Future of the Technician Workforce Study

DATA ANALYTICS AND DATA SCIENCE TECHNICAL SKILLS General:

TOOLS Infrastructure & Computing Platforms:  Apache Hadoop, Apache Spark: open-source data storage, analysis, and com- puting framework  Cloud-based business intelli- gence (BI) platforms and IoT frameworks:  PaaS: Platform as a Service (i.e., Siemens Mind Apps)  SaaS: Software as a Service  Looker  Edge computing platforms  Scalable computing environments Data Libraries:  Deep learning libraries (i.e., PyTorch)  Machine learning libraries (i.e., Scikit-learn)  Neural network libraries (i.e., Keras) Software:  Deep Learning Frameworks (i.e., TensorFlow)  SQL query engines (i.e., PrestoDB)  Statistical analysis software (i.e., JMP [“jump”], Minitab, SAS Analytics)  Data visualization software (Tableau, Power BI, Crystal Reports) Programming Languages:  Python  R

 Basic & advanced math skills; basic statistics  Use of LabView and Python; cross-platform compatibility/interoperability  Python programming using open tool sets (i.e., TensorFlow)  Use of machine learning (ML) algorithms  Establishing ML evaluation metrics (accuracy, precision, recall, etc.)  Statistical analysis skills; Data interpretation and visualization  Data preprocessing techniques  Large data set management (i.e., data lakes)  Analysis and calibration of measurement systems  Software engineering  Graphic design  General understanding of business and trend analytics  Experience with cloud computing platforms  Knowledge of diverse data formats (text, audio, image, etc.) SOFT SKILLS General:  “Storytelling” and/or communication ability to translate quantitative data into tangible information and results  Interpretation and communication of data to stakeholders and among collaborators  Inquisitiveness; ability to ask the right questions  Understanding of the value of data to the business (i.e., whether data is kept or discarded following collection)  Judgment and decision-making ability to utilize and add meaning to the data  Quantitative reasoning abilities  Goal-oriented reasoning  Writing (summary/report of findings) and problem definition  Understanding of business processes

 Team dynamics/communication  Accuracy and attention to detail  Ethics Project Management & Process Improvement:  Risk mitigation  Six Sigma (Black belt, Green belt)  Project management method: Agile Method

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