Data science is a scientific discipline that searches for truth and uses data to generate knowledge and ideas. Data science is developing rapidly and is already of great importance for every industry and field of science. Nevertheless, it is still at an early stage of development.
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What does data science mean?
It has become much easier to launch a tech company, as well as design and develop a good product, thanks to modern communication tools, the development of cloud storage, and lower computing costs. As a result, the time it takes for the product to reach 100 million monthly active users has been significantly reduced and continues to decrease.
For example, iTunes took about 100 months to reach 100 million monthly active users in 2003. It only took Pokemon Go a few days to do the same.
The rise in product development and sales of internet-enabled devices, as well as the increase in time spent online, has caused a dramatic spike in user experience data. As a result, there has been tremendous interest in extracting this data and generating key insights to develop better products. Now the competitiveness of a company depends on how well it applies analytics.
Thus, data scientists are in high demand, and one team of good people can create or discontinue a product. Foe example data science agency – DataScience UA – makes artificial intelligence evident by helping businesses to discover what they can do with AI.
Development teams use the data to produce four specific results
– Business Assessment
A key outcome of a product analysis is an assessment of the condition of the product or business. The success of a product is measured by a goal and a metric, so you need to track the progress of the metric to make sure the product is moving towards the goal. Analysts identify outliers, understand why the metric changes, and design dashboards, reports, visualizations, and more.
– Selling the right products and features
Another role for analytics is to create the right products and features. Most companies run several experiments and sell a product after evaluating the results of those experiments. Typically, data scientists are involved in developing experiments, defining hypotheses for statements, and guiding the development team to continually optimize the product.
– Predicting results and efficiency of production systems
Another challenge for data scientists is developing prototypes or models and improving the efficiency of production systems using AI / ML. These specialists train the model for a specific phenomenon in order to predict future expectations and trends.
As a result, two types of data scientists have emerged in the industry – product analysts and algorithm developers.
Evolution of data science
Imagine that there is a machine that knows absolutely everything about you. She does shopping, knows what food you like, and even cooks for you. Knows your options and can make decisions for you. Knows what’s best for you and plans your life. This world awaits us in the distant future, and to achieve it requires the development of artificial intelligence.
In an ideal world with perfect information and a complete understanding of all the system drivers and how they interact, the two approaches could merge. To build an ideal model, you need to fully understand the phenomenon under study, since only the ideal model (and the rich set of functions associated with it) can describe the relationship between the data and the phenomenon.
To achieve this level of excellence, as well as progress in the interim, data-informed decision making needs to be developed.