Data Science and its applications are now part of our daily lives. We hear about it every day, and in all sorts of ways: Artificial Intelligence, Big Data, Machine Learning. But how can you find your way around? We'll help you understand by answering your main questions: What is Data Science? How does it work? What are its applications? And much more!
What is Data Science?
Data Science brings together a set of methods and tools for extracting, manipulating, analyzing and interpreting large volumes of data. It calls upon skills in mathematics, statistics, programming and data visualization to solve complex problems and make decisions.
How can we describe Data Science?
One of the reasons why Data Science and its professions are so sought after on the market is the transversal nature of the skills required to master the discipline. Finally, Data Science is at the crossroads of three major areas, all of which are complementary in the context of a Data project.
What are the applications of Data Science?
The application areas of Data Science are numerous, ranging from finance to healthcare, energy to social media. The applications of Data Science are countless and are now part of our daily lives.
- Facebook's image recognition: no need to enter the names of people tagged in a photo, Facebook does it for you, right?
- Netflix: a recommendation engine created on the basis of what you have already liked, or watched previously!
- Uber: Uber's algorithm will allow your driver to pick up an extra person on the way without making you take a big diversions.
In the enterprise, these same applications are also used for 4 distinct purposes - among others:
- Prediction of phenomena such as predicting purchasing behaviour on given products
- Optimisation of processes such as pricing
- Automation of tasks such as automatic driving
- The creation of new data, for example with the use of GANs
What are the differences between Artificial Intelligence, Big Data and Data Sciences?
These keywords are part of the Data Science jargon, and contrary to popular belief, they are very different from each other!
The particularity of Artificial Intelligence lies in the set of techniques used to imitate the mechanisms of the human brain, whether it is to recognize images with Deep Learning, to make prediction models on various phenomena thanks to Machine Learning (weather, buying behavior, etc.), or to process text in order to spot abusive comments, for example. The applications are countless, especially since the necessary technologies are accessible to everyone.
As for the term Big Data, it refers to all the methods used to analyze a much larger volume of data. While traditional data management tools do not allow this, evolving in a Big Data environment requires much greater computing power.
The technologies used are different. Learn more about Big Data with this article.
Finally, we are regularly asked the difference between Data Science and Business Intelligence, or between Data Science and Data Analysis: each time they are cousin concepts but not identical!
What are the main technologies used in Data Science?
- Programming - Python & R are the 2 major languages with which you can code your algorithms!
- Data Visualisation - Tableau is a tool that allows you to create interactive dashboards to better understand your data or present your analysis results to your managers.
- Machine Learning & Deep Learning - Programming in Python will allow you to code algorithms to make predictions, do image recognition. Of course, behind Machine Learning there are important statistical concepts that you will need to be comfortable with. Do not hesitate to consult our article on how to become a data scientist without an engineering degree.
- Web Development : Learn how to build web applications with the Flask framework.
- Big Data: Learn to work in Big Data environments with Spark or Hadoop frameworks. You will be required to work on Cloud platforms such as AWS (Amazon), Microsoft Azure, or Google Cloud Platform.
Are data science jobs hiring?
Why train in Data Science? According to a recent study by Tencent, for one person trained in AI, there are 10 jobs to be had! These jobs in analytics are more and more in demand (Data Engineer, Data Scientists, or Data Analyst: all the jobs explained in this article) because the AI projects of companies are flourishing, for example in the constitution of their Datalab.
SMEs, start-ups and large groups are all looking for such profiles, and all sectors are concerned: there are as many applications of Data Science as there are business problems. The gap between supply and demand in the Data professions is such that salaries are largely impacted, recruitment agencies specialising in Data are also flourishing, and companies are redoubling their efforts to keep their Data talents in-house!
In short, a Data Scientist is never left without a project. We've created a guide to getting your Data Science projects done.
How to get trained in Data Science?
Data science is not a fad, and is already an integral part of the decision-making process of all companies. Tainted by this "black box" image that it may have for us, Data Science & its tools are bound to be democratized within companies, and not only in their Data departments!
This is why training courses to become a Data Scientist are multiplying. Depending on your needs and your professional project, the formats are very varied:
- Online courses
- Master's degrees in 1 or 2 years
- Bootcamps
Need a career advice to train in data? Make an appointment with our teams 👋
