| 1.00 | Apply Advanced Statistical Methods | 15% | - |
| 1.01 | Understand and apply advanced statistical techniques such as regression analysis, ANOVA, and hypothesis testing. | - | - |
| 1.02 | Interpret and communicate results of a data set by using inference skills. | - | - |
| 1.03 | Design scripts for automating data analysis workflows. | - | - |
| 1.04 | Hypothesis testing and confidence intervals. | - | - |
| 2.00 | Evaluate Advanced Data Collection Methods | 20% | - |
| 2.01 | Identify and define common data collection methods, including surveys, interviews, observations, experiments, and secondary data sources. | - | - |
| 2.02 | Analyze data models to eliminate bias against certain groups or individuals. | - | - |
| 2.03 | Analyze the advantages and limitations of each data collection method, considering factors such as cost, time, and feasibility. | - | - |
| 3.00 | Data Security, Privacy, and Ethical Impacts | 25% | - |
| 3.01 | Implement techniques such as differential privacy to minimize the risk of re-identification in datasets. | - | - |
| 3.02 | Analyze security measures to protect data systems from adversarial attacks and unauthorized access. | - | - |
| 3.03 | Clearly define roles and responsibilities for individuals involved in the development and deployment of data sets and analysis. | - | - |
| 3.04 | Address ethical implications associated with the use of machine learning, particularly in sensitive domains. | - | - |
| 3.05 | Consider the societal impact of machine learning applications and strive to minimize negative consequences. | - | - |
| 4.00 | Data Visualization | 20% | - |
| 4.01 | Understanding data visualization tools | - | - |
| 4.02 | Develop skills in storytelling with data, conveying insights through compelling narratives supported by visualizations. | - | - |
| 4.03 | Interpret complex visualizations and communicate insights effectively. | - | - |
| 4.04 | Applying data visualization techniques to compile data sets. | - | - |
| 5.00 | Big Data Technologies | 20% | - |
| 5.01 | Understand the concepts and challenges associated with big data. | - | - |
| 5.02 | Explore tools and technologies for handling large datasets. | - | - |
| 5.03 | Develop skills in feature engineering to enhance the predictive power of machine learning models. | - | - |
| 5.04 | Write scripts in a programming language (e.g., Python, R) for automating data analysis workflows. | - | - |
