Variability is inevitable but you can control some of it. There are times when the supply chain works effortlessly, and other times ... helps managers maintain their cool when something goes wrong.
Imagine practicing hitting a target using darts, a bow and arrow, pistol, cannon, missile launcher, or whatever. You aim for the center of the target. If your shots land where you aimed then you are considered to be accurate. If all your shots land near each other, you are considered to be precise. The two properties are not linked. You can be accurate but not precise, precise but not accurate, neither accurate nor precise, nor both accurate and precise.
Accuracy and precision also apply to statistics calculated from data. If you’re trying to determine some characteristic of a population (i.e., a population parameter), you want your statistical estimates of the characteristic to be both accurate and precise.
The same also applies to the data themselves. When you start measuring data for an analysis, you’ll notice that even under similar conditions, you can get dissimilar results. That lack of precision is called variability. Variability is everywhere; it’s a normal part of life. In fact, it is the spice in the soup. Without variability, all wines would taste the same. Every race would end in a tie. Even statistics might lose its charm. Your doctor wouldn’t tell you that you have about a year to live, or say don’t make any plans for January 11 after 6:13 pm EST. So a bit of variability isn’t such a bad thing. The important question, though, is what kind of variability?
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science; Content-Based Recommendation System using Word Embeddings; How Natural Language Processing Is Changing Data Analytics. Also this week: The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science