Descriptive Analyst
Techniques:
Summary Statistics: Mean, median, mode, standard deviation
Data Visualization: Bar charts, histograms, pie charts, box plots
Reporting Tools: Excel, Tableau, Power BI
Diagnostic Analyst
Techniques:
Drill-Down Analysis: Breaking down data into smaller parts for detailed examination
Root Cause Analysis: Fishbone diagrams, 5 Whys technique
Data Mining: Association rule learning, clustering
Predictive Analyst
Techniques:
Machine Learning Models: Linear regression, decision trees, neural networks
Time Series Analysis: ARIMA models, exponential smoothing
Predictive Analytics Tools: SAS, SPSS, Python (scikit-learn), R
Prescriptive Analyst
Techniques:
Optimization Algorithms: Linear programming, integer programming
Simulation: Monte Carlo simulation, agent-based modeling
Decision Analysis: Decision trees, cost-benefit analysis
Exploratory Data Analyst
Techniques:
Data Visualization: Scatter plots, heat maps, pair plots
Clustering: K-means, hierarchical clustering, DBSCAN
Correlation Analysis: Pearson correlation, Spearman rank correlation
Business Analyst
Techniques:
Requirements Gathering: Interviews, surveys, focus groups
Process Modeling: Flowcharts, BPMN diagrams
Business Intelligence Tools: SQL, Power BI, Tableau
Operations Analyst
Techniques:
Process Optimization: Lean Six Sigma, value stream mapping
Performance Metrics: Key performance indicators (KPIs), balanced scorecards
Workflow Analysis: Swimlane diagrams, Gantt charts
Marketing Analyst
Techniques:
Market Segmentation: Cluster analysis, RFM analysis
Customer Behavior Analysis: Cohort analysis, customer journey mapping
Campaign Performance Analysis: A/B testing, multivariate testing
Financial Analyst
Techniques:
Financial Modeling: Discounted cash flow (DCF) analysis, Monte Carlo simulation
Budgeting: Variance analysis, zero-based budgeting
Financial Ratios: Liquidity ratios, profitability ratios
Healthcare Data Analyst
Techniques:
Patient Data Analysis: Survival analysis, cohort studies
Healthcare Metrics: Length of stay, readmission rates
Clinical Data Analysis: Clinical trials analysis, health outcomes research
Data Scientist
Techniques:
Machine Learning: Supervised learning, unsupervised learning, reinforcement learning
Statistical Analysis: Hypothesis testing, ANOVA, chi-square tests
Data Engineering: ETL processes, data warehousing, big data technologies (Hadoop, Spark)