Data Science is a field of study that focuses on using scientific methods and algorithms to extract knowledge from data. The Data Scientist role may differ depending on the project. Some associate this position with application analytics, others with vaguely defined AI, and the truth lies somewhere in between. Ultimately, the Data Scientist's primary goal is to improve the quality of application development (like mHealth App Development) and bring value to a product.
"Dig into every industry, and you'll find AI changing the nature of work."
Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory
The role of a Data Scientist
Data Scientist is generally required to have knowledge in data analysis, data transformations, and machine learning. However, different positions are related to this role, such as:
- Data Analyst,
- Data Engineer,
- Machine Learning Engineer,
- MLOps,
- or DataOps.
Data Scientists may often be perceived as full-stack developers of a machine learning world. Therefore, many companies prefer hiring Data Scientists with particular skills that involve mentioned roles to fit project requirements. In small teams, Data Scientists are responsible for designing architecture and building data processing pipelines, preparing application analytics, developing machine learning solutions, deploying these to the production environment and monitoring results.
Transforming data into value
The primary purpose of a Data Scientist's work is to solve problems that include reducing costs, increasing revenue and improving user experience. It can be either achieved by maintaining and investigating application analytics or introducing AI systems in a project.
Application analytics usually include components addressing the following questions:
Users demographic
- Where do the users come from?
- What age are they?
- What devices and systems are they using?
Users activity
- How many active users have the application?
- What time does the application suffer from increased traffic?
- How does the cohort analysis look like?
- What is the users' engagement time?
Users paths
- Which application features are frequently used?
- Where do bottlenecks occur in applications flows?
Additional application KPIs
- What is the overall user engagement?
- What is application revenue?
A/B tests results
- What are the results of A/B testing?
- How can the results change considering different user segments?
Crashes and Errors
- How many users are affected?
- Is there any pattern in the segment of affected users?
Analytics can give plenty of information to the development team and the client. Therefore, application development can be accelerated with tasks prioritization, features validation, and detection of hidden issues.
Although analytics is an important part of application development, Data Scientists are also responsible for delivering machine learning solutions. Machine learning is a branch of science that focuses on automatic insights extraction in order to build a knowledge model that can perform a certain task. On the other hand, AI (Artificial Intelligence) is a much broader term often used by marketers. As a result, that expression has become a buzzword and is loosely used as a machine learning term equivalent in the business world.
There is a wide variety of applications that can utilize machine learning. Some common AI systems with examples are presented below:
Recommender systems - profiling a user to propose the best items that fit their interests;
Customer segmentation - assigning users to different segments (e.g., based on their behavior) to maximize profit from marketing campaigns;
Image recognition - detect the particular object on images/videos (may be used for censoring inappropriate content);
Anomaly and fraud detection - automated detection of anomalies (detecting changes in users behavior, transaction flows, or detecting cyberattacks attempts by analyzing network traffic);
Text mining - e.g., sentiment analysis that provides information concerning positive or negative attitudes toward a product based on the content provided by the user (e.g., user opinion);
Churn prediction - detecting and preventing users from leaving the application or canceling the service subscription;
Other systems:
- Antispam filters;
- Forecasting methods (e.g., predicting future sales);
- Chatbots;
- and various processes automation.
Data Scientist during the application development life cycle
There are two approaches to hiring a Data Scientist. Preparing application MVP may be difficult for a client financially. During the initial development, there is an obvious need for developers rather than Data Scientists. In this scenario, Data Scientist is usually hired when the application is publicly available. Gathered data can be utilized for further application development and the application might require some AI-centered features.
On the other hand, Data Scientist knowledge and experience may be beneficial from the start of the development cycle. Although introducing new machine learning solutions may not be crucial for a new application, to apply these solutions proper data collection is required. That means that Data Scientist should be included in the work related to designing databases and data flows. This way, it will be more effortless to develop machine learning solutions in the future.
Conclusions
Data Science is a broad field that has a lot to offer and is advancing rapidly. Although it is required for a Data Scientist to have a broad set of skills in various data-centric approaches, the experience should match the requirements for a particular project. In conclusion, Data Scientists can significantly benefit application development and bring the system to the world of artificial intelligence, machine learning, and data-driven decisions.
