Title: 1.1 What is Data Analytics?
Course: Manufacturing Analytics (ATE Central)

INTRODUCTION

Data analytics is the process of using data to draw insights, identify patterns, and make informed decisions. It involves collecting and analyzing large amounts of data to identify trends, patterns, and anomalies that can provide valuable insights into a particular subject. The primary goal of data analytics is to make data-driven decisions that can lead to better business outcomes. By analyzing data, businesses can gain valuable insights into their operations, customer behavior, market trends, and more. This information can be used to optimize business processes, improve customer experiences, and increase profitability.

Data analytics involves a variety of techniques and tools, including statistical analysis, data visualization, and machine learning. Statistical analysis is used to identify patterns and relationships between different variables in a dataset. Data visualization is used to communicate insights visually, allowing decision-makers to easily understand and interpret complex data. Machine learning is a form of artificial intelligence that is used to build predictive models that can forecast future outcomes based on historical data.

Data analytics can be used in a variety of industries, including healthcare, finance, retail, and manufacturing. In healthcare, data analytics can be used to analyze electronic health records to identify patient trends, optimize treatments, and improve patient outcomes. In finance, data analytics can be used to identify fraudulent activities, forecast market trends, and optimize investment portfolios. In retail, data analytics can be used to analyze customer behavior, optimize supply chain operations, and improve marketing campaigns. In manufacturing, data analytics can be used to optimize production processes, improve quality control, and reduce waste.

Analyzing data is driven by a purpose. Stakeholders typically have a question that needs answering or a problem that needs solving. A stakeholder refers to an individual, group, or organization that has an interest in, is affected by, or can influence the outcome of a data analysis project. This may include clients, customers, management, employees, investors, partners, regulators, or any other relevant party who has a stake in the success, results, or insights generated by the data analysis process. Stakeholders usually provide input, feedback, and support in decision-making based on the data insights and are typically involved in various stages of the project, from planning and data collection to interpretation and implementation of findings.

Stakeholder |  | An individual or organization that has a stake in the data analytics problem. |

ROADMAP

There is a roadmap to articulate the process used to find answers in data. This roadmap is The Data Cycle. Data analytics is typically an iterative (cyclical) process that follows specific steps, as seen in the image below. The standard data cycle includes six steps: question, collect, clean, analyze, results, and present. This process will be defined and reviewed throughout this program, and along the way there will be demonstrations how each of these steps influence how data is handled and how answers are sought.