additional Packages

 

Data Science & Applied Machine Learning

 

 

 

Messy data?
No problem.

Whether you’re collecting sensor streams, customer records, or documents in different formats, our engineers build robust pipelines that clean, validate, and normalize your data, so it’s ready for analysis or machine learning.

Insight beyond dashboards.

Our data analysis and feature engineering sprint helps uncover patterns, identify anomalies, and create model-ready features, all rooted in statistical rigor and domain expertise.

Ready to model?
We don’t stop at notebooks.

We help you move from prototypes to production - building and tuning predictive models, handling missing data, and deploying real-time or batch pipelines that support smarter decisions.

 

Whether you need clean, reliable datasets, sharp analytical insights, or full-scale machine learning models, we tailor every project to your business goals and technical reality.

Ask us how our flexible, end-to-end data science services can deliver exactly what your organization needs — no more, no less.


 

 


Data to Insights:
A Journey Through Data Processing

Data Collection & Cleansing

2–4 weeks

Data Analysis & Feature Engineering

3–5 weeks

Machine Learning & Predictive Modeling

4–8 weeks

 

 

 

 

1 Data Collection & Cleansing

 

Duration: 2–4 weeks

Target: Organizations aiming to prepare their data for further analysis and modeling.

Includes:

  • Design and implementation of data pipelines for structured and unstructured data.
    Data ingestion from internal systems and external sources.
  • Standardization and normalization of data formats.
  • Identification and flagging of missing values, deduplication, and consistency enforcement.
  • Detection and flagging of invalid or extreme values using domain-specific rules or basic statistical heuristics (e.g., impossible values, sensor glitches, obvious entry errors).
  • Basic statistical imputations where necessary to ensure continuity in data processing.

Deliverables: 

  • Automated data ingestion and cleansing pipelines.
  • Cleaned datasets ready for downstream tasks.
  • Documentation of preprocessing procedures.

 

 

2 Data Analysis & Feature Engineering

 

Duration: 3–5 weeks

Target: Teams seeking to extract meaningful insights and prepare enriched features for modeling.

Includes:

  • Exploratory Data Analysis: distribution analysis, trends, and patterns.
  • Correlation analysis: Pearson, Spearman, and non-linear dependencies (e.g., Mutual Information, Distance Correlation, MIC).
  • Anomaly detection using statistical and unsupervised methods (e.g., Isolation Forests, Autoencoders, clustering-based methods).
  • Feature engineering and preliminary feature selection using statistical and unsupervised techniques such as mutual information, variance thresholding, and exploratory dimensionality reduction (e.g., PCA).
  • Domain-driven information extraction (e.g., key phrases or tags, named entities).
  • Rule-based or statistical imputations for complex missing data scenarios.

Deliverables:

  • Analytical reports and visual insights.
  • Feature sets and metadata documentation.
  • Configurable anomaly detection modules.

 

3 Machine Learning & Predictive Modeling

Data Science

 

Duration: 4–8 weeks

Target: Organizations ready to apply ML models for prediction and decision support.

Includes:

  • Development of classification and regression models, from interpretable baselines to advanced ensemble and deep learning approaches (e.g., logistic regression, gradient boosting, neural networks, transformers).
  • Handling of missing data using model-based approaches (e.g., KNN imputation, ML estimators).
  • Sentiment analysis using NLP techniques (e.g., transformers, classical pipelines).
  • Time series forecasting models: ARIMA, Prophet, LSTM, TCN, TFT, etc.
  • Advanced feature selection and dimensionality reduction as part of the modeling pipeline (e.g., Recursive Feature Elimination, LASSO, PCA, Kernel PCA, NMF, Autoencoders).
  • Model selection, tuning, training and evaluation.
  • Integration into operational pipelines or existing infrastructure.

Deliverables: 

  • Prototypes and performance benchmarks.
  • Deployed or deployable models.
  • Dashboards for monitoring predictions and KPIs.