Analytical Chemistry

1. Data Analysis
   1. Statistical Methods
      1. Descriptive Statistics
         1. Mean, median, mode
         2. Range and interquartile range
         3. Standard deviation and variance
      2. Inferential Statistics
         1. Hypothesis testing
            1. Null hypothesis and alternative hypothesis
            2. P-values and significance levels
         2. Confidence intervals
      3. Regression Analysis
         1. Linear regression
            1. Simple linear regression
            2. Multiple linear regression
         2. Nonlinear regression
         3. Logistic regression
      4. Correlation Analysis
         1. Pearson correlation coefficient
         2. Spearman's rank correlation
         3. Partial correlation
   2. Data Preprocessing
      1. Data Cleaning
         1. Handling missing data
         2. Removing duplicates
         3. Correcting errors
      2. Data Transformation
         1. Normalization and standardization
         2. Encoding categorical variables
         3. Log transformation
      3. Data Reduction
         1. Principal component analysis (PCA)
         2. Feature selection
         3. Dimensionality reduction techniques
   3. Data Visualization
      1. Graphical Representation
         1. Scatter plots and line charts
         2. Bar charts and histograms
         3. Box plots and heat maps
      2. Dashboard Creation
         1. Interactive dashboards
         2. Real-time data visualization
         3. Customizable layout and design
   4. Machine Learning and Predictive Analytics
      1. Supervised Learning
         1. Classification algorithms
            1. Decision trees
            2. Random forests
            3. Support vector machines (SVM)
         2. Regression algorithms
            1. Linear regression
            2. Ridge and Lasso regression
      2. Unsupervised Learning
         1. Clustering
            1. K-means clustering
            2. Hierarchical clustering
         2. Association rules
      3. Model Evaluation
         1. Cross-validation
         2. Confusion matrix
         3. ROC curves and AUC
   5. Chemometrics
      1. Multivariate Analysis
         1. Principal component analysis (PCA)
         2. Partial least squares (PLS) regression
      2. Experimental Design
         1. Factorial design
         2. Response surface methodology (RSM)
         3. Mixture design
      3. Pattern Recognition
         1. Discriminant analysis
         2. Clustering techniques
   6. Software and Computational Tools
      1. Statistical Software
         1. R and Python for data analysis
         2. MATLAB and SPSS for advanced analytics
      2. Data Management Tools
         1. SQL databases
         2. NoSQL databases
      3. Big Data Technologies
         1. Hadoop and Spark frameworks
         2. Cloud-based analytics platforms