Data Science Consulting
Asesoría

Data Science Consulting

We turn your data into decisions. From dashboards and business intelligence to predictive models and process automation, we build analytics solutions around the questions your organization actually needs to answer. Whether your data lives in spreadsheets, databases, or scattered across systems, we clean it, model it, and make it useful — with the statistical rigor of someone who works with official data, not guesswork.

Back to services
+6
Years of experience
MSc
Applied Statistics
Official
Statistical methodology
R · Python
Power BI
USAC
University teaching
Audit
Data that withstands scrutiny

We turn your data into decisions. From dashboards and business intelligence to predictive models and process automation, we build analytics solutions around the questions your organization actually needs to answer. Whether your data lives in spreadsheets, databases, or scattered across systems, we clean it, model it, and make it useful — with the statistical rigor of someone who works with official data, not guesswork.

Companies with data they aren't using to make decisions

Businesses that need dashboards and clear reporting

Organizations looking to predict demand, churn, or risk

Teams drowning in spreadsheets and manual processes

Results

Interactive dashboards that answer your key questions

Clean, organized, and documented data

Predictive models built and validated on your data

Automated processes that save your team hours

Analysis you can trust and defend

Sample · Illustrative data

This is what your data could look like

We transform the complexity of your data into rigorous analysis that reveals the key insights for your thesis, market research, project, or business. We provide the clarity you need to interpret results, predict trends, and prepare to make decisions or support findings with solid evidence. We have the perfect solution for you.

Monthly trend
Distribution by segment
0%
Model accuracy
0
Customer segments
Q0M
Projected revenue
0%
Explained variance (R²)
Customer segmentation (k-means)

Grouping by spend and frequency

Prediction (regression)

Fitted model over observed data

Variable importance (random forest)

Which factors drive the outcome