The Center for Disease Control’s (CDC) National Center for Injury Prevention and Control (NCIP) is searching for data science training to help meet the organization’s Data Science Strategy.

In a sources sought notice on, NCIP said the training will be for its Division of Injury Prevention’s Data Analytics Branch, specifically the Data Science Team, and NCIP staff scientists. The CDC said the Data Science Team will be receiving advanced training, and the staff scientists receiving more general training.

The NCIP’s Division of Injury Prevention’s Data Analytics Branch (DAB) is tasked with conducting methodologic research and data analysis, addressing existing data issues and new data challenges (big, complex, non-traditional data), and developing and disseminating data, tools, and applications. The sources sought notice said DAB focuses on improving the availability, timeliness, quality, and usability of injury data to inform research and practice.

The Data Science Team within DAB consists of statisticians, computer scientists, programmers, and health scientists who use and promote data science for injury prevention and control. The Data Science Team is fairly new, having been established in January of 2020, and its goal is to increase NCIPC’s data science capabilities for injury and violence surveillance and research.

To meet its data science goals, NCIP said it needs to strengthen the data science workforce and advance information technology infrastructure. To strengthen its data science workforce, NCIP set a goal to improve “general knowledge and awareness of staff scientists in data science techniques.” Additionally, NCIP has set a goal to improve the advanced upskilling of the Data Science Team through advanced training and consultation.

The sources sought notice said NCIP is looking to obtain “services and deliverables that support training and consultation in general and advanced data science topics that will ensure the upskilling necessary to achieve NCIPC’s Data Science Strategy.” The period of performance will be 12 months and NCIP said the work will be performed fully virtually.

Among other deliverables, NCIP is requesting the contractor to provide 12 monthly seminar trainings on relevant data science topics. In the sources sought notice, NCIP said the topics can include broad topics with a general scientific audience of 30-90 people and should be less than two hours. NCIP also included a list of recommended topics:

  • Intro to Data Science,
  • Data Wrangling,
  • Machine learning Theory,
  • Supervised and unsupervised machine learning techniques,
  • Time Series Analysis,
  • Data analysis tools,
  • Regression analysis,
  • Cluster analysis,
  • Social Media analysis, and
  • Natural language processing.

For the advanced training portion of the notice, NCIP is requesting the contractor provide 12-16 trainings in advanced topics for 5-10 members of the Data Science Team and other data scientists. The source sought notice provided a list of topics the trainings need to cover:

  • Intermediate neural networks/deep learning: specifying custom layers and training,
  • Beyond feed forward neural networks: recurrent networks and convolutional networks,
  • Beyond feed forward neural networks: Generative neural networks, autoencoders,
  • Neural networks/word embeddings in public health data, transfer learning,
  • Introduction to Bayesian Inference,
  • Intermediate Bayesian Inference,
  • Quantifying potential outcomes with Markov Modeling approaches,
  • Bayesian approaches with Markov Modeling: conditioning Markov Modeling,
  • Approaches on observed data,
  • Introduction to causal inference methods in observational data,
  • Leveraging high-capacity models (machine learning) with causal inference to estimate treatment effects,
  • Intermediate / Advanced R and Python coding,
  • Ensemble Modeling,
  • Transformer Models, and
  • Time Series Analysis.
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Kate Polit
Kate Polit
Kate Polit is MeriTalk's Assistant Copy & Production Editor covering the intersection of government and technology.