframe, sudden variations in your website visits can occur that will distort the baseline calculation. So the measured baseline may no longer be an accurate representation. This can result in significant higher or lower measured uplifts than what is really the case. So adding an additional measure point does not change anything. Now your baseline is simply based upon two potentially inaccurate points.

Average baseline 

So why don’t we take the average traffic of each specific day and minute combination? Well that model is flawed because there is no such thing as an average Monday. Seasons, the weather, events, etc., can all heavily influence the baseline.

Our Baseline

At Mediasynced, we have taken a more sophisticated approach in order to provide an accurate baseline. For each client, we build specific day models. These models are based on historical data and take several factors into consideration. They are then aligned with the actual data with the use of a y-intercept. Using this method we can construct a baseline that follows the natural curve we expect it to have, so based on the organic rhythm of the website throughout the day. If we expect the traffic of your website to rise slightly from 16:10 to 16:20, then the constructed baseline for that time frame will also rise slightly. To increase the accuracy we have splitted our data up by device and repeated this process for each of those devices. Using these methods we were able to construct a baseline that correlates better with reality and is less affected by the noise within the data.

In the example below, we see that the number of baseline sessions (real baseline) is oscillating around an average of 10 sessions per minute. During the commercial the baseline is starting a positive peak which the two simple baselines fail to capture. Our baseline on the other hand can correctly predict this pattern and recreates this peak as best as possible.

Hopefully, you’ve enjoyed reading our first blog post of this special series. Every month we will release a new article, so stay tuned!