data science in big data world audioimageand video audioimageand video are data types that pose specific challenges to data scientist tasks that are trivial for humanssuch as recognizing objects in picturesturn out to be challenging for computers mlbam (major league baseball advanced mediaannounced in that they'll increase video capture to approximately tb per game for the purpose of livein-game analytics high-speed cameras at stadiums will capture ball and athlete movements to calculate in real timefor examplethe path taken by defender relative to two baselines recently company called deepmind succeeded at creating an algorithm that' capable of learning how to play video games this algorithm takes the video screen as input and learns to interpret everything via complex process of deep learning it' remarkable feat that prompted google to buy the company for their own artificial intelligence (aidevelopment plans the learning algorithm takes in data as it' produced by the computer gameit' streaming data streaming data while streaming data can take almost any of the previous formsit has an extra property the data flows into the system when an event happens instead of being loaded into data store in batch although this isn' really different type of datawe treat it here as such because you need to adapt your process to deal with this type of information examples are the "what' trendingon twitterlive sporting or music eventsand the stock market the data science process the data science process typically consists of six stepsas you can see in the mind map in figure we will introduce them briefly here and handle them in more detail in data science process setting the research goal setting the research goal retrieving data data science is mostly applied in the context of an organization when the business asks you to perform data science projectyou'll first prepare project charter this charter contains information such as what you're going to researchhow the company benefits from thatwhat data and resources you needa timetableand deliverables data preparation data exploration data modeling presentation and automation figure the data science process