From my studies of general mathematics I know that objects are defined by their properties, not names. Semantics matters, and what's better to clarify a meaning than a formula codifying a definition? In many companies, people casually use terms like customer, product, transaction, sale, without common agreement on the details. How many times have we discussed who is a customer at various meetings? These conversations get even trickier when colleagues from different departments are involved and share their point of view.

Despite a poor market performance in Japan, Tesla CEO Elon Musk confirmed on October 3 that Tesla's Full Self-Driving suite will soon arrive in this Asian market. A Japanese Tesla owner @Model3teslaJ tweeted on October 3 asking Musk when the FSD suite would make its way to Japan. The tweet says: "Elon, in Japan, we are still waiting for Navigate on Autopilot, Smart Summon, and FSD visualization preview. When will we get all these features?" The electric automaker's chief simply replied: "Coming soon."

Forecasting is a technique that uses historical data and events to build estimates about future trends, potential disasters, and the overall behavior of any subject. Forecasting can be used as probabilistic support for decision analysis, to estimate expenses, revenues, and budget plans. Forecasting in business can be divided into two distinct categories: qualitative forecasting and quantitative forecasting. For more information, you can take a look at Investopedia's Financial Forecasting primer. Both types of forecasting have shown a lot of promise and managed to create business enhancements for many entities.

Time series are one of the most common data types encountered in daily life. Financial prices, weather, home energy usage, and even weight are all examples of data that can be collected at regular intervals. Almost every data scientist will encounter time series in their daily work and learning how to model them is an important skill in the data science toolbox. One powerful yet simple method for analyzing and predicting periodic data is the additive model. The idea is straightforward: represent a time-series as a combination of patterns at different scales such as daily, weekly, seasonally, and yearly, along with an overall trend.