Industrial Data Analysis EMT30304 PDF

EMT30304 INDUSTRIAL DATA ANALYSIS  Course Learning Outcomes Able to apply knowledge of exploratory CLO1 data analysis, probability concepts, and inferential statistics. Able to evaluate statistical data analysis CLO2 using suitable software. CLO3 Able to solve...

EMT30304 INDUSTRIAL DATA ANALYSIS  Course Learning Outcomes Able to apply knowledge of exploratory CLO1 data analysis, probability concepts, and inferential statistics. Able to evaluate statistical data analysis CLO2 using suitable software. CLO3 Able to solve statistical industrial problems.  CHAPTER 1 INTRODUCTION TO INDUSTRIAL DATA Ts. Dr. Salsabila binti Ahmad ANALYTICS, BIG DATA AND I.R 4.0 What are they? 01 VARIABLE VS VALUE Their difference 02 DATA COLLECTION Basic Principles and methods 03  First Industrial Revolution Mechanization Second Industrial Revolution Mass Production and electricity Third Industrial Revolution Digital Automation Industry 4.0 Cyber-physical system, IoT, AI, Big Data  System Augmented reality integration Cybersecurity Internet of Things BIG DATA Simulation Cloud Computing Autonomous robots Additive Manufacturing Data-Driven Manufacturing Environment  Data Analysis vs Data Analytics in Industry Data Analysis Data Analytics definition Process of inspecting, cleaning, Data analytics is a broader term that includes data analysis transforming, and modelling data to but also incorporates the tools and technologies used to discover useful information, draw perform this analysis, particularly large datasets (Big Data). conclusions, and support decision- It focuses on uncovering patterns, correlations, and making. actionable insights from data. purpose Improve decision-making by analyzing Descriptive Analytics: Uses historical data to understand past performance. what happened in the past. Optimize production processes and Diagnostic Analytics: Explores why something happened by supply chains by identifying finding the underlying causes. inefficiencies. Predictive Analytics: Uses machine learning and AI Predict trends, such as customer algorithms to predict future trends and outcomes (e.g., demands or equipment failures, based demand forecasting, predictive maintenance). on historical data. Prescriptive Analytics: Provides recommendations on what Enhance quality control by analyzing actions to take based on predictive analytics to optimize data related to defects, variations, and outcomes. errors in production. There are three main goals in analyzing data Efron and Hastie 2016 Prediction: To predict the response to future input variables. Estimation: To infer how response variables are associated with input variables. Explanation: To understand the relative contribution of input variables to response values Statistics is the fundamental component of data analysis Statistics is a science that helps us make decisions and draw conclusions in the presence of variability.  Variable vs Value A variable is an attribute or property that describes a person or a thing. Variables have values. Values vary from person to person and from object to object—hence the term variable. Variable Value Surname Smith Weight 60 kg Brand of car Ford Age 20 +Data Collection In data collection; a variable represents a characteristic or factor being measured, its value is the specific observation recorded for that variable, and a sample is the subset of data points gathered from the population to analyze these variables. Program Sekolah Sihat, KKM. To check BMI of students in Standard 6.  + Sample in data collection refers to a subset of individuals or items selected from a larger population A B⊂A B 250g 360g 450g 500g 230g 680g 910g What are the elements of B? And elements of A?  ❑Systematic Census process of collecting, recording all of the observations in the population.  Census vs Sampling Census is the process of collecting data from every member of a population. In a census, every member of a population is included Sampling is the process of collecting data from a subset of a population. While in sampling, a smaller group of individuals is selected to represent the population as a whole.  Retrospective 3 Methods in study Data Collection 3 basic methods Designed Observational experiment study  Retrospective study ❑An experimental design that looks back in time and assesses events that have already occurred.  Retrospective ❑For example if you are interested in how dengue diseases spread. study ▪ You interview a group of people who have been hospitalized with severe dengue fever, asking about the events surrounding their illness and their medical history to study the spread of the disease.  Disadvantages of Retrospective study ▪As retrospective study involved statistical analysis of historical data, sometimes it identifies interesting phenomena, but solid and reliable explanations of these phenomena are difficult to obtain. Disadvantages of Retrospective study ▪ A retrospective study may involve a significant amount of data, but those data may contain relatively little useful information about the problem. ▪ Furthermore, some of the relevant data may be missing, there may be transcription or recording errors resulting in outliers (or unusual values), or data on other important factors may not have been collected and archived (kept).  Observational Study ❑In an observational study, the observer observes the process or population, disturbing it as little as possible, and records the quantities of interest.  Observational Study Pro Con Because these studies are Generally, an observational. usually conducted for a study tends to solve some relatively short time period, problems accurately sometimes variables that are not routinely measured may also be included  Designed Experiment ❑Experiments are designed with basic principles such as randomization are needed to establish cause-and-effect relationships. ❑Here, we observe the responses of the output, and then make an inference or decision about which variables are responsible for the observed changes in output performance  Designed Experiment ❑Means, we make deliberate or purposeful changes in the controllable variables of the system or process to see the responses. Activities: Give one example of any term in this chapter you would like to elaborate on using different example