Time is the most valuable asset, use it wisely. That is why you should prove your ideas before implementing them. Programming is time consuming, I love programming but I avoid writing code whenever it is possible. A perfect written software, that nobody wants, is just a waste. To identify if an idea worth implementing, I use these tactics.
What non technical methods I eventually learned to build a maintainable and extendable project? What Is Project Organization? The project organization is the structure of the project. It's created separately, with specialists and workers from ...
You, and you alone managed to birth your solution, despite the ambitious deadlines and resource constraints. The solution works - it’s standing tall on its own, but you know that it is but a gentle gust of wind away from needing your delicate and nuanced maintenance.
There are lot of things which i learned in my life and looking forward to learn more onn. The one thing i learned is being a Team Player. We all had our git branches, created 4 columns of To-Do,In-Progress,Review,Sprint1,Sprint2 and Done.
In this article, I’ll explore how to create a well-defined data science process.
Kickstarter with MLOps. Classifying success of kickstarter projects using PySpark and TensorFlow.
As you create the plan for your machine learning project, it's important to consider some emerging best practices. Machine learning (ML) and Data Science (DS) projects are hard to manage.
How to Plan and Organize a Data Science/Analytics Project? Conducting a data science/analytics project always takes time and has never been easy.
In the 1930s, the Toyota Production System gave us lean manufacturing principles. Now, the IT, software and web development industry have also adopted these principles to improve their production processes. In actuality, the concepts and principles of Lean are used in more ways than just in manufacturing. Yet, in IT and software, there are still those who are pointing towards Agile development when they mention Lean and software development in the same context. While it is true that Agile and Lean principles share similar philosophies, there are key differences which set them apart. Diving deep inside Lean, I will discuss what lean talks about other than it’s key points.
Modern software development methods don’t prevent you from the everlasting question.
What is technical debt? “Techinical Debt” comes from the mouth of Ward Cunningham, he first used the technical complexity ratio as a liability, referred to as “technical debt”.
And they have nothing to do with your technical skills. Finally, all data were cleansed and ready to analyze. Andy started overenthusiastically to visualize the data to get a first impression of the data.
“In these unprecedented times…” People build unprecedented products, and contribute to the internet in unprecedented ways.
While we may not be very helpful when it comes to food, we can help you in ensuring that your software development and deployment happens within the set timeline.
Three hackathons and raced for two acceleration programs. Having some experience in national and international scale. First 30 and the first place for two projects on a national scale. Now, I’ll tell you what I learned about participating in hackathons.
Monitoring is an important part of project success. It includes looking at quality of data being collected, identifying gaps in data collection, and creating monthly reports to show the program standing. For anyone who oversees the data management component, there is always the question of what software to use. The dilemma lies in choosing a tool that is affordable for the agency, is multifunctional (statistical analysis vs visualization), and requires less technical knowledge/coding to operate.
What project manager should know about data science and AI projects. Spoiler: If you are starting with requirements, think again
Many of us are struggling to prioritize our learning as a working professional or aspiring data scientist. We’re told that we need to learn so many things that at times it can be overwhelming.
Why Scrum is awful for Data Science. More and more data science teams seem to be hopping on the scrum bandwagon, but is it a good idea?
Bad practices often occur when it comes to productive CF infrastructures. However, these guidelines should help everyone who runs or uses any of the production-ready workloads.