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Data Analytics in Gaming Industry

I n the modern gaming industry, creating a successful mobile or social project is possible only by processing large  quantities of information. Many  instruments are used to design and support products, write marketing strategies, and monetize game analytics there can be several within a single  design, depending on the  thing.   In the era of the development of computer games, it's hard to imagine the number of specialists working on the final product — developers, designers, artists, screenwriters, directors, and other specialists. But besides the fact that the game needs to be  constructed and developed, it must be successfully sold and analyzed. But what to do with all this information, and how to make a good game? Let’s figure it out.  Since we've touched on such important actors in the game dev field, it'll be in the right place to remind us of what they do. After all, analytics isn't just looking at statistics and reading player reviews. Vi...

Difference between Data Scientist & Data Analyst

 T ransitioning from a Data Analyst to a Data Scientist Although their many similarities between the two career paths, there are also many differences as well. The higher pay that Data Scientists receive comes with more responsibility. This extra responsibility entails more studying, more knowledge, and more practicing your coding skills. Below are a few pointers on what I would recommend you to do if you wish to make that transition from a Data Analyst to a Data Scientist. Play the role of a Data Scientist. If you’ve made the decision to transition into a Data Scientist, you must have done a lot of extra reading to fully understand what it entails to become a Data Scientist. You will go from describing trends in your data to uncover new data using your existing data and build machine learning models to support your hypothesis. Data Scientists: Spend a lot of their time cleaning data using languages like Python or R. Build predictive models using machine learning algorithms such as...

Introduction to Data

D ata is information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer. In other words, Data is a set of variables which can be quantitative or qualitative. Data Types: Data can be either quantitative or qualitative. Understanding the difference between quantitative and qualitative data is very important, because they are treated and analyzed in different ways: for example, you cannot calculate statistics for qualitative data, or you cannot exploit Natural Language Processing techniques for quantitative data. Data is information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer. In other words, Data is a set of variables which can be quantitative or qualitative. Data Types: Data can be either quantitative or qualitative. Und...