Our curriculum is for Federal business users, employees who need to increase their data literacy skills to perform their jobs and support the agency mission.

1. Analytics Process Model (APM)
2. Role of the data analyst
3. Problem-solving frameworks
4. Evidence-based decision-making
5. Stakeholder analysis
6. Analytical objectives and hypotheses
7. Excel tables and range references
8. Anchoring and formulas
9. Sorting and filtering
10. VLOOKUP and IF functions
11. Conditional formatting
12. PivotTables and PivotCharts
13. Text manipulation formulas
14. Power Query / Get & Transform
15. Descriptive statistics
16. Central tendency:
17. Measures of variation:
18. Skewness and kurtosis
19. Frequency distributions and histograms
20. Probability and probability distributions
21. Sampling methods
22. Hypothesis testing
23. t-tests
24. Correlation and regression
25. Chart selection and design
26. Scatterplots, histograms, line and bar charts
27. Dashboards
28. Time series visualization
29. Comparison and deviation charts
30. Gestalt principles
31. Visualization best practices
32. Storytelling through visuals
33. Surveys and interviews
34. Primary vs. secondary data
35. Exploratory Data Analysis (EDA)
36. Data cleansing
37. Sampling techniques
38. Data quality management
39. Power Query transformations
40. Regression analysis
41. Time series forecasting
42. Monte Carlo simulation
43. Statistical Process Control (SPC)
44. Sensitivity analysis
45. Scenario modeling
46. Solver and Scenario Manager
47. Data-driven decision-making
48. Tradeoff analysis
49. Risk and uncertainty analysis
50. Strategic thinking
51. Decision frameworks
52. Decision support systems (DSS)
53. Presenting analytical results
54. Audience and stakeholder communication
55. Executive summaries
56. Dashboards and visual aids
57. Presentation best practices
58. Analysis reports
59. Data storytelling
60. Federal Data Ethics Framework
61. Privacy and confidentiality
62. FOIA, FISMA, Evidence Act
63. Data governance
64. Data quality and integrity
65. Ethical AI considerations
66. Data literacy
67. Data culture
68. KPIs and metrics
69. Cross-functional communication
70. Building data-savvy teams
71. Becoming a data champion

Our students are employees expected to utilize data in their work across most Federal job series.

1. Analytical Thinking and Problem-Solving
2. Data Analysis Skills
3. Excel Analytics Skills
4. Statistical and Quantitative Skills
5. Data Visualization Skills
6. Data Collection and Preparation Skills
7. Communication and Presentation Skill
8. Decision-Making Skills
9. Data Governance and Ethics Skills
10. Leadership and Organizational Skills
11. Practical Application Skills

Data analysis training supports decision-making in organizations by teaching employees how to collect, interpret, evaluate, and communicate data so leaders can make informed, evidence-based decisions instead of relying on assumptions or intuition. Here are the primary ways the training supports organizational decision-making: Improves problem identification Helps employees define business problems clearly Teaches analytical objective setting and hypothesis development Supports root-cause analysis and structured problem-solving.