I think there is some confusion about the different components - I’ll try to clarify: The tokenizer does not produce vectors. It’s just a component that segments texts into tokens. In spaCy, it’s rule-based and not trainable, and doesn’t have anything to do with vectors. It looks at whitespace and punctuation to determine which are the unique tokens in a sentence. An nlp model in spaCy can have predefined (static) word vectors that are accessible on the Token level.
Powerpoint Usage: Commercial vs Government Firms
Powerpoint is becoming a de-facto form of communication both within and between organizations. This has both pros and cons, but the important thing to emphasize, when you can, is that it is just a tool. As soon as slides become a sacred cow, then you’re no longer delivering value. You’re just pushing propaganda. Commercial can be a useful tool provide talking points a way to start a discussion with the customer leads to key concepts that customer should take away fewer presentations, but similar pitch, supports personal engagement will definitely have a few important numbers, that will be discussed not the place to make promises / commits; that is during contract negotiation and final delivery Government
User Stories vs Data Stories
Agile is used to remove uncertainty from projects, so that the features for development are fairly clear, at least for the current iteration. However, producing an analytics application creates many more tasks because it is focused on data. To remove uncertainty from these data tasks, and the overall project, we keep it separate from development. This ensures that we: i) understand the data, ii) have a clear methodology for addressing problems using the data.
The 'Slack' Form of Communication - From A Business Size Perspective
A colleague of mine has put-off using Slack for a while. She’s totally committed to Skype for Business, which I’m a little revolted by. When I described Slack to her, ‘Instead of separate conversations, you have a continuing discussion or narrative with people. You often end using it as a Knowledge Base’. Her response to that, ‘so I can’t use it for the things that shouldn’t go in writing – got it.
Managing Cloud Infrastructure with Visual Tools
I’m a big fan of using cloud environments, especially for getting a product started. But for early prototyping with a small team, remembering everything that needs to be created / configured, as well as everything that is already performed, can be daunting. To improve this process, I’ve started using visual tools to help document my work. This is a reasonable approach because most aspects of the infrastructure should have a corresponding physical implementation.
The Tech Stack Quick Start Guide
This is a guide to the fastest and cheapest way to get your development project moving forward with typical tools and infrastructure. Jira Jira is one of the oldest applications for applying Agile to a Team’s workflow. This is a really simple tool that comes across as overly-complicated because of all the available functionality. Stop trying to appease your corporate masters, Atlassian! New and simplified UIs are rolling-out to fix this.
Why Data Scientists Need Some Fundamental Front End Skills
Most data scientists are lucky to have a basic understanding of HTML, or web applications, in general. However, this dearth of knowledge can be quite limiting as their career grows. They will not be able to grow their skills more broadly to areas such as data visualization or web scraping. They will also have difficulty leading mixed teams delivering complete solutions. They can be easily relegated to creating models of simple reports.
Public vs Private Sector Employment
Comparing offers between the federal government and private sector can be very difficult. It is more akin to choosing between an academic career than it is a choice with another commercial employer. Because of the benefits the government offers, it is very much an apples and oranges comparison. Here are three huge benefits of working with the government: Retirement - occaisionally, some very large, older firms (insurance or banking) will have a pension system.
Difficulties in Working with Contractor Teams on Data Science Projects
Working with contractors can be very helpful in some situations. There is an understanding of support without long-term commitment. However, giving too much control to contractors can be very dangerous because their incentives are often quite different from yours. This is especially true in Data Science projects because of the long-term investments that must be made. There are several areas of caution which should be addressed to ensure the project is kept under control.
What to do after an initial MVP: The 'Throw One Away' Method
You built it, it works, but you’re far from monetization or even consistent users. So, how do you proceed? Below is a list of questions to ask yourself. Did you solve the problem for which you originally targeted? Is the original problem that you intended to solve still important? Oftentimes, you may need to decompose the problem in greater detail: What aspects of the problem are users most concerned? The greater problem depth will lend to 2-3 detailed solution methodologies: How (by what criteria) should we best solve it?