In the data science world, people tend to think cleaning data is boring and desire more of machine learning and modeling challenges, but sometimes some problems might arise like a fun brain teaser or an algorithmic puzzle during data cleaning. Here’s a fun one I recently ran into when I am cleaning an event table. It’s not anything like crazy ML algorithms or new cool python tools but a small interesting problem worth a couple of minutes to figure out.
The event table is pretty common when we are building any kind of data pipeline or ML pipeline, especially when we are dealing with prediction problem. An event table is like a transaction table that normally has a start time, end time, person ID, and other event attributes. Each row is an event and some events might overlap which could be a problem.
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It is essential to keep the correct time on a server. This is especially true when it comes to processing financial transactions or other vital functions which need to be handled in a specific order. Using the Network Time Protocol (or NTP), computers can synchronize their internal clock times with the internet standard reference clocks. In essence, NTP is a hierarchy of servers. The higher the Stratum number of a server, the more accurate the timing is and the lower the Stratum number of a server is, the lower the accuracy and time stability. Stratus are defined by the distance from the initial reference clock.
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Dealing with dates and times in Python can be a hassle. Thankfully, there’s a built-in way of making it easier: the Python datetime module.
datetime helps us identify and process time-related elements like dates, hours, minutes, seconds, days of the week, months, years, etc. It offers various services like managing time zones and daylight savings time. It can work with timestamp data. It can extract the day of the week, day of the month, and other date and time formats from strings.
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In this video, I will be talking about problem-solving as a developer.
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Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.
The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.
While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.
In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.
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