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Data Science That Drives Decisions

Comprehensive data science services from pipeline architecture to model validation — reproducible, scalable, and scientifically sound

Turn Data Into Actionable Insights

Data science is more than just building models. It's about asking the right questions, designing rigorous experiments, and communicating insights that drive business decisions. We combine statistical rigor with modern ML techniques.

Data Science Services

Exploratory Data Analysis

Uncover hidden patterns, relationships, and insights in your data using statistical analysis and visualization techniques.

Statistical Modeling

Build rigorous statistical models for inference, hypothesis testing, and causal analysis with proper uncertainty quantification.

Data Pipeline Architecture

Design scalable ETL/ELT pipelines for data ingestion, transformation, and validation with Apache Airflow and modern data stack.

A/B Testing & Experimentation

Design controlled experiments, calculate sample sizes, and analyze results with proper statistical rigor and power analysis.

Causal Inference

Understand cause-and-effect relationships using propensity score matching, difference-in-differences, and instrumental variables.

Data Warehousing & BI

Build centralized data repositories and business intelligence dashboards for data-driven decision making.

Comprehensive Data Science Solutions

Data Strategy Consulting

Define data architecture, governance, and analytics roadmap

Data maturity assessment
Analytics roadmap
Tool selection

Advanced Analytics

Statistical modeling and predictive analytics

Regression analysis
Survival analysis
Bayesian modeling

Data Engineering

Build scalable data pipelines and infrastructure

ETL pipelines
Data quality monitoring
Cloud architecture

Reporting & Visualization

Interactive dashboards and executive reporting

Tableau/PowerBI dashboards
Custom visualizations
Automated reports

Scientific Data Science Methodology

1

Discovery

Understand business questions and data landscape

2

Data Collection

Gather, clean, and validate data sources

3

Exploration

Analyze patterns, distributions, and relationships

4

Modeling

Build statistical or ML models for insights

5

Validation

Test hypotheses and validate findings

6

Communication

Present results with visualizations and reports

Data Science Tools & Platforms

Languages

Python
R
SQL
Scala

Analysis

Pandas
NumPy
SciPy
Statsmodels

Visualization

Matplotlib
Seaborn
Plotly
Tableau

Data Engineering

Apache Spark
Airflow
dbt
Snowflake

Unlock the Value in Your Data

Let's build data science solutions that turn information into action.