What's behind all the hype about data science? We asked Lead Teacher João to explain in a few simple words the opportunities of data science and advise people pursuing a career in this field.
With an engineering background, João is an alumni of batch #28, the very first bootcamp in Lisbon back in 2016. Since then, he has been teaching with Le Wagon and progressively dived into the world ofdata science. He is now our lead teacher for the data science bootcamp!
But let's get to the point: machine learning, convolutional neural networks, NLP...ever heard about those weird expressions? We asked João a few questions to understand a bit more about the boom of data science and some advice to people who would like to pursue a career in this field.
Why did you switch from coding to data science?
I cannot think of a more fascinating and impactful field nowadays. Artificial Intelligence has become increasingly intrinsic to our lives, from online research with our smartphones to diagnosing diseases, from self-driving cars to fitting parameters in climate change models. At the same time, computers are incredibly efficient at storing, organising, fetching and processing huge volumes of data. We moved from holding thousands of records in old paper cabinets, to storing large volumes of data in computers for quick and convenient access. And looks like things will keep on growing. I don't know if you ever heard about Moore's law: since the 1970s, processing capacity has doubled every two years and the same thing for the volume of stored data. Thetotal amount of data created, captured, copied, and consumed globally was forecast to reach 64.2 zettabytes in 2020 (Statista) and, over the next five years, global data creation is projected to grow to more than 180 zettabytes - FYI, 1 zettabyte is equivalent to 1.073.741.824 terabytes! Sometimes it's quite hard to get our heads around these huge numbers.
Everybody is talking about Machine Learning, but what is it exactly?
Yeah, machine learning is quite a buzzword today. Basically, machine learning refers to a group of techniques used by data scientists that allow computers to learn from data, and then make decisions or future predictions. Cool, right? The fundamental idea is that data scientists can create systems that keep evolving and use data to improve their performance at specific tasks.
Spoiler alert: although data science includes machine learning techniques, it is a vast field with many other different tools as well.
Let's say you are both a Software Developer and Data Scientist, can we? What’s the main difference between these two roles?
They have different but complementary functions. Software developers usually build web applications, or design softwares to be used by people and organisations. Data scientists, on the other hand, focus on building predictive models and developing machine learning capabilities based on the data captured by that software.
The role of a data scientist can also involve finding methods for solving business problems, using data created by the organisation's systems. Let me give you an example, an e-commerce platform. A software engineer might design and build a system for that business, then the data scientist takes its data to determine, for example:
The correlation between customer demographics and sales revenues;
The correlation between seller characteristics and customer ratings;
The effect of location on purchasing propensity by day of week or time of day;
The amount of orders a seller shall expect to receive on a given day;
The expected order delivery time to the customer.
In a nutshell, why do you think people should learn data science?
It is a huge opportunity to grab as soon as possible. With all this data, the necessity of extracting useful insights or making future predictions will grow more and more. A successful business needs to use data for competitive advantage - In the end, that's the main reason for such a high demand in data science roles. Still, this field is relatively unexplored by most companies, even in 2021.
With most businesses turning data-driven decisions their priority, there are not enough data scientists to fill in this demand. The exponential growth of this field was quite hard to predict and traditional education was not ready to meet the needs of new learners. People from other areas such as business or psychology transitioned on a self-taught basis through online courses. Today, something like our Data Science bootcamp is a great way to start a smooth and quick transition into this area!
Alright. One final question: any advice for those who are considering a career with data?
The time is now, just embrace this opportunity. The need to analyse data, extract useful insights and try to forecast what will happen next became crucial for all organisations. The demand for data science roles exists and will keep on growing around a huge variety of sectors!
Also, keep pushing and never stop learning! The field is relatively young and keeps growing, with new conventions and tools coming up every now and then. Just like in software engineering, it is a career of constant learning and fun.
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