Essential 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