Intro to Analytics Chapter 1

Descriptive Analytics

Focuses on summarizing and describing historical data to provide insights into past trends and patterns.

Diagnostic Analytics

Analyzes past data to identify the root causes of specific outcomes or events, answering the question 'Why did it happen?'

Predictive Analytics

Uses historical data to forecast future outcomes, addressing questions like 'What is likely to happen in the future?'

Prescriptive Analytics

Recommends actions to optimize or improve a situation, answering questions such as 'What should we do?'

Exploratory Analytics

Involves exploring and analyzing data to identify potential trends and insights when there is no clear objective.

Data Collection

The process of gathering large datasets from various sources, including databases and surveys.

Data Cleaning

The process of correcting data errors, removing duplicates, and ensuring dataset accuracy for analysis.

Data Reporting

Creating reports and visualizations that communicate findings to team members and stakeholders clearly.

Predictive Analysis

Utilizes algorithms to predict future trends and outcomes based on historical data.

Data-Driven Decision-Making

Making decisions based on the analysis of data to identify opportunities for improvement.

Continuous Improvement in Data Analytics

Involves professional development and adapting to new data sources, tools, and techniques.

Data Pipeline Development

Designing and developing processes that move data from source systems to data storage systems.

Data Storage Management

Managing data in databases, data lakes, and warehouses, including tasks like data partitioning and indexing.

Data Quality Control

Ensuring that data is accurate, consistent, and free of errors.

Machine Learning Model Training

The process of training machine learning models on data using various algorithms.

Data Integration

Combining data from various sources to create a unified view for analysis.

A/B Testing

A statistical method used to compare two versions of a product or service to determine which performs better.

Data Storytelling

The practice of presenting complex technical concepts and insights in a clear and understandable way.

Collaboration in Data Science

Working with engineers, developers, and stakeholders to design solutions that align with business goals.

Cloud Computing in Data Engineering

Using cloud platforms to deploy and manage data infrastructure.

Data Mining

A business process for exploring large amounts of data to discover meaningful patterns and rules.

Customer Relationship Management (CRM)

A broad topic focusing on managing a company's interactions with current and potential customers to improve business relationships.

Data Warehouse

A system that stores large amounts of historical data from various sources in a consistent format for analysis.

Meaningful Patterns

Significant data trends discovered through data mining that can help improve business operations.

Learning Relationships

The connections formed by businesses understanding and utilizing knowledge about their customers over time.

Predictive Modeling

A statistical technique used to predict future behavior by analyzing patterns in existing data.

Oeno-philes

Wine enthusiasts or experts who have extensive knowledge about wines.

Analytic CRM

The practice of using data mining and analytical techniques to enhance customer relationship management.

Computing Power

The capability of a computer to process data, which has become increasingly affordable and influential in data mining.

The Virtuous Cycle of Data Mining

A cyclical process involving discovering patterns, responding to them, and driving value through actionable insights.

Data Science

The practice of using data to understand and solve real-world problems.

Data Scientist

A professional who analyzes and interprets complex data to help companies make decisions.

Analytics

The process of taking data and putting it in front of the right people to support decision-making.

Machine Learning

A field of artificial intelligence that uses algorithms to allow software to improve its performance on tasks through experience.

Decision Science

The discipline of turning raw data into information that helps make informed business decisions.

Data Cleaning

The process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset.

SQL

A programming language used for managing and querying data in relational databases.

ETL

Extract, Transform, Load - a process that involves extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a database.

Venn Diagram of Data Science

A representation that shows the intersection of math/statistics, domain expertise, and programming skills necessary to succeed in data science.

Business Intelligence Analyst

A professional who uses data analysis tools and software to understand and improve business processes.

Data Science Bootcamp

An intensive, short-term program lasting 8 to 15 weeks that teaches data science skills.

Graduate Degree

An advanced academic program, usually requiring two years, focusing on data science or a related field.

Self-Teaching

The process of learning without a formal instructor, often through books, online courses, and personal projects.

Industry Connections

Relationships between educational programs and businesses that can help with internships and job placements.

Project Work

Hands-on tasks that involve applying data science skills to real-world problems, often included in bootcamp and academic curricula.

Application Process

The series of steps taken to apply to a graduate program, including submitting documents like transcripts and letters of recommendation.

Networking

Building professional relationships that can help in job searches, commonly emphasized in bootcamp programs.

Cost of Education

The financial expense associated with pursuing a degree or bootcamp, which can include tuition and opportunity costs.

Teaching Assistantship

A position often offered in graduate programs that involves assisting professors and may include funding for tuition.

Alumni Network

Groups of former students from a program that can provide support, connections, and job opportunities.

