In the past few years, the role of Data Scientists (DS) has grown significantly. This role can have many different interpretations. And one of the consequences of its growing importance has been a change in the perception of another. It’s related and no less important role: Data Analysts (DA). An important question is data scientist vs analyst vs engineer salary.
Purpose of data science
There is still some misunderstanding between the meaning of Data Scientist and Data Analyst. But more often than not DS is considered a better and better version of DA. One of the unpleasant consequences of this misunderstanding is that there is some pressure on data analysts. It’s used in order to rename them data scientists. As if the first term had gone out of fashion. The roles of analysts and data scientists often overlap in terms of the skillset required. But there are fundamental differences in their benefits to organizations. Both roles are critical to a data-centric company. And there are several critical aspects to understand.
Data science results can be applied to almost all types of industries
Why data science is important? There are a few basic directions, where it’s basically used nowadays:
- detection of anomalies, for example, abnormal behavior of the client, fraud;
- personalized marketing – e-mailing, retargeting, recommendation systems;
- quantitative forecasts – indications of efficiency, quality of advertising campaigns and other measures;
- scoring systems – processing of large amounts of data, assistance in making decisions, for example, about providing;
- basic interaction with the client – standard answers in chats, voice helpers, sors typing letters in folders.
Skills for data scientist
There are now two main languages in data science: Python and R. The R language is used for complex financial analysis and scientific research. So its deep study can be postponed until later. The importance of data science can’t be underevaluated.
Python is more popular. It is easier to learn how to code and many packages for data visualization, machine learning, natural language processing and complex data analysis have been written for it.
Future of data science
Data Science extends far beyond retail, insurance, and fintech. We use Data Science applications every day when a social network, music streaming service or YouTube recommends content to us.
Billions of users around the world use smartphones, watches and other electronic devices. They generate colossal amounts of data. Processing data from wearable trackers will allow a large number of people to develop healthy habits and prevent critical health problems. Medical data from wearable devices can help diagnose and accelerate drug development. In https://blog.dataart.com/taxonomy-of-data-professionals-find-the-right-one-for-your-business there is more information on the topic.
Systems that will make it possible to put routine operations on the stream and speed up development will be greatly developed. Automating tasks such as selecting and evaluating algorithms can reduce the time it takes to work with data by up to 10 times. Improving the quality of algorithms and simplifying software tools will lower the barrier to entry into the profession. Such simple machine learning algorithms, decision trees, are now easier to deploy. And understanding frameworks like PyTorch and TensorFlow doesn’t require a PhD in mathematics at all. The benefits of data science are numerous.
Machine learning forms the backbone of data science and requires you to be knowledgeable in the field. You need to have a clear understanding of the area in which you work in order to clearly understand the business objectives. Your task does not end here. You should be able to implement various algorithms that require good programming skills. Finally, after you’ve made certain key decisions, it’s important for you to communicate them to your stakeholders. Thus, good communication will definitely add points to your skills.