Data science and artificial intelligence go hand in hand: both feed and enrich each other. The possibilities of automating data analysis open the door to definite benefits for tomorrow's "augmented" data scientists. So, what are the benefits of automating your data analysis? Here is why you should get started.
Automation of data analysis: What is it all about?
With the development of artificial intelligence and technologies, the possibilities for automation are becoming more extensive.
Can everything really be automated?
First of all, we need to remember the work process of the data scientist. The management of a data science project goes through the following chronological steps:
- Data preparation: the collection, cleaning and exploration of data;
- The creation of a model: the selection of the algorithm, the choice of parameters, testing and evaluation of the model;
- The production, deployment and monitoring of the model.
However, each of these phases of work is not of equal importance. Indeed, the data preparation phase takes up most of the data scientist's work time. It is estimated that he devotes between 50 and 80% of his time to it! This is essential for working on "healthy" data.
Nevertheless, this often slow and tedious work could be made easier by using the machine. This is where artificial intelligence could play a facilitating role.
Concretely, not all phases can be automated. Some are more suitable than others.
For example, it is difficult to automate the beginning of a data science project, i.e. the selection of relevant data. It is up to man to sort the right data and give direction to his work.
Another example, data cleaning can be semi-automated, depending on the nature of the source. For example, the learning machine can help when searching for outliers or anomalies in a dataset. The search for a data analysis model also lends itself well to automation.
The data scientist is not dead
Let’s face it: the data scientist is not interchangeable with a machine. It is utopia to believe that it can be replaced by the machine, even in the long term. His experience of data science projects and his intuition are irreplaceable in knowing what to do and how to do it. In the same way, the data scientist will never be able to compete with power and speed of a machine to handle the complexity and specificity of the data specific to each project! Each project is unique, and the data scientist must adapt to the project each time.
Another important point to remember is that the data scientist is the link between all the actors in the project, coordinating the teams and making them collaborate. He is the one who supervises the management of the project at all stages, understands the client's needs and requests and knows how to explain the project to the client. He constantly keeps an eye on the project's progress and can find solutions in case of deadlock. He also has this managerial role which is crucial for the project to come to an end. No machine will be able to replace it by simply "mechanically" executing the steps to be followed.
And this is all the truer since, as we shall see, the artificial intelligence techniques on which data scientists can rely are still fairly experimental. Presumably, for the time being, they are only tools to assist the data scientist.
Which instruments to automate data science?
The automation of the stages of the "pipeline" of a data science project is based on auto-ML (automated machine learning) techniques. Most auto-ML solutions take the form of APIs (Application Programming Interfaces). These techniques mainly use 2 systems: AUTO-SKLEARN and TPOT.
The AUTO-SKLEARN system: it is one of the most complete. It is based on optimization possibilities, including Bayesian optimization, meta-learning, and the creation of model sets. Its main criticism for data-scientists is a relative rigidity of the model.
The Tree-based Pipeline Optimization Tool (TPOT) system is "your data science wizard" as its vendor puts it. It is a system offering a much larger number of models than the AUTO-SKLEARN system in the form of trees of arbitrary complexity. It uses a genetic algorithm. Nevertheless, this model is still experimental.
The 4 benefits of automating data science
Here are the 4 main advantages of automating data science.
# 1 Conduct data science projects faster.
This is perhaps the greatest asset of artificial intelligence.
By delegating certain tasks to the machine, the data scientist wastes less time performing time-consuming, repetitive, and sometimes thankless tasks. Without losing in efficiency, benefiting from automation tools makes it possible to do more in less time! For example, the data scientist can try more things in a shorter period of time, where he might have postponed some ideas due to lack of time or simply wasted time finding the right option manually.
# 2 Be more productive
The data scientist thus saves time on these uninteresting tasks. This time can be allocated to other activities. He can devote himself to tasks with higher added value without being exhausted on those of lesser importance.
These tasks with high added value are those that really require a human hold because of their complexity. They are more rewarding and more interesting. For example, a data scientist will always be needed to drive the machine and ensure the smooth running of the project. In the end, automation allows a more efficient redistribution of tasks between human and machine.
# 3 Getting better results
Globally, having access to tools using the latest artificial intelligence technologies makes you more efficient than staying "all manual". It is quite obvious, but the data scientist assisted or assisted by the machine becomes what we could call an "augmented data scientist".
By multiplying the possibilities of data interpretation, the data scientist acquires a perfect knowledge of it. With this high level of information about the data he or she is handling, the data scientist can make better decisions and produce more informed results.
It should also be noted that these artificial intelligence tools reduce the risk of human error.
# 4 Democratizing data science
Automation techniques also pursue this avowed goal: to democratize data science to make it accessible to neophytes.
In other words, in order to manipulate algorithms, one should no longer need advanced and complex knowledge of statistics and/or computer programming. This would make it possible, for example, to cope with the shortage of manpower in the field. For the time being, this idea of democratizing data science is still a myth, and we are still far from it!
More generally, it can nevertheless be said that the automation of data science makes it possible to speed up the establishment of a data science culture. It makes these projects, sometimes seen as obscure, more accessible and leaves room for the possibility of developing them more easily in companies.
If the automation of data science still has a bright future ahead of it, it will nevertheless remain limited to its role of assistance in data analysis. Complementary to the data scientist, it is not intended to replace him. This is especially true because only he knows how unique each data science project is.
La Ryax Team.