- Collect Information
- Building a Knowledge Tree
- Choosing a Project Structure
Let's dive right in!
# Collect Information
A vital phase when starting a project is to search for information that can help you. These can be of any kind:
- Existing projects
- Research Papers
This guide will not teach you how to use Google (opens new window), because you will already know if you are reading these lines 😄
By the way, probably not everyone knows about these Google tricks (opens new window)...
But there are a few tips that can come in handy when you start "amassing" knowledge that will then come in handy.
In this guide, we'll make heavy use of a collection of awesome best practices from the PLOS | Public Library of Science (opens new window).
Do you know the joy when you find a giant diamond cluster in Minecraft?
If yes, you need to know that you've just hit a big one: take your time to explore the Ten Rules Collection (opens new window).
# Understand what you're looking for
The first thing to do when dealing with a new problem is to make sure you're looking for the right things. Are you sure the problem is called that for example? Image segmentation is different from image classification! This Google guide (opens new window) can help you be sure of the name of your problem.
This may seem trivial, but many useful resources are not found because the correct keywords are not typed into the search engine.
Consider the use of the 5 Whys technique (opens new window) to better understand the problem you're trying to solve.
# Don't re-invent the wheel
Are you sure that someone hasn't already solved your problem? In that case, if you needed it to solve a real problem you'd already have the dish ready, while if you're doing it to learn you have a base from which to start! Also, observing the code of others is very effective for learning. In the latter case, it is still advisable to try to re-implement the solution.
To look if someone has solved the same problem the first place to look is Github (opens new window), the platform where every developer puts Open Source code. Another interesting place can be Kaggle (opens new window), the site of the Data Science challenges, where thousands of practitioners and experts challenge each other on real problems, and whose works are available in the form of Notebooks.
Let's suppose for example that I want to solve a problem related to time series: I can type on Kaggle "analysis of time series" and I will probably find dozens of Notebooks that show how to solve a similar problem, and from which you can observe the approach. What a great source of inspiration!
Also check out TensorFlow Hub (opens new window), ModelZoo (opens new window) and Papers with Code (opens new window). These three platforms are full of pre-trained models that can come in handy, or even solve your problem already! 😃
# Find communities
Join communities of people interested in the topic (e.g. Reddit (opens new window)): here you can find discussions, search by keywords (e.g. "time series analysis"), and ask questions, with experts who will answer and help you.
Try to form specific, well-written questions, to minimize the time used by the respondent. For example, the question "how do I analyze a time series?" is too general, and a short Google search is all it takes to get the answer.
Instead, a question like "to analyze a time series and train a model that predicts 2 steps forward in the future, is it better to approach X or approach Y?".
If the questions are too general or show laziness they'll likely remain unanswered...
Some subreddits you can subscribe to are:
- r/MachineLearning (opens new window)
- r/LearnMachineLearning (opens new window)
- r/DeepLearning (opens new window)
- r/DataScience (opens new window)
- r/LearnDataScience (opens new window)
Two other good places to post (well structured) questions are:
# Building a Knowledge Tree
Given the speed of scientific research in the world of data, every day a new approach to your problem could be discovered that proposes a much better solution than the previous one. The only way to get up to date is to read research papers!
Reading papers is difficult though, they are often full of mathematical, and statistical concepts, with complex theories. The important thing, however, is to be able to understand the concepts, and maybe try to apply them to your problem.
Also often remember that Papers With Code (opens new window) collects the code to implement (almost) any paper!
Often already after a couple of days from the release, there is code available in various frameworks, ready to be tried on your problem.
However, when you are confronted for the first time with a new problem you do not know which paper to start with, also because usually, the papers refer to all previous papers that have tried to solve the same problem, and assume that the reader has some kind of knowledge about the problem.
So what to do?
Use the Papers Tree strategy:
- Find the last survey paper about the sub-field of Data Science you're trying to solve
- Read carefully this paper, and understand which are the foundations and try to figure out which are the most important papers the sub-field is based on. Usually, the history of the field is covered, citing the most important papers, and this gives you an overview of which were the important steps of the research, up to the state of the art in the approach to the problem.
Following the example above, this paper -> A Survey of the Recent Architectures of Deep Convolutional Neural Networks (opens new window) contains a detailed map of the most important papers on convolutional neural networks (neural networks that work well with images and videos) and their evolutions, up to the most advanced architectures.
Now you just have to look for the most important (or interesting) papers mentioned, organized in a time-aware tree!
A good practice is to use Zotero (opens new window), a document manager that allows you to keep track of all your research papers.
You can then repeat this process in a more specific way, for example by looking for a survey paper on convolutional networks applied to the diagnosis of medical images.
Once you collected the most important papers for your research, document your exploration!
Tools like MindMup (opens new window) can help you in this task.
Consider the Rhizomaps (opens new window) approach too, it really helps in dumping our thoughts on paper.
Before reading any paper read this! (opens new window)
It's a paper that explains how to read a paper. Yes, Virgilio loves recursion.
