Data science has seen incredible growth as a field in the past decade or so. But what does a data scientist actually do? If you’ve always wanted to find out, or if you are confused about all the different data-related tech jobs out there, read on to have your questions answered!
Scientists, Analysts, and Engineers
The first step in understanding what a data scientist does is probably understanding what a data scientist doesn’t do. In order for a company to obtain data, a data ecosystem needs to be built and maintained. This is the job of data engineers, and it includes responsibility for building reliable data pipelines which turn raw data into usable information. A data analyst will then analyze the data which the data engineer has made available to them, process it, interpret it and draw conclusions from it which answer specific business questions. Data scientists do a similar job to data analysts, but they use more complex systems to make predictions, such as statistical analysis, machine learning, and mathematical modeling. They often build their own machine learning models and use their findings to improve business metrics. Both data analysts and data scientists (and sometimes even data engineers) are required to present their findings to their clients or employers, so data visualization is an important part of these jobs.
Data cleaning is the process of ‘cleaning up’ a dataset so it can be analyzed. This involves fixing spelling and syntax errors, correcting empty fields and missing code, and identifying duplicated data. The goal of the data cleaning process is to create a standardized dataset so that data analysis software can easily access the right data for each query. Depending on the size of the company, this job might be done by a data engineer, a data analyst, or a data scientist. This part of a data scientist’s job requires extreme attention to detail, whereas the business strategy aspect of the job is all about big picture thinking, which is why data scientists need to have a wide set of soft skills in addition to many specialized technical skills. This is also why data scientists get paid so much!
Applications of Data Science in Business
Data scientists are employed by many different kinds of companies to help them solve problems, improve their business systems and make savvy business decisions based on data gathered from customers, clients, and more. For instance, data scientists are employed by the manufacturing and logistics industries to predict demand and therefore optimize their supply chain and to improve the management of warehouses. Data science is also useful in retail and advertising, as analyzing purchase and sales history can help retailers identify their best advertising targets. Finally, data science is used to make predictions such as weather forecasts—which are invaluable in agriculture—and disease progression projections. In summary, there is a possible application of data science in almost every field!
In recent years, advances in software development and hardware processing power have made it possible for companies to collect a huge amount of data from those who interact with them through a digital device of some kind. Google searches, online shopping, and app usage are some of the ways in which companies can collect data about individuals. However, once collected, that data needs to be processed and analyzed in order to be of use to the company. Data mining is the process of digging through data to uncover patterns and other information and use it to make predictions. Data mining is such an important part of working with data that Baylor University’s online masters in data science devotes a whole module to it, and with good reason: career experts at Zippia predict that demand for data miners will increase by 20% between 2018 and 2028. Data mining is one key way in which data scientists can help businesses improve their systems and make more informed business decisions.
Once data has been collected, processed, and analyzed, it needs to be presented to business leaders in a form that they can easily understand and that will allow them to use the results of this process in their business decisions. This process is called data visualization, and it often consists of various kinds of charts, although many data scientists will also find other creative ways of displaying their data to suit different purposes and audiences. For example, word clouds are a simple and effective way to display the most used words within a dataset, which is why they are often used in advertising to display positive responses to marketing polls.
Enabling Better Decision-Making
Data scientists are useful because they enable leaders in businesses and other services to make decisions that are informed by evidence. Therefore, companies need to set clear business goals which they want the data scientist to help them achieve: for instance, improving a certain workflow or understanding their customers’ needs in a specific area. The data scientist should collect data from sources that are relevant to the business goal and then draw conclusions from that data that directly relate to that goal. Importantly, after the results and recommendations drawn by the data scientist have been presented to the business leaders, they need to implement them in their business. This may sound obvious, but it bears pointing out since 87% of data science projects never make it to the production stage. While there will certainly be many reasons for this, including the inevitable fact that some projects just won’t reveal very useful information, one of the issues could well be a failure of integration between the different departments in a company, resulting perhaps in data scientists’ reports sitting unread for months in the inbox of an over-stretched operations manager. The irony is that a data scientist may well be able to suggest a solution to this problem after running a data science project on it!
In conclusion, being a data scientist is an exciting and varied career that pays extremely well and is applicable in all sorts of fields.