Data Science @Athena

Our analytics modeling and machine learning tools help enterprises adapt to data-driven ecosystem. We design, implement and integrate data science solutions within client’s business environment.

Why Data Science

Data science helps business to transform data into productive insights and then into goal-oriented actions. Statistical models which can predict behavioural outcomes can be created through hidden patterns in data. Transactional data has undeniable value which can be used for reporting or data analytics.

Why It Matters

The field data science has been rapidly growing and organizations across industries are keen on making sense of the available data. Organizations can mitigate risk, predict product relevance and enhance personalized customer experience through data analytics. Data science empowers management by directing actions based on trends.

Future with Data Science

The next-gen data analytics will rely heavily on domain specialization, thereby delivering solutions for target industry sectors. The current business analytical trends indicate that the platform strategy will soon shift from being a “one-stop, general purpose” to a domain-specific solution across all possible industries.

Data Science Capabilities

What We Do

We assist enterprises identify, analyze and resolve existing business problems. Our approach comes to aid in acquiring a deep knowledge on current big data trends and potential vendors.

We leverage the power of SCAD - (social media, cloud technologies, analytics and data science) to extract insights from unstructured data.

We identify client's product relevance at best and help deliver the right products at the right time. We understand client’s audience at a very granular level and hence create the best possible customer experiences.

Expert Services

We collect, store, curate data and apply suitable statistical methods to derive insights from it.

Athena's Data Analytics foretells the impending trends by conceiving actionable heights.

Our data savvy engineers help you leverage the new age data processing tools and data science technologies to understand unstructured and complex data sources. Our expert team holds experience in processing structured, semi-structured and also unstructured data stored.

Data Science

We build statistical models to predict the future actions based on past data. With predictive insights, we dig into the past so as to deliver the best forecast results. We establish data pipelines that feed the predictive analytics layