Data Analytics
Knowledge and ability to identify patterns in data. Ability to use statistics, operations research, and other mathematical tools to make sense of information generated or collected by organisations.
Proficiency Level
Level 1 (Follow)
- Appreciation of custom coding requirement to customise every step of the data science/analytics life cycle.
- Awareness of the mainstream programming languages available (e.g., R, Python, etc).
- Awareness and understanding of all stages of data science/analytics life cycle and specifics of the data science/analytics project management.
Level 2 (Assist)
- Familiarity with the wide range of mainstream commercial and open-source data science/analytics software tools, their constraints, advantages, disadvantages and areas of application.
- Intermediate skills in using at least one such tool.
- Familiarity with programming languages (e.g., R, Python, etc).
- Basic programming skills.
- Interpret an existing script of moderate complexity.
- General understanding of all stages of Data Science/Analytics life cycle and project management.
- Assist in the scoping, planning and delivery of projects under the direction of Senior Analyst or Lead Analyst, including documenting business requirements.
- Manage moderate-scale projects and assist in management of large-scale or multi-stage projects.
Level 3 (Apply)
- Familiarity with the wide range of data science/analytics commercial and open-source software tools, their constraints, advantages, disadvantages, areas of application and mainstream packages relevant to technical stages of data science/analytics projects.
- Expertise with at least one such tool from intermediate to advanced skills in programming languages used for data science/analytics (e.g., R, Python, etc) and ability to apply these for data acquisition, pre-processing, modelling and model deployment.
- Interpret and modify existing scripts and conduct quality checks.
- Conduct general impact analysis on database change management.
- Prepare a project plan, communicate the plan to the team and allocate the tasks.
- Experience in working with stakeholders and the collection of business requirements for data science projects including establishing the business need, key stakeholders, scope, resourcing and success criteria for a specific issue.
Level 4 (Ensure)
- Familiarity with the wide range of data science/analytics commercial and open-source software tools, their constraints, advantages, disadvantages, areas of application and best-practice packages.
- In-depth expertise with at least one or two such tools.
- Advanced skills in programming languages used for data science/ analytics (e.g., R, Python, etc).
- Apply these skills for data acquisition, pre-processing, modelling and model deployment.
- Ability to coordinate quality checks of scripts for one or more projects as well as to maintain and monitor a library of team scripts and coordinate its review and updates.
Level 5 (Strategise)
- In-depth knowledge of big data technologies, the specifics of integrating them with existing information systems and using them for data science/analytics solutions.
- Design and lead data science/analytics projects including creation of a big data environment by setting up and deploying tools, capturing and evaluating results and deploying big data solutions on large-scale data sets in the enterprise.
- Lead a team in identifying a big data problem, selecting the adequate techniques and performing data acquisition, data audit, cleansing, pre-processing, model development and testing and deployment.
- Design and implement a multi-stage solution that encapsulates systems dealing with both structured and unstructured data.
- Share knowledge, experience and skills with team members through coaching and mentoring.
- In-depth understanding of all stages of data science/analytics life cycle and specifics of data science/analytics project management, the relevant resources, time requirements, etc.
- Manage large-scale data science/analytics projects and assist in managing data science/analytics programmes.