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Data Analysis

Data Collection and Acquisition​

  • What you Need to Know

    • Data Sources and Collection Methods

      • Primary data collection through surveys and experiments
      • Secondary data from databases, APIs, and public datasets
      • Web scraping and automated data collection
      • Resources:

    • Database Querying and SQL

      • SQL fundamentals for data extraction
      • Joins, aggregations, and window functions
      • Database optimization for analytics
      • Resources:

    • API Integration and Data Extraction

Data Cleaning and Preprocessing​

  • What you Need to Know

    • Missing Data Analysis and Imputation

      • Missing data patterns and mechanisms (MCAR, MAR, MNAR)
      • Imputation techniques (mean, median, mode, interpolation)
      • Advanced imputation methods (KNN, iterative)
      • Resources:

    • Outlier Detection and Treatment

      • Statistical outlier detection methods (Z-score, IQR)
      • Multivariate outlier detection techniques
      • Outlier treatment strategies and impact assessment
      • Resources:

    • Data Type Conversion and Standardization

      • Data type validation and conversion
      • String processing and text cleaning
      • Date and time data standardization
      • Resources:

Exploratory Data Analysis (EDA)​

  • What you Need to Know

    • Univariate Analysis Techniques

    • Bivariate and Multivariate Analysis

      • Correlation analysis and scatter plot matrices
      • Cross-tabulation and contingency tables
      • Multivariate relationships and interaction effects
      • Resources:

    • Data Quality Assessment

      • Data completeness and consistency checking
      • Duplicate detection and deduplication
      • Data validation and constraint checking
      • Resources:

Data Transformation and Feature Engineering​

  • What you Need to Know

    • Feature Scaling and Normalization

    • Categorical Data Encoding

    • Feature Creation and Selection

Statistical Analysis and Modeling​

  • What you Need to Know

    • Regression Analysis

      • Simple and multiple linear regression
      • Logistic regression for binary outcomes
      • Polynomial regression and non-linear relationships
      • Resources:

    • Classification and Clustering

      • Decision trees and ensemble methods
      • K-means clustering and hierarchical clustering
      • Classification evaluation metrics and interpretation
      • Resources:

    • Statistical Significance and Effect Size

      • Power analysis and sample size determination
      • Multiple testing correction methods
      • Effect size calculation and interpretation
      • Resources:

Data Manipulation with Python​

  • What you Need to Know

    • Pandas DataFrames and Series

      • DataFrame creation, indexing, and selection
      • Data filtering, sorting, and grouping operations
      • Merging, joining, and concatenating datasets
      • Resources:

    • NumPy Array Operations

      • Array creation, indexing, and slicing
      • Mathematical operations and broadcasting
      • Array reshaping and manipulation
      • Resources:

    • Data Aggregation and Grouping

      • GroupBy operations and split-apply-combine
      • Pivot tables and cross-tabulations
      • Window functions and rolling calculations
      • Resources:

Database Integration and Big Data​

Statistical Software and Tools​

  • What you Need to Know

    • R Programming for Statistics

      • R syntax and data structures
      • Statistical packages and libraries
      • R Markdown for reproducible analysis
      • Resources:

    • Jupyter Notebooks and Reproducible Research

Ready for Machine Learning? Continue to Module 3: Machine Learning to master predictive modeling, algorithm selection, and model evaluation techniques.