With consumers spending more time than ever on games, the gaming companies became an important part of the global entertainment industry. These companies are the champions of interactive entertainment as they are outstanding at stimulating social, creative, and sharing behavior. The global gaming market will reach $115.8 billion in 2018 with mobile taking 45%, states Newzoo in its Global Games Market Report.
It’s all about smartphones and social media platforms now and this notion has downsized the market for video game consoles like PlayStation, Xbox or Nintendo. Not only multinationals like Electronic Arts (EA), Sony or Microsoft, but also the developers and designers are getting into the business of gaming as it is a very promising field.
With gaming setting out a foot on social media platforms, there are loads of data for the gaming companies to deal with. Gaming company, Zynga launched games like Zynga Poker, Farmville, Chess with Friends, Speed Guess Something, and Words with Friends on social media. This has brought a lot of user connections and generates large data sets. This is where the gaming industry needs data science to make use of this data gathered from players all over social networks. Data analysis provides gamers with a compulsive, novel distraction to stay ahead of the competition! Another most interesting uses for data science is within the aspects and process around game development.
Games and Data Collection
Games are used as a method for collection of data. The main idea is to track the data entered in a graphical form at some time and recording the user input for that image frame. This data then is used to draw an end result, for example – the final score.
Deep Learning Uses
Right from creating smart artificial intelligence systems to finding a transferable algorithms, Data Scientists uses the data sources from games for deep learning. This type of learning is interesting from a data scientist perspective as it helps to find commonalities and generalizable methodologies to use not only for the present game project but also for future games and systems.
Data Oriented Game Design
Data driven game design is not a new concept but has become increasingly ubiquitous. Purchase based games generates tough data science tasks which are not like normal data analysis and needs to be studied with statistical perspective of what is happening to users at specific points along the games.
Gaming Industry – An Opportunity for Data Science Lovers
Gaming industry is utilizing data analytics in its full scope. Technology, financials, gameplay, marketing, strategic, just name an analytics domain and you’ll find it working with revenue in the gaming industry.
Game Data Scientist
A data scientist develops and investigate, design and implement hypotheses, and structure experiments to test various hypothesis. They are also responsible to build mathematical and automated models to analyze and identify game optimization points. If you wanna join the gaming industry as a data scientist and have a passion for mobile gaming, data mining, and mathematical modelling, then it’s an added advantage for you.
Deep Learning Data Scientist
Being a part of the Advanced Analytics Team, a deep learning data scientist needs to mine massive amounts of data and perform large-scale data analysis, and develop descriptive, predictive, and prescriptive models using deep learning/machine learning algorithms. If you have a passion for problem-solving, developing creative solutions, and continuous learning, then this is the job role you want to play.
Game Data Analytics
A game data analytics is responsible to examine and visualize service performance and user conversion funnel data to identify opportunities to improve user engagement, and user retention. They use data analysis techniques to identify meaningful relationships, patterns, trends, and user behavior models from complex data sets to guide service roadmaps and creates automated anomaly detection systems and constantly tracks its performance.
If you are passionate about gaming and aspire to grow as a data scientist then master the processes and practice of data science, including machine learning and natural language processing with SpringPeople’s HDP Analyst Data Science course.