Data Science & ML

Data science includes a wide range of services based on a very advanced knowledge domain. Data science applies truly scientific methods to your data to discover the important trends and dependencies. Data science can work with very raw data that needs a lot of preparation and cleaning. 

When the data is ready, data science also uses math, statistics, and the latest computer technologies to create complex models. Data science solves practical problems using a very high level of procedural abstraction

Although data science can help with providing general business insights, its main application domains are more technically oriented. Data scientists may assist engineers with image recognition, help financial institutions unveil fraud and anomalies, build customer ranking, make complex forecastings, and reveal not-so-obvious trends and relationships between them.

When your business relies heavily on big data, then you need a data scientist, artificial intelligence, or machine learning specialist.

Data preparation is very important for every data science project. Only clean data can provide reliable insights. That’s why, most of the time, a data scientist will start work by making the data tidy.

Once there are no missing data or outliers—two typical data-readiness problems—the main phase of the project can begin. This can include models, neural networks, deep learning, or decision trees. At the final stage, the data scientist works on a report or other method of presenting the results, such as a data visualization.

Pricing can vary based on complexity, data quality, risk, and deliverables. Advanced modeling, unclear requirements, tight timelines, and production-ready expectations all increase effort and drive the cost upwards.

To learn more about the costs of a data scientist expert, read our article that dives deeper into the topic.

Apart from the data preparation methods that data scientists need mostly for themselves, data science also includes methods that come from applied math, statistics, and computer science.

In many cases, data scientists create an algorithm—a sequence of programmed actions—and train it using a known dataset to enable it to interpret any unknown dataset. In other words, a data scientist trains an algorithm to independently handle unknown situations based on the experiences obtained through training. Such datasets may contain images, numbers, and text.

Data scientists usually need advanced computer technologies to perform their calculations and store all the data they use, including specialist programming languages that allow them to build algorithms, such as R and Python. However, a few famous computer giants offer no-code or low-code data science tools, such as IBM Watson, Google ML Kit, and Azure ML Studio. 

When you handle huge amounts of data, you need to think about the tools that process it and the tools that can store it and provide quick access. These are often cloud-based technologies.