# Choosing a Project Structure
Choosing a project structure is vital to managing the complexities that result from the evolution of the project. Without a clear structure, you'll find yourself with randomly scattered files, dataset versions with similar names, so much so that it hurts your head!
Well organized code tends to be self-documenting in that the organization itself provides context for your code without much overhead.
People will thank you for this because they can:
- Collaborate more easily with you on this analysis
- Learn from your analysis about the process and the domain
- Feel confident in the conclusions at which the project arrives
But the first person to thank the ordered project structure is you!
When we look at the code we wrote months ago, we often don't remember anything!
"Mmmm... I don't remember if the good file was analysis.py, analysis_final.py, analysis_1.py" 😄
For these reasons, good people have developed a fantastic project, Cookiecutter (opens new window), which wants to standardize the structure of projects by providing a sensible and flexible template.
To create the project skeleton just install the package:
pip install cookiecutter
and then use:
You can customize the template according to your needs, just clone the repo, modify it, and then use:
cookiecutter https://github.com/...... your-repo .....
Cookiecutter projects have the following structure:
# Metadata and file names
Another important issue with regard to the overall order of the project and the management of its complexity is the management of the data and metadata associated with them.
This awesome cheatsheet (opens new window) contains everything you need to know about data management and file names best practices: keep it under your pillow!
Why are we talking about reproducibility (opens new window)?
The field name in the Data Science indicates that the work process is scientific (Data Science, even with software as a component, is not pure software, which is reproducible by definition).
Reproducible experiments are the foundation of every scientific field and, indeed, even the scientific method itself.
Karl Popper said it best in The Logic of Scientific Discovery (opens new window): “non-reproducible single occurrences are of no significance to science.”
If you’re the only person in the world who can achieve a particular result, others may find it difficult to trust you, especially if they have spent time and effort attempting to reproduce your work.
It is reckless and irresponsible to build a product or theory on a singular unconfirmed anecdote, and if you present anecdote as a reliable phenomenon, it can consume time and resources that would otherwise be spent on actual productive work.
Reproducibility has a number of indirect advantages, in addition to being sure to present good results (analysis or model predictions):
- It saves time in various ways, for example by saving the intermediate steps of data processing and cleaning, so that you don't have to redo all the steps
- Allows you to automate various parts of the project workflow
- Allows others to reproduce results
- Allows others to understand each phase without confusion
- Reproducible design is easier to document
- Allows you to take over the project after months or years, and be sure to get the most out of it
Here you can find articles and papers that explain to you how to ensure a high reproducibility across all the phases of the project:
- Reproducibility in Science (opens new window)
- Replicability is not Reproducibility: Nor is it Good Science (opens new window)
- Best Practices for Reproducible, Collaborative Data Science (Video) (opens new window)
Once you've walked through the above resources, you'll be equipped with best practices to ensure that your code will be highly reproducible, and again, people will be grateful to you!
Especially, the future yourself will be happy in finding reproducible and automated results, months later or years !!!
- Ten Simple Rules for Reproducible Computational Research (opens new window)
- Ten Simple Rules for Reproducible Research in Jupyter Notebooks (opens new window)
In order to make reproducible projects, and also for peace in the heart of every programmer, learn to use Git (opens new window)!
Git is a versioning system that allows you to always have under control every change in your code, be able to go back, and be sure that your code will never be lost!
Git is defined as Distributed Version Control System: What does it mean?
Control System: This basically means that Git is a content tracker. So Git can be used to store content — it is mostly used to store code due to the other features it provides.
Version Control System: The code which is stored in Git keeps changing as more code is added. Also, many developers can add code in parallel. So Version Control System helps in handling this by maintaining a history of what changes have happened.
Distributed Version Control System: Git has a remote repository that is stored in a server and a local repository that is stored in the computer of each developer. This means that the code is not just stored in a central server, but the full copy of the code is present in all the developers’ computers. Git is a Distributed Version Control System since the code is present in every developer’s computer.
Any existing software project that is not under version control is considered a dead project, and the responsible developers are considered crazy.
Data Science projects (which make heavy use of software) are no different, indeed!
They also have the additional problem of data versioning, which is the raw material on which you work most.
Always having the versions of the data, from raw (just collected) to clean, keeping every intermediate processing phase, is perhaps the most important best practice (opens new window) when doing a Data Science project.
Here (opens new window) you can find a simple guide to Git. Learn it, it's freaking worth (and necessary).
Documenting your work with Git is crucial: read How to Write a Git Commit Message (opens new window).
Like any project, documenting the work done is fundamental to the success of the project.
We don't need to list the benefits that good documentation brings to a project, so we immediately understand what are the best practices to keep in mind when we produce documentation for our projects.
This guide (opens new window) explains in detail how to document data collection and its organization.
You can choose among different ways to document your project, but Virgilio recommends you to use Sphinx (opens new window), the official Python automated docs library.
Remember that documenting your code and project steps it's NEVER wasted time.
After reading this guide and the resources it contains, you should be equipped with all the necessary best practices when starting a new Data Science project.
In the next sections of Purgatorio, you will begin to put these practices into practice, and you will see how grateful you are to yourself!