Data Scientists
Do you remember what Facebook looked like ten years ago? Is it still the same, or have new features and tools been added to it? You've surely noticed that there are always updates to different social media apps like Facebook, TikTok, and Twitter, or even to global e-commerce sites like Amazon and Alibaba. For example, Reels weren't among Facebook's features a few years ago. You didn't used to be able to lock your profile to non-friends. Some e-commerce sites didn't used to allow returning a product after purchase, and they didn't have a way to track a product after ordering it from their platform. A lot has changed in the features of most social platforms and e-commerce sites over the years — but why? The global companies and institutions that own these platforms constantly conduct in-depth research and analysis to understand user and customer behavior, to figure out exactly what they need so they keep coming back to use their apps and platforms. You might not imagine that these platforms employ specific experts called "data scientists" to study and analyze the behavior of millions, even billions, of users every day, and based on these in-depth analyses and studies, they make decisions to add new features or remove or modify old ones. You should understand that these companies earn billions of dollars if people keep using their platforms, and they can lose those billions if users turn away from them, so it's no surprise they hire these specialists called "data scientists" to help them improve the quality of their services. Data scientists are people with strong skills and expertise in mathematics and statistics, capable of developing methods and programs to collect and analyze huge amounts of data, which helps companies make critical decisions. That's why almost every industry needs them, and various institutions — whether commercial, industrial, medical, or otherwise — are always looking for them to guide them on what they should do to develop and improve the quality of their services and products.
Meet the Writer: Waleed Abo Omiraa
What You'll Actually Do
The core tasks and responsibilities that fill a typical day.
- Analyze, manipulate, or process large sets of data using statistical software.
- Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
- Design surveys, opinion polls, or other instruments to collect data.
- Identify business problems or management objectives that can be addressed through data analysis.
- Identify relationships and trends or any factors that could affect the results of research.
- Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
- Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
- Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
- Recommend data-driven solutions to key stakeholders.
- Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
- Write new functions or applications in programming languages to conduct analyses.