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
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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
5. Tools and Technologies
Excel for simple analysis
SQL for querying databases
Python or R for advanced analytics
Introduction to Jupyter Notebooks or RStudio
Supervised vs. unsupervised learning
Model evaluation metrics
7. Data Ethics and Governance
Data privacy and security
Ethical considerations in data usage
Regulatory compliance (e.g., GDPR)
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8. Capstone Project or Case Studies
Real-world datasets
End-to-end project using data cleaning, analysis, visualization, and reporting
Presentation of findings