Here's how you can advance your career in gaming with data analytics and machine learning.

I think building a solid base in stats and machine learning really helps. Getting hands-on with projects or internships in gaming can teach you a lot, too. For anyone looking to switch fields, using platforms like Kaggle for gaming data challenges and joining online communities can be super useful. Plus, participating in gaming competitions can give you great insights into your target audience.

Machine learning and data analytics are now essential in game development. With the video game industry being highly competitive and unforgiving, developers must leverage these tools to enhance player experiences through better story telling, more human like NPCs , adaptive gameplay... Start by building a solid foundation in Python and data analytics through platforms like Coursera and Deeplearning.ai, where you can also earn certifications. Then, cultivate a "data-driven mindset" to uncover hidden metrics and relationships in your data. This mindset, developed through hands-on work like projects and hackathons, can greatly improve the accuracy and impact of your insights.

Speak fluent data and ML, there are tons of online courses on linkedin and coursera, use data to uncover player secrets and make the game insanely fun. ? Craft algorithms that know what players want before they do, train to be a future teller :) Build cool dashboards to visualize your insights and impress everyone on your team.. everyone loves dashboards (especially me)

Before applying data analytics and machine learning in gaming, grasp the fundamentals. Understand how to collect, process, and analyze data to make informed decisions. For instance, analyzing player behavior data can reveal patterns that enhance game design and player engagement. Familiarize yourself with key machine learning concepts like supervised and unsupervised learning. These can be applied to create personalized gaming experiences or detect cheating. Investing in online courses from platforms like Coursera and LinkedIn Learning builds a solid foundation. Mastering these basics enhances your technical skills and positions you to leverage data and machine learning for innovation in the gaming industry.

Having the basic foundation will be crucial here, you'll have to question yourself what metrics you need, why do you need it and where do you obtain it. Before going deep into visual analytics make sure you adhere to the basic principles of data, confidential for you and the people involved to know, integrity must not be compromised that may affect the credibility and accuracy of the data and lastly availability of the data can be obtained from. Being doubtful about the metrics is normal, seek help and clarify any of the basics with the people who knows more and improve from there to hone the foundational knowledge.

Analyzing key metrics from the data analytics results gives a broader understanding of the performance of the players thus informing various actions that can be taken. Applying the right data analytics tools also enable better understanding of the players behavior according to the training data set and these insights are used to make informed decisions that can drive development of the game and make it more user-friendly to improve the users experience.

Another thing data should be used for is to monitor logging for both bad actors doing nefarious actions like game play sabotage and good actors who get penalized when their servers reboot for an update showing them AFK during a game ( which also comes with a hefty penalty of some sort).

Application of Machine learning is increasingly growing in the field of gaming. ML provides the ability to assign players different skills and train them to automatically apply the skills when called. Learning ML is an advantage after a broad understanding of data analytics which then introduces you to Large Language Models that enhance the capability and development of games such as Virtual Realities.

One time at work, I implemented a machine learning algorithm to analyze player behavior and adjust the game's difficulty level in real-time. This kept players engaged by providing a personalized gaming experience tailored to their skill level. - In my experience, using machine learning to automate repetitive tasks has been invaluable. For example, by applying ML algorithms to sort and categorize bug reports, I was able to focus more on developing new game features and improving overall gameplay. - One thing I've found is leveraging machine learning for in-game economy management. By predicting player trends and automating transactions, ML allowed me to create a more balanced and game environment, enhancing the player experience.

ML or specifically, reinforcement learning, can also group your friend suggestions based on your gaming history, play style, propensity to join competitive matches vs quick play vs story mode against NPCs et al. It can also recommend other games you may like based on your game type purchases ; if you only play 1P Shooters, than suggesting Sims would be a miss eg. but Call of Duty might be what the Dr. Al Gorithm ordered!

By understanding game play style and tactics, game makers and platform hosts get an inside look into user engagement trends. This then enables personalized in game suggestions for loot you may like to buy because you engage as a pay to play gamer vs someone who likes to grind and earn their loot. Engagement through Reinforcement Learning can also pull like mind gamers together not just based on ELO / skill rating but how a person likes to engage with others. If you like to be on comms with your mic unmuted during a game and you enjoy side quests which require coordination more than skill, then quest + group with player suggestions can help drive a ton of sticky engagement moments.