Data Science

Data Science Consulting

For over 25 years, Fulcrum Analytics has been a leading provider of data science, advanced analytics, and data engineering. Our experienced team of on-shore consultants work closely with client end-users and stakeholders to provide:

  • Strategy development
  • Data assessment
  • Data augmentation, integration, and maintenance
  • Predictive modeling and statistical inference
  • Supervised deep learning with neural networks
  • Generalized linear models
  • Machine learning modeling
  • Cluster analysis
  • Principal component analysis
  • Classification models
  • Data visualization
  • Time series modeling
  • Forecasting
  • Natural Language Processing (NLP)
  • HR analytics
  • Share of wallet analysis
  • Risk analytics
  • Loss driver analysis
  • Unstructured data mining
  • Big data programming
  • Data anomaly detection
  • Management dashboards
  • Recommendation systems
  • Website analytics

We work across industries

We can help by deploying our resources on a per-project basis or with a consulting team-based approach.


Fulcrum's Data Science Acceleration Team

Our Data Science Acceleration Team (DSAT) 2.5 is great for any business looking to ramp up their data capabilities. A DSAT 2.5 package comes equipped with a team of interchangeable specialists fitted to your needs. To optimize your engagement outcomes, a mix of data scientists, engineers, developers and the project lead will work the equivalent of two and a half full time resources. Whether you are a small startup with no data science team or a multi-billion dollar financial institution with more projects than the bandwidth you have to support, DSAT 2.5 can help move you forward.

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Our staff is trained in a wide range of programming languages, methodologies and frameworks including:

  • Python: Pandas, NumPy, Scikit-learn, Matplotlib, PyTorch, Keras
  • R: tidyverse, Shiny  
  • SAS: Base, EG, STAT, GRAPH, IML  
  • SQL: Hive SQL, PL/SQL, MySQL, PostgreSQL, Impala
  • Data visualization: R Shiny, Superset, Dash-Plotly
  • Distributed computing: Cloudera Hadoop,Druid, Spark, Hive, Impala, HBase, Kafka
  • Container/Virtualization: Docker, Kubernetes, OpenStack
  • Development: Agile methodology, Git, Atlassian (Bitbucket, JIRA, etc.), Jupyter, R-Studio, Unit testing,  Modular, Object Oriented design principles  
  • Web development: Flask, Django, Javascript, Bootstrap, and D3.js, API development

Recent Thoughts on Data Science

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Machine Learning and mathematical rigor are tools at data scientists' disposal. But we all know that having a better calculator Read more
Evolving past local Jupyter Notebooks to Autonomous Systems: Industry Data Science has diverged into a few dominant verticals as the Read more
Jupyter Notebook has become the de facto standard for data analysis. An acronym of the three main coding languages it Read more

How we have helped clients

A startup needed to scale up their data science and engineering capabilities in short order. They used DSAT 2.5 for our deep financial industry expertise and advanced data engineering skills. We provided them with the right mix of technical and domain knowledge to rapidly accelerate their client service.

A global investment bank uses DSAT 2.5 to modernize tagging, tracking and reporting for their research portal. Also, we are creating a test and learn framework to enable more efficient experimentation and comprehensive feedback.

A retailer wanted to understand the attractiveness of their local markets and how they fared against their competitors. This retailer used DSAT to automate the generation of custom detailed reports, comprised of an (automatic) aggregation and analysis of various public data sets, and develop an interactive application allowing business users to create further customizable charts and reports.

To learn more about ways we have aided our clients with data science please check out our case studies