What is Hardcore Data Science In Practice

Data science has engaged with all the industries. Determining human intelligence and creating artificial intelligence are the initial efforts included by the data scientist. Artificial intelligence has the capability to anticipate customer intelligence.

Companies deal with heavy data sets and they use the provided data to identify the customer requirements and necessities. They work on the customer recommendations to improve the customer satisfaction. Recommendation process is considered a back-end process by many companies. 

Computing Recommendations

There are various ways to compute the recommendation data like; wish-list actions, product list, and purchases. These are the best areas to collect and compute the user data. Initially accumulated data will be filtered and then it is calculated to identify the similar data sets.

The other approach that focuses on the high marginal items and related data. A user might get recommended with the same brand, color and model. These approaches can be extensible and integrated to improve the efficiency.

Mathematical Approaches in Industries

There are few questions that will reflect the worth of data science and the data scientist. Questions like what type of team structures are fit for this approach and preference of data scientist and the engineer.

Understanding Data Science and Production

Considering the workflow of data science, it starts with determining the problem and accumulating the data from the production logs. This execution has relied on the readability of the organization. 

Gathering data to solve the problem is the major huddle that will need to be crossed. Identify the source to get the data, initiate the feature extraction process and resultant features should be capable to resolve the task. These resultant features are included in the learning algorithms and evaluated to test for the future data.

Why Start Small

Data science projects are the solution finders and this process cannot be accomplished in a single step. The data available is not totally sure to solve the problem always and that can be termed as “risk for achievement”.

Pipelining is a technique that will allow the process to improve and enhance many times. This technique will allow you to execute different methods and processes but also enable you to add more data sources. If the method performance is worthy then execute with the real data that will reflect the production.

Differ Production System and Data Science 

Production systems and the data science systems work for the functionality but they differ in the procedure and the result after execution. Production system deals with the real-time system whereas, data science system needs to maintain and update the models and the data must be processed. The data should be executed within a specific period with the upgraded models.

Data Science and Developers 

Data scientist and developer are the two different directions that can be differentiated with the performance. A data scientist should be explorative. The process to progress the project is based on the applying methods and getting out with new ideas and insights.

Developers are basically designers, they work on the coding process and their main objective is to create a program with required functionality. They spend less time on research and exploration that is related to the building prototype and performance benchmarks.

Constantly Adapt and Improve 

Consistent change in the system, applying new methods, quick iterations and applying the test cases on the A/B test is most essential. Separating the data scientist and the developers only with the functionality. Change in the system will be achieved by integrating data scientists and the developers together.  

Leave a Reply