Operations Management Course Policy

Operations Management House Rules

• Be in complete uniform always, with a 15-minute leeway for lateness.
• Gadgets are not allowed during class.
• No special exams, quizzes, or projects.
• 100% attention is required during class.
• Cleaning As You Go (CLAYGO).

Grading System

• Attendance: 10%
• Assignments: 10%
• Class Participation: 20%
• Quizzes: 30%
• Major Exam: 30%
• Finals: 50% of the final grade.

Business Analytics and Business Intelligence

• Business analytics involves statistical methods and computing technologies to process, mine, and visualize data for better business decision-making.
• Data management is the practice of ingesting, processing, securing, and storing an organization's data for strategic decision-making.
• Data mining, or knowledge discovery in data (KDD), uncovers patterns and valuable information from large data sets.
• A data warehouse aggregates data from different sources for analysis, mining, artificial intelligence, and machine learning.

Data Visualization

• Data visualization represents data using graphics, charts, plots, infographics, and animations to communicate complex relationships and insights.

Predictive Analysis

• Predictive analysis uses historical data and current market conditions to forecast revenue.

Machine Learning Algorithm

• A machine learning algorithm is a set of rules or processes used by an AI system to discover new insights and patterns or predict output values from input variables.

Business Analytics Cycle

• The analytics lifecycle includes data preparation and management, analysis, and reporting.
• Statistical analysis enables organizations to extract actionable insights from data.

Data Scientists

• Data scientists manage algorithms and models, solve problems, and know programming languages like Python and SQL.

Data Engineers

• Data engineers create and maintain information systems, collect data, and ensure data accessibility.

Data Analysts

• Data analysts communicate insights to stakeholders and may collect and analyze data sets, build data visualizations, and focus on storytelling.

Enterprise Data Flow

• Enterprise data flow involves the collection, analysis, and reporting of data for business decision-making.

Activity

• On page 202 of the Quick Guide, study the concept of data and its collection.

Data

• Data are a collection of discrete or continuous values that convey information.
• A datum is an individual value in a collection of data.
• Data are usually organized into structures like tables that provide additional context and meaning.

Reaching Data-Driven Decisions

• Analyze data using analytical tools to draw conclusions and make recommendations.

Variables

• Variables refer to characteristics or attributes that can be measured, manipulated, or controlled.
• Examples of variables include characteristics, statements, phenomena, and figures.

Characteristics

• Examples of characteristics include age, weight, height, skin color, nationality, and educational qualification.

Statements

• Examples of statements include opinions or attitudes towards a phenomenon.

Phenomena / Situations

• Examples of phenomena or situations include disaster, drought, success, employee attrition, customer churn.

Figures / Numbers

• Examples of figures or numbers include population, enrollment, monthly salary, number of typhoons, economic indicators, proportions, and rates.

What to do with Data?

• Collect data using tools like questionnaires.
• Clean and encode data.
• Treat and analyze data to get insights.

Questionnaire

• Types of questionnaires include standard, semi-standard, and original questionnaires.
• Validity and reliability of a questionnaire can be ensured through expert opinion, pilot testing, principal component analysis, and Chronbach Alpha analysis.

Treatment of Data

• Descriptive analysis involves calculating frequency, range, mean, median, mode, variance, standard deviation, and percentage.

Insights from Data

• Analyze data to gain insights, such as identifying trends, patterns, and correlations.
• Use data narratives to communicate findings in a clear and concise manner.