Transform data
into value

Data Science and Analytics

Data Science vs. Analytics...
How do they differ?

While closely related, data analytics is a component of data science, used to understand what an organization’s data looks like. Meanwhile, data science takes the output of analytics to solve problems.

Both allow us to obtain valuable information from data sources. This in turn, empowers organizations with insights to make quicker and informed decisions.

Unleash the benefits

  • Extract business-focused insights from data

  • Identify key data assets, transform them into data pipelines that feed tools and solutions

  • Identify new business opportunities

  • Prevent critical failures and disruptions across business operations and systems

  • Enable planning through reduced uncertainty and better quantified risk

The applications of big data and analytics are near-endless with many benefits for businesses.

What can Data Science &
Analytics be used for?

  • Anomaly detection (fraud, etc.)

  • Automation and decision-making (background checks, credit worthiness, etc.)

  • Classifications (categories and filters, etc.)

  • Forecasting (sales, revenue and customer retention)

  • Pattern detection (financial market patterns, etc.)

  • Recognition (facial, voice, text, etc.)

  • Recommendations (based on learned preferences)



Three Phase Approach

Understand the business, identify a use case and define the goal

Obtain the data, clean the data, extract data components and data sets

Build Visualizations, get predictive with algorithms and iterate

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