ELECTIVE 1 REVIEWER.pdf

ELECTIVE 1 REVIEWER 9.Text Analytics - Analyzing text data to get insights. 1. Business Analytic - The process of using data to make better business Scenario: A company uses software to decisions. (focuses on...

ELECTIVE 1 REVIEWER 9.Text Analytics - Analyzing text data to get insights. 1. Business Analytic - The process of using data to make better business Scenario: A company uses software to decisions. (focuses on present and future) analyze customer reviews and identify common complaints. Scenario: A company looks at last month's sales data to decide which 10. Web Analytics - Analyzing website products to focus on next month. data to understand user behavior. 2. Business Intelligence - The use of Scenario: A blog owner checks how technology and data to help businesses many visitors they had and which pages understand their performance and make are the most popular. better decisions. (focuses on the present) 11.Embedded Analytics - Integrating Scenario: A restaurant checks a data analysis tools directly into a software dashboard that shows which meals are application. most popular. Scenario: A project management app 3. Descriptive Analytics - Analyzing past shows a chart of how many tasks were data to understand what happened. completed this week. Scenario: A store reviews last year’s sales 12.Machine Learning - A type of to see which months were the busiest. artificial intelligence where computers learn from data and improve over time. 3. Diagnostic Analytics - Looking deeper into data to understand why something Scenario: A music app learns your happened. favorite songs and recommends new ones based on your listening habits. Scenario: After a drop in sales, a shop analyzes customer feedback to figure out 13. Artificial Intelligence - Technology why fewer people are buying. that enables machines to perform tasks that usually require human intelligence. 5.Predictive Analytics -Using data to predict future trends or events. Scenario: A virtual assistant like Siri can understand your questions and give Scenario: A weather app analyzes answers. patterns to predict if it will rain next week. 14. Decision Intelligence - Combining 6. Prescriptive Analytics - Providing data, AI, and human knowledge to make suggestions based on data to make smarter decisions. decisions. Scenario: A retail store uses AI to decide Scenario: An app recommends which the best products to stock based on route you should take based on traffic customer preferences. predictions. 15. Deep Learning - A more advanced 7. Real-time Analytics - Analyzing data form of machine learning, where systems instantly as it comes in. learn from large amounts of data. Scenario: A social media platform shows Scenario: Self-driving cars use deep how many people are currently watching learning to recognize objects on the road. your live video. 16.Augmented Reality- Technology that 8. Cloud Analytics - Using the internet overlays digital information onto the real (cloud) to store and analyze data instead world. of local computers. Scenario: A game app lets you catch Scenario: A company uses Google Drive virtual creatures by pointing your phone at to store data and analyze it with online real-world locations. tools. 17. Edge Technology - Processing data 26. Scenario: An employee knows how to near the source (like a device), rather than read and interpret graphs showing sales sending it to a central cloud server. trends. Scenario: Smartwatches process fitness 26. Data Science - The study of data to data on the device itself rather than gain insights and knowledge. sending it to the cloud. Scenario: A data scientist analyzes social 18. Blockchain Technology - A system media trends to help a brand understand of recording information in a way that its audience. makes it difficult to change or hack. 27. Data Storytelling - Presenting data in Scenario: Cryptocurrency like Bitcoin a way that tells a story and makes it easy uses blockchain to track transactions to understand. (with emotions) securely. Scenario: A manager uses charts and 19. Data Mining - Finding patterns and graphs to explain why the company’s useful information from large sets of data. sales are rising. Scenario: An online store uses data 28. Data Governance - Managing data to mining to see which products are often ensure its quality, security, and proper use. bought together. Scenario: A company sets rules on how 20. Data Discovery - The process of customer data should be handled to finding new insights by exploring data. protect privacy. Scenario: A team looks at sales data to 29.Data Quality Management - discover that a product sells better in the Ensuring that data is accurate and useful. summer. Scenario: A business regularly checks its 21. Data Consumption - How businesses customer data to ensure it’s up-to-date. or individuals use data to make decisions. 30. Data Engineering - The process of Scenario: A company uses sales data to building systems to collect, store, and decide how much stock to order. analyze data. 22. Data Integration - Combining data Scenario: An engineer creates a database from different sources into one place. to store customer orders and ensure it runs smoothly. Scenario: A business collects customer data from its website, store, and app, and 31. Data Steward - A person combines it into one report. responsible for managing and protecting data in an organization. (like a librarian) 23. Data Cleansing - Removing or correcting incorrect or incomplete data. Scenario: A data steward ensures that customer data is secure and meets privacy Scenario: A company fixes errors in regulations. customer contact details before launching an email campaign. 32. Data Lineage - Tracking the origin and movement of data through a system. 24. Data Democratization - Making data accessible to everyone in an Scenario: A company tracks where its organization, not just experts. data comes from and how it’s changed over time to ensure accuracy. Scenario: All employees in a company can access and use data dashboards, not 33. Data Fabric - A unified architecture just the IT department. that connects and manages all data across platforms. 25. Data Literacy - The ability to understand and use data effectively. Scenario: A company uses a data fabric to ensure that data from different departments is consistent and accessible. 34. Data Warehousing - Storing large amounts of data in one place for analysis. Scenario: A company stores years of sales data in a warehouse to analyze long-term trends. 35. Data Visualization - Using charts, graphs, and other visuals to make data easier to understand. Scenario: A company uses a pie chart to show how its budget is spent. Data Culture - Encouraging the use of data in decision-making throughout an organization. Scenario: A business makes data-driven decisions a priority at every level, from management to staff.