Comprehensive Study Guide on Scientific Research Methodology, Experimental Design, Data Presentation, and Descriptive Analysis

Travel & Tourism — Picture Task, Conversation & Role Play

6.2 Wo fahren wir hin?

6.5 Wie waren die Schulferien?

6.1 Im Urlaub und unterwegs

6.4 Probleme, Probleme

6.3 Ich möchte eine Reservierung machen!

6.6 Ich möchte um die Welt reisen!

Database, Systems, Version Control, Data Ethics, and AI Terminology

Week 1: Chapter 2 Data Representation

#3 (2.4-2.5) Chapter 2: Data Management

#2 (2.1-2.3) Chapter 2: Data Management

#1 (VOCAB) Chapter 2: Data Management

ITCS-3320 Week 1 Security of Data & Applications Vocabulary

11: Cleaning Data in R

DAHA ÇOK ÇALIŞ 🍀🍀

📚 STAT 100 — Chapters 1–3 Flash Cards FLASH CARD 1 Front: What is an estimate? Back: A number or percentage used to describe something about a population. Example: 75% of students play a sport → 75% = estimate FLASH CARD 2 Front: What is the population of interest? Back: The whole group of people or things you want information about. Example: 75% of high school students play a sport → high school students = population. FLASH CARD 3 Front: What is an attribute? Back: A characteristic or piece of information being measured about an observation. Example: Name, major, GPA, age. FLASH CARD 4 Front: What is an observation? Back: One person, object, place, or event in a dataset. ⭐ Usually, one row = one observation. FLASH CARD 5 Front: In a tidy data table, what does each ROW represent? Back: One observation. FLASH CARD 6 Front: In a tidy data table, what does each COLUMN represent? Back: One attribute. FLASH CARD 7 Front: What is quantitative data? Back: Data that represents a number or amount. Examples: GPA, age, price, height, wind speed. FLASH CARD 8 Front: What is categorical data? Back: Data that places something into a group or category. Examples: major, eye color, blood type, ocean. FLASH CARD 9 Front: Is blood type quantitative or categorical? Back: Categorical. A, B, AB, and O are categories, not amounts. FLASH CARD 10 Front: What is a rating scale? Back: Categories that have a meaningful order. Example: Poor → Fair → Good → Excellent. FLASH CARD 11 Front: What is text data? Back: Written words or responses. Example: A student writing why they enjoyed a class. FLASH CARD 12 Front: What is time-series data? Back: Data collected or measured over time. Example: Google’s stock price each day. FLASH CARD 13 Front: What is a measurement? Back: The way an attribute is measured or collected. FLASH CARD 14 Front: What is reliability? Back: How much you can trust a measurement to give dependable information. FLASH CARD 15 Front: What is data validation? Back: Rules used to prevent invalid data from being entered. Example: If GPA must be 0–4, don’t allow someone to enter 40. FLASH CARD 16 Front: What is data cleaning? Back: Finding and handling invalid or incorrect values AFTER data has been collected. FLASH CARD 17 Front: Validation vs. data cleaning? Back: Validation = PREVENT bad data. Cleaning = deal with bad data afterward. FLASH CARD 18 Front: What is a generalization claim? Back: Using information from a sample to make a claim about a larger population. FLASH CARD 19 Front: What is a causal claim? Back: A claim saying one thing causes another thing to change. FLASH CARD 20 Front: What is a causal factor? Back: The thing believed to cause the change. Example: “Eating breakfast improves grades.” → Eating breakfast = causal factor. FLASH CARD 21 Front: What is an outcome? Back: The thing that is affected or changed. Example: “Eating breakfast improves grades.” → Grades = outcome. ⭐ QUICK MEMORY CARD Front: What should I remember most for Chapters 1–3? Back: Estimate = NUMBER Population = WHO Attribute = WHAT Observation = ROW Attribute = COLUMN Quantitative = AMOUNT Categorical = GROUP Time series = OVER TIME Reliability = CAN I TRUST IT? Validation = PREVENT bad data Cleaning = FIX/REMOVE bad data afterward

DS 266 Big Data Management Flashcards

Unit 2 Networks of Exchange (1200-1450) Vocabulary

Travel and Tourism