A Data Analytics course would ideally include a mixture of theoretical foundations and hands-on skills in deriving insights from data. The exact topics may differ based on the platform or institution,
but here's an overview of what is generally included:
1. Introduction to Data Analytics
Overview of data analytics and its uses
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2. Data Cleaning and Collection
Data types and sources (unstructured vs. structured data)
Data collection techniques (surveys, APIs, web scraping, etc.)
Data cleaning methods: missing values, duplicates, outliers, and inconsistencies
3. Data Exploration and Visualization
Exploratory Data Analysis (EDA)
Summary statistics and distribution analysis
Visualization tools and techniques for data
4. Statistical Analysis
Probability and statistical distributions
Hypothesis testing and confidence intervals
Correlation and regression analysis
Inferential statistics
5. Tools and Technologies
Excel for basic analysis
SQL for database querying
Python or R for advanced analytics
Introduction to Jupyter Notebooks or RStudio
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Model evaluation metrics
7. Data Ethics and Governance
Data privacy and security
Ethical considerations in data use
Regulatory compliance (e.g., GDPR)
8. Capstone Project or Case Studies
Real-world datasets
End-to-end project using data cleaning, analysis, visualization, and reporting
Presentation of findings
Would you like an outline of what a particular course entails (e.g., from Coursera, Google, or a university)?
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