Essential Data Analysis Techniques

Deepshika

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Data Analysis Techniques

Essential Data Analysis Techniques Every Analyst Should Know

  1. Descriptive Statistics
    • Measures of Central Tendency
      • Mean
      • Median
      • Mode
    • Measures of Spread
      • Variance
      • Standard Deviation
    • Summarizing Data
  2. Data Cleaning
    • Handling Missing Values
    • Managing Outliers
    • Resolving Data Inconsistencies
    • Ensuring Data Accuracy and Reliability
  3. Exploratory Data Analysis (EDA)
    • Visualization Tools
      • Histograms
      • Scatter Plots
      • Box Plots
    • Uncovering Patterns, Trends, and Relationships
  4. Hypothesis Testing
    • Making Inferences from Sample Data
    • Understanding P-Values
    • Confidence Intervals
    • Statistical Significance
  5. Correlation and Regression Analysis
    • Measuring Relationships Between Variables
    • Predicting Future Outcomes
    • Analyzing Existing Data
  6. Time Series Analysis
    • Analyzing Data Over Time
    • Identifying Trends, Seasonality, and Cyclical Patterns
    • Forecasting
  7. Clustering
    • Grouping Similar Data Points
    • Customer Segmentation
    • Market Analysis
  8. Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • Reducing Number of Variables
    • Preserving Information
  9. ANOVA (Analysis of Variance)
    • Comparing Means of Three or More Samples
    • Determining Differences in Means
  10. Machine Learning Integration
    • Applying Machine Learning Algorithms
    • Enhancing Data Analysis
    • Enabling Predictions and Task Automation
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