Big Data

Data that is so large, fast, or complex that it's difficult or impossible to process using traditional methods.

3 Vs of Big Data

Volume, Variety, and Velocity - these are the three key characteristics that define Big Data.

Data Scientist

A professional who uses statistical and computational skills to analyze and interpret complex data.

Business Intelligence (BI)

Technologies and strategies used by enterprises for data analysis of business information.

Data Science

An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Analytic Sandbox

A secure environment for analysts to test and develop models using raw and aggregated data without affecting production systems.

Structured Data

Data that adheres to a predefined data model and is easily searchable, typically organized in rows and columns.

Unstructured Data

Data that does not have a predefined data model or structure, making it more complex to analyze.

Semi-structured Data

Data that does not conform strictly to a data model but contains tags or markers to separate elements.

Machine Learning

A subset of artificial intelligence that focuses on building systems that learn from data and improve their performance over time without being explicitly programmed.

Predictive Analytics

A form of advanced analytics that uses historical data, machine learning, and statistical algorithms to predict future outcomes.

Genomics

The branch of molecular biology concerned with the structure, function, evolution, and mapping of genomes.

Data Deluge

The overwhelming amount of data being generated today from various sources, requiring new strategies for data management.

OLAP

Online Analytical Processing, a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access.

Hadoop

An open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

Clickstream Data

Data generated from a user's interactions on a website, tracking their navigation path and how they interact with different elements.

Data Warehousing

The process of collecting and managing data from various sources to provide meaningful business insights.

Data Mining

The practice of examining large databases to generate new information, often using complex algorithms.

Project sponsor

The executive who authorizes the project and ensures alignment with the company's strategic goals.

Project manager

The individual overseeing day-to-day operations of the project, coordinating with stakeholders and ensuring the project stays on track.

Financial operations

The team responsible for managing the project's budget and finances to ensure it stays within budget.

Database administrator (DBA)

The professional managing the data infrastructure, ensuring secure and efficient data storage and access.

Researchers

Individuals responsible for interpreting data results and making recommendations based on statistical and computational analysis.

Partners

External stakeholders collaborating with the company, such as software vendors or consultants, providing expertise or tools.

End users (customers)

The ultimate beneficiaries of the project, who will benefit from an optimized inventory management system.

Budgeting (in financial operations)

The process of creating and maintaining the project budget, ensuring it reflects the project's needs.

Communication (in project management)

The project manager's responsibility to inform stakeholders about the project's progress, challenges, and successes.

Quality assurance (in project management)

The process of ensuring that the project meets quality standards and that deliverables meet project requirements.

Database security (DBA responsibility)

Implementing measures to protect project data from unauthorized access, theft, or corruption.

Testing and feedback (in end users)

The process in which end users test the project at various stages and provide feedback on usability and performance.

Providing recommendations (by researchers)

The act of researchers giving suggestions to inform project objectives and guide decision-making based on findings.

Collaborating with partners

The need for partners to work together to achieve project objectives effectively.

Providing user documentation (by end users)

The process in which end users review and ensure that project documentation meets their needs.

Providing project support (by project sponsor)

The project sponsor's role in guiding the project team and removing obstacles to stay on track.

Failure Criteria

Guidelines that help a team understand when to stop trying or settle for results during a project.

Key Stakeholders

Individuals or groups that will benefit from the project or be significantly impacted by it.

Success Criteria

Standards defined by stakeholders to judge the success of the project.

Analytics Sponsor

The person who funds the project and provides high-level requirements.

Active Listening

A communication technique that involves fully concentrating, understanding, responding, and remembering what is being said.

Interview Preparation

The process of drafting questions and reviewing them with colleagues before an interview.

Open-ended Questions

Questions designed to encourage detailed responses rather than simple yes or no answers.

Probing Questions

Follow-up questions intended to elicit more detailed information in an interview.

Documenting Findings

The practice of recording what was discussed in an interview for future reference.

Project Scope

The boundaries of what will be covered in a project, including its goals and objectives.

Stakeholder

A person, group, or organization that is actively involved in a project, is affected by its outcome, or can influence its outcome.

Business Stakeholders

Individuals from departments like marketing or product who oversee business decisions and request analyses to make better decisions.

Communication

The process of sharing information and updates with stakeholders regarding project timelines, progress, and findings.

Engineering Stakeholders

Teams responsible for maintaining software and hardware products who rely on data science work to inform technical decisions.

Corporate Leadership

High-level executives such as directors and vice presidents who need data to guide organizational strategies.

Your Manager

The individual who supervises your work, provides guidance, and can act as a stakeholder by influencing project direction.