Business Analytics: Types, Ecosystem, and Lifecycle

• Business Analytics involves collecting, organizing, analyzing, and interpreting data to make informed business decisions.

Types of Business Analytics

• Descriptive Analytics describes what happened or is currently happening.
• Answers the question: "What happened?"
• Diagnostic Analytics provides insights into why trends occurred and supports data-driven decision-making.
• Addresses the question: "Why did this happen?"
• Predictive Analytics is used to predict future trends or events.
• Answers the question: "What might happen in the future?"
• Prescriptive Analytics considers all possible factors and suggests actionable takeaways.
• Answers the question: "What should we do next?"

Data Ecosystem & Lifestyle

• Data Ecosystem includes the programming languages, packages, algorithms, cloud services, and infrastructure used to collect, store, analyze, and leverage data.
• Data Life Cycle is the path data takes from generation to interpretation, including generation, collection, processing, storage, management, analysis, and visualization.

Data Analytics Model (IMPACT Cycle)

• Identify the Questions (I): Help business partners identify critical business questions, setting clear expectations for time and work.
• Master the Data (M): Assemble, analyze, and synthesize all available information to answer critical questions.
• Create simple and clear visual presentations of data.
• Provide the Meaning (P): Articulate clear and concise interpretations in the context of the critical business questions.
• Actionable Recommendation (A): Provide thoughtful business recommendations based on data interpretation.
• C-ommunicate Insights (C): Use a multi-pronged strategy to get insights to as many people as possible in the organization.
• T-rack Outcomes (T): Set up a way to track the impact of your insights and follow up with business partners.

Basic Concepts of Probability and Statistics

• Learning Objectives include discussing variability, data collection methods, and the use of probability models.
• Population is the entire group of individuals or objects of interest and studying.
• Sample is a subset of the population and represents the population.
• Statistics are used to make inferences about the population based on the sample.
• UNIVARIATE - Univariate analysis examines a single variable at a time.
• BIVARIATE - Bivariate analysis looks at the relationship between two variables.
• MULTIVARIATE - Multivariate analysis explores the relationships between three or more variables simultaneously.

Variable

• Variable is any property, characteristic, number, or quantity that can vary.
• Examples include age, gender, income, expenditure, and family size.

Qualitative Variable

• Qualitative variables describe attributes that are not measurable.
• Examples are gender, hair color, eye color, and religion. -Nominal Variable - a categorical variable that labels or groups things into categories without any numerical value or order.
• Examples include sex, type of dwelling, and hair color.
• Ordinal Variable – a categorical variable with a clear order to its possible values.
• Examples include educational level, economic status, job satisfaction and sleep quality.

Quantitative Variable

• Quantitative variables can be measured and expressed as numbers.
• Examples are weight, age, height, shoe size, and profit.
• Discreet is a numerical variable that can only take on a finite number of distinct values within a given range.
• Examples are the number of pets in the household, the number of cousins you have, the number of children in the family, and the number of business sites.
• Continuous are numerical variables that can assume any value within a specified range.
• Examples include the amount of time it takes to complete a task, height, time, age, temperature and weight.