Data Analytics
Analytics is the systematic computational analysis of data. With everything going digital, there is a lot of data generated and stored in databases. There is user contact data, shopping preferences, video and music preferences, spending patterns, and social media exchanges. Then there is machine data, largely from IoT solutions, such as images, video, industrial machine failure data, city maintenance data and so on.
Data analytics is used for the discovery and interpretation of meaningful patterns in data. These data patterns can then be used for decision-making. AI/ML algorithms rely on data and patterns to make decisions. The more data they are provided the better they learn and the better the decisions and autonomous outcomes.
Given the huge volumes of data today, it is not possible for humans to understand and discover patterns hence this is one of the early applications for use of AI/ML.
Data analytics has applications in almost every field:
Medicine: Analysis of data on the efficacy of medicines or the success of medical procedures help improve the quality of medicines and procedures and to detect and predict trends on how and where similar medical problems may recur and how they can be proactively prevented or treated.
Finance: Analysis of financial transaction data can help identify fraudulent activities and help financial institutions build controls to prevent them from happening in the future.
Sales and Marketing: Analysis of user shopping and other preferences can help organizations identify the right target audience and design and sell their products better.
Industrial Operations: Analysis of production patterns can help organizations plan their operations better. Analysis of machine performance data can help them plan for preventive maintenance thus reducing failure rates and downtime.
Transport and Logistics: Analysis of time taken for deliveries can help delivery teams to plan routes better to deliver faster and save fuel.
Computing System Operations: Analysis of hardware and software logs can help computer administrators identify potential issues in the system including security issues and take proactive measures to fix them.
High-quality data analytics can add a lot of value to any business operation and hence it is one of the key branches of AI/ML study.
The R environment
R is an integrated suite of software programs for data manipulation, calculation and graphical display. It includes a data handling and storage facility, operators for calculations on arrays, in particular matrices, a large, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
This is one of the languages you will have to learn if you would like to get into the field of data analytics.