Data Science & Machine Learning
Data Science and Machine Learning for Business Problems
Support for framing a business question as a data problem, exploring what the data can tell you, and building and evaluating models with sound methodology.
Challenges we can help with
When this kind of support is useful
Typical situations where Data Science & Machine Learning expertise makes a difference.
- Plenty of data, but no clear view of which factors drive an outcome
- A model that performed well in testing but disappoints in practice
- Forecasts produced manually with little visibility into accuracy
- Uncertainty about whether a problem is suitable for machine learning at all
Technical capabilities
What we can support
- 01
Exploratory analysis
Understanding distributions, relationships and data quality before modelling.
- 02
Statistics and trend analysis
Testing assumptions and quantifying patterns over time.
- 03
Predictive modelling
Classification, regression and forecasting approaches suited to the problem.
- 04
Model evaluation
Choosing appropriate metrics and validation to judge real-world performance.
- 05
ML workflows
Reproducible training, experiment tracking and handover of models.
Example use cases
Requirements we commonly see
- 1Exploring which customer attributes relate to churn
- 2Building a demand forecast and measuring its error against history
- 3Improving the evaluation approach for an existing classification model
- 4Setting up experiment tracking for a team training several models
Representative examples, not descriptions of specific client projects.
Tools and technologies
Examples of technologies professionals in our network work with.
- Python
- pandas
- NumPy
- scikit-learn
- Jupyter
- MLflow
Availability for a specific tool or version is confirmed for each requirement. Product names are used only to describe skills and do not imply any partnership with their owners.
What determines results
- Feasibility and outcomes depend on the quality and relevance of the data available.
- A well-defined business problem matters more than the choice of algorithm.
- Models should be judged on careful evaluation, not on a single headline accuracy figure.
How Corpolance may help
From requirement to the right specialist
- 1We discuss the business question, the data available and how success would be measured.
- 2We propose a data scientist with experience in similar problem types and tooling.
- 3You can explore their proposed approach — and its limits — before engagement.
FAQ
Data Science & Machine Learning questions
More answers are available on our FAQ page.
We can discuss your problem and data during the initial meeting. Sometimes a simpler analytical approach is more appropriate, and we will say so.
Related services
Often needed alongside
Tell us about your Data Science & Machine Learning requirement.
Share what you are working on and the technologies involved. We'll discuss the requirement and explain the next steps before anything is agreed.