Statistics & Probability Terminologies in data science скачать в хорошем качестве

Statistics & Probability Terminologies in data science

About This Course This course is designed for complete self-study—no teacher required. Each module includes: • Clear explanations with real-world examples • Hands-on exercises with solutions • Datasets you can download and analyze • Python code examples (using free tools) • Projects relevant to African and global contexts Prerequisites • Basic mathematics (secondary school level) • Access to a computer with internet • Python installed (free: python.org) with libraries: NumPy, Pandas, Matplotlib, SciPy How to Use This Course 1. Read each module carefully 2. Complete all exercises before moving forward 3. Check your answers against provided solutions 4. Work on the mini-project at the end of each section 5. Join online communities (Reddit r/learnpython, Stack Overflow) for help   Module 1: Introduction to Statistics & Data Science What is Statistics? Statistics is the science of collecting, analyzing, and interpreting data to make informed decisions. In data science, statistics helps us: • Understand patterns in data • Make predictions • Test hypotheses • Quantify uncertainty Descriptive vs Inferential Statistics Descriptive Statistics: Summarize and describe data you have (e.g., average age of students in your class) Inferential Statistics: Make predictions about a larger group based on a sample (e.g., estimating average income in Cameroon from a survey of 1,000 people) Types of Data in Data Science Structured Data: Organized in tables (Excel spreadsheets, databases) Example: Customer purchase records, hospital patient data Unstructured Data: No predefined format (text, images, audio) Example: Social media posts, medical scans, phone call recordings Real-World Example: Mobile Money in Cameroon Orange Money and MTN Mobile Money generate massive amounts of data: • Transaction amounts (numerical data) • Transaction types: send money, pay bill, withdraw cash (categorical data) • Timestamps (time series data) • User locations (geospatial data) Data scientists use statistics to: • Detect fraudulent transactions • Predict cash demand at agents • Segment users for targeted promotions EXERCISE 1.1: Identify Data Types Classify each as structured or unstructured: 1. A CSV file of crop yields from farms in the West Region 2. WhatsApp messages about cocoa prices 3. Photos of diseased cassava plants 4. A database of student exam scores 5. Audio recordings of customer service calls at a bank SOLUTION 1.1 1. Structured (organized in rows/columns) 2. Unstructured (free-form text) 3. Unstructured (images) 4. Structured (database table) 5. Unstructured (audio files) Population vs Sample Population: The entire group you want to study (e.g., all mobile phone users in Cameroon) Sample: A subset of the population used for analysis (e.g., 5,000 randomly selected users) Why use samples? Studying entire populations is often impossible, expensive, or time-consuming. EXERCISE 1.2: Population or Sample? 1. Surveying all 10 million Cameroonian voters about election preferences 2. Testing 100 bags of rice from a shipment of 10,000 bags 3. Analyzing all transactions from your company last month 4. Interviewing 500 farmers about crop insurance SOLUTION 1.2 1. Population (all voters) 2. Sample (100 from 10,000) 3. Population (all company transactions) 4. Sample (500 farmers represent a larger population) MODULE 1 PROJECT: Data Science Use Cases Choose ONE business or organization in Cameroon: • A bank (e.g., Afriland First Bank) • A telco (MTN, Orange) • A hospital • An agricultural cooperative