COMPETENCES BIG DATA CONCEPT 25 th February 2015
UNECE High-Level Group for the Modernisation of Statistical Production and Services
/ Modernisation Committee on Organizational Framework and Evaluation
S/he must be accomplished in analytical methods, and have an appreciation and understanding of information presented in mathematical terms; have the ability to extract the key messages or underlying trends present within data; and be able to present statistical results and concepts, both orally and in writing, in a confident and professional manner.
A Big Data Scientist must have or be able to quickly acquire knowledge and understanding of statistical methodology, concepts and research in the area of Big Data and how this can be applied to the production of official statistics.
A.
KNOWLEDGE
1.
Knowledge of data science and data science methods including those used in the areas of
Machine learning, Data mining, Artificial intelligence and Computational Statistics.
2.
Knowledge of statistics and statistical theory (e.g. representativeness: target population, sampling frame, sampling, weighting, inference; measurement: validity, reliability; modelling).
3.
Knowledge of High Performance Computing platforms like Hadoop, Spark, Hive, Pig and others.
4.
Knowledge of data visualization methods.
5.
Knowledge of potential risks to data security and standards in data security at all times.
B.
SKILLS
B1: Technical skills
Statistical skills:
1.
Ability to apply methods to assess representativeness of data given a target population.
2.
Ability to apply methods to assess the validity and reliability of a measured variable.
3.
Ability to apply noise reduction methods and techniques.
4.
Ability to determine the disclosure risks of data.
5.
Curious in an innovative way: able to think out of the traditional box.
BIG Data & computing skills:
1.
Ability to code algorithms for data processing in suitable technologies/environments;
2.
Strong ability to work with a programming/scripting languages, e.g. bash, R and Python for data preparation
3.
Ability to develop software for more advanced data processing tasks (in programming language of choice i.e. Java, Python, R);
4.
Can use High Performance Computing platforms in an efficient way
5.
Can operate on structured and unstructured data with a wide range of tools (from software development to computational packages);
6.
Knowledge of Linux operating systems.
B2: Analytical skills
DEFINITION: The ability to address issues/problems, to overlook a certain complexity, approach a problem from different angles, set it to fray and indicate relationships.
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PERFORMANCE INDICATORS
1.
Can assimilate information from a range of sources.
2.
Organizes (complex) information to make it accessible.
3.
Can (quickly) analyse complex data, identifying what is relevant/ sees similarities between data and/or information.
4.
Can join (combine) various data processing techniques to achieve a given analytical task/
Analyse issues/problems from different angles.
The following skills are applicable to all data scientists - including those working on Big Data:
B3: Behavioural skills
1.
SITUATIONAL AWARENESS
DEFINITION: The ability to observe relevant developments (political, social, cultural etc.) and to exploit them for the work-environment.
PERFORMANCE INDICATORS
1.
Follows and shows awareness of recent developments and trends in the organization and the environment / society.
2.
Identifies environmental factors and developments that could affect the current policy.
3.
Gives meaning to trends and developments for the organization
4.
Translates political, social developments and / or developments into statistical indicators
5.
Is able to work in a group of people in a constructive way and to motivate others
2.
RESULT ORIENTATION
DEFINITION: Is able to act in such a way that the predetermined goal is achieved.
PERFORMANCE INDICATORS
1.
Strives to understand what the results mean; Knows what s/he wants to achieve from his/her data analysis rather than just following procedures
2.
Sets measurable, achievable and clear objectives on his/her own responsibility.
3.
Meets deadlines and doesn’t miss appointments.
4.
Undertakes targeted actions to achieve the objective.
3.
INNOVATIVE / CREATIVE ACTION
DEFINITION: The ability to think both in- and outside the established patterns to develop new ideas, concepts and solutions.
PERFORMANCE INDICATORS
1.
Demonstrates originality in thought and action.
2.
Comes up with innovative ideas.
3.
Thinks unconventionally about questions or problems.
4.
Escapes from existing concepts ('out of the box thinking’) making new roads visible and new solutions arise.
4.
WEIGHT ALTERNATIVES (Judgment)
DEFINITION: The ability to form an opinion on the basis of available information and experience.
PERFORMANCE INDICATORS
1.
Forms an opinion on the basis of information, arguments and considerations.
2.
Takes into account different perspectives / views and uncertainties / contradictions.
3.
Weighs available information and alternatives against each other in light of relevant criteria and treasures realistic applicability.
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4.
Oversees the possible consequences of his/ her position (advantages and disadvantages and performance risks).
5.
ADAPTABILITY
DEFINITION: The ability to (where necessary) conform to new situations and to release the original objectives.
PERFORMANCE INDICATORS
1.
If necessary, releases the original purposes.
2.
Adapts quickly under changing conditions / in a new work situation.
3.
Continues to function effectively under all circumstances.
4.
If necessary, subordinates own goal to a common goal.
C.
ATTITUDES
1.
Has a keen interest in data analytics and is inquisitive
2.
Is comfortable working with numbers, tables, algorithms and computers
3.
Is willing to work with various data processing techniques using various statistical packages
4.
Has willingness to be up to date with newest trends in big data techniques and technologies
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