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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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