![]() ![]() Modeling, on the other hand, is an iterative process. But it’s necessary as real-world data is messy and algorithms can’t deal with messy and unstructured data sets. As you can imagine, this is a very inefficient use of a data scientist’s time. ![]() When it comes to how time is spent in each of these parts, we have this rule of thumb: 50-60 percent is spent on just processing the data, and the rest is spent on modeling and deployment. Usually, every data science project consists of three parts: data processing, modeling, and deployment. To understand what I mean, let me walk you through the data science workflow. This process can be quite time consuming and needlessly complex. #DATA SCIENCE AND MACHINE LEARNING ON THE JOB CODE#Although they enable us to set up and maintain data infrastructures more easily, somebody still needs to write lines of code to clean the data and experiment with machine learning models. Take for example various cloud-based platforms that make it much easier to develop and maintain big-data infrastructures, from my own team to others in the market like Amazon Web Services (AWS), Google, Microsoft, and Anaconda.Īutomated big data platforms are only part of the story. What we have seen in the past few years is the appearance of platforms that automate this process. Possible, but you needed people with highly specialized skills, and it took a lot of money and a time. The issue is that for many years building such an infrastructure was like building a car just from parts. Think of it as different pieces of technology that can run all the tools a data scientist needs. To implement any type of big-data project, a company must build a data infrastructure first. However, extracting information from big data sets can be expensive. In other words, if you have some data and you want to make some decision or predictions based on it, you use data science. In its essence, it is a field that uses tools taken from computer science, statistics, and machine learning to extract insights from data. Although a complex topic, I’ll be focusing on two important developments that are changing the data science landscape:īefore I start talking about data science platforms, let me give you a short introduction into what data science is. Just as with driving, gradual automation is happening in data science as well. If you’re an entry-level accountant, the timeframe is much smaller. If you’re a doctor, it’s going to take a while until major parts of your job are automated. ![]() The only major variable is the timeframe at which it happens. And this process can be observed in many jobs. Just last week Waymo, Alphabet’s self-driving subsidiary, officially received the very first California permit to test their vehicles in the state without a human behind the wheel.Īll these developments illustrate an important lesson: automation happens in steps. In the next couple of years, we will most certainly have fully autonomous cars driving on the roads. Now, we are able to tackle more complex problems like lane merging and emergency braking. For route planning, we developed the GPS. For a better driving experience, we built cruise control. For many years now, we have been taking small parts of the driving process and automating them. What we can tell is that these narratives share similar inception: more and more parts of our jobs and lives are being automated. Of course, it’s impossible to tell which of the two narratives will become a reality. And the number of jobs and industries they’ll destroy will far exceed the number of jobs they create. The robots are becoming more and more intelligent and capable. The other narrative is that this time is different. Of course, some people will lose their jobs, but as history shows, new jobs will be created. The first one is that it will definitely lead to a better future, as it always had since the industrial revolution. Almost any article you read about how automation will affect our future can be classified into one of two narratives. ![]()
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