Competency Matrix for Data Science and Engineering Analytics
No. |
Task |
Minimum Competence
|
Minimum Competence
|
|---|---|---|---|
| 1 | Data Management | Identify content in different formats, edit and improve information content. Use search tools and application interfaces to extract specific data from source systems for standard business intelligence reporting. Define basic calculations in spreadsheets or data analytics tools. | Demonstrate use of basic tools and programming languages, like SQL, Visual Basic or statistical programming languages like Python Pandas library, or R, to access structured data and subset and reformat data (data prep) for projects. Discuss good practices in data cleaning, code annotation, and version control. Demonstrates ability to perform exploratory data analysis, identify correlated and colinear variables, present exploratory data analysis using a variety of techniques. Explain intellectual property rights and the importance of data security. Continuously improve your personal digital competence through training or self-paced learning. |
| 2 | Data Science and Analytics Terminology | Explain general terminology of all sub-disciplines of petroleum engineering and data science. Perform data gathering and cleansing for structured/unstructured data and preparation for detailed descriptive analysis. | Describe terminology specific to the sub-discipline of petroleum engineering and data science. Be able to use sample theory to characterize the data set used for building models. |
| 3 | Data Standardization | Identify what data standards (industry wide and company specific) exist in all sub-disciplines of petroleum engineering (drilling & completions, production and operations, reservoir and earth science). | Explain and use relevant data standards and definitions specific to the subdiscipline of petroleum engineering. |
| 4 | Database and Software | Have an awareness of the technical software and information databases that exist where you work in all sub-disciplines of petroleum engineering. | Recognize and use conventional technical software and information databases specific to the subdiscipline of petroleum engineering. Describe the potential of emerging solutions and technology trends. |
| 5 | Information Security | Identify basic steps to protect devices (for instance: by using anti-viruses, passwords, etc.). Explain how to protect one's reputation online and how to deal with the data produced through several accounts and applications. Describe your role in following best practices for safe and responsible computing. Complete information protection training. Have awareness of cyber security protocols. | Explain risks and uncertainties of data usage: i.e. cyber security, intellectual property and individual privacy. Summarize common terms of service, active protection of personal data, understanding other people privacy. Summarize how copyright and licenses apply to information and content. Describe corporate data confidentiality guidelines, records management policy and recommended practices. |
| 6 | Integration and Automation | Identify how to share the location of information, specify ways to share knowledge, content and resources. Describe how to search for and access data from source systems by using application programming interfaces (APIs). | Describe processes to modify, refine and re-use existing resources to create new, original and relevant content and knowledge. Demonstrate how master data can be used to normalize data from different sources to create an integrated data collection for analysis. Describe where related data sources are that can be added to projects to enrich analysis and modeling. |
| 7 | Basic Analytics | Layout the process of fitting a data set to a curve (regression) described by a mathematical formula (linear, exponential, hyperbolic, etc.). Specify the purpose of basic filtering and classification techniques. | Define when to use statistical techniques such as: filtering, clustering, classification and decision trees. Identify patterns in data sets. Demonstrate use of regression techniques to work with production decline curves. Explain 'curse of multidimensionality'. Explain 'over-fitting' a model. |
| 8 | Advanced Analytics | Describe the analytical terms: statistical uncertainty; data analysis, risk analysis; decision tree; Monte Carlo analysis etc. Identify the principles of artificial intelligence techniques such as machine learning and neural networks. | Illustrate how to factor statistical uncertainty into data analysis (probabilistic analysis) and risk analysis using tools like decision trees and Monte Carlo analysis. Explain how to justify the use of additional explanatory variables in developing a model or algorithm. |
| 9 | Modeling | Layout the organization of structured data models and how standard systems of record are organized. | Build and implement prediction, forecast and optimization models e.g. for drilling, reservoir and production. Outline how models can be used to share information about a complex product. Describe how engineers can build models hierarchically so that many different people can receive views of the same system. |
| Be familiar with various strategies to define model success criteria. | Explain how to define acceptable accuracy and success criteria for model evaluation. | ||
| Be familiar with various model calibration methods including data selection and error metrics. | Be able to interpret error metrics and adjust model hyper-parameters for tuning and generalization. | ||
| Be familiar with model validation techniques using held out data. | Ability to interpret bias, variance, stability, sensitivity, leakage, and noise tolerance. | ||
| 10 | Analytics Use Cases History | Explain how analytics has been used in the oil and gas industry by researching existing use case histories. Develop and implement analytics for surveillance activities e.g. exception-based or predictive-based surveillance. | Follow defined analytics techniques to augment the capabilities of subject matter and domain experts to solve real business problems (ref. the discussion that data science will not replace domain experts but make them more effective). |
| 11 | Analytics Program | Awareness of analytics programming language with ability to interpret basic commands. | Ability to assist in building tools to automate work. Understanding of basic command functions and their use. Assist in PE-specific data-driven algorithms with ability to relate to cross-industry existing methods. |
| 12 | Data Visualization | Read, Interpret and Create basic charts, graphs and dashboards from commonly used operational and financial data. Explain how to use spatial data GIS products. Describe how to recognize when graphics are used to misrepresent interpretations. | Layout cartographic (geospatial) design principles to effectively convey results to decision makers, ensuring the use of appropriate techniques to suit the purpose and audience (e.g. expert vs lay, technical vs. non-technical, data-savvy vs. otherwise). Illustrate how to use commonly used tools such as Spotfire and ArcGIS, for basic analysis and visualization. |
| 13 | Data Acquisition and Sensors | Specify the basic functionality of sensors for converting physical parameters to electrical signals. | Demonstrate the use of software packages, either open source or in-house, that control data acquisition. Demonstrate the ability to modify the data acquisition software, in such events as using newer sensors or different sampling signals. |
| 14 | Data Analytics | Be familiar with the methods to identify, collect, manipulate, transform, normalize, clean, and validate data. | Be able to conduct exploratory and detailed data analysis to screen data, identify vulnerabilities, and select inputs/outputs. |
| 15 | Predictive Analysis | To assist in the development of predictive analytics applications. | Ability to utilize data mining statistics, modeling machine learning and artificial intelligence techniques to analyze current data and generate future predictions. |
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