. Introduction to Data Analytics
Overview of data analytics and its applications
Data lifecycle and workflow
2. Data Cleaning and Collection
Data sources and types (structured data vs. unstructured data)
Data collection techniques (surveys, APIs, web scraping, etc.)
Data cleaning methods: missing values, duplicates, outliers, and inconsistencies handling
3. Exploratory Data Analysis and Visualization
Exploratory Data Analysis (EDA)
Summary statistics and distribution analysis
Data visualization methods and tools
4. Statistical Analysis
Probability and statistical distributions
Hypothesis testing and confidence intervals
Correlation and regression analysis
Inferential statistics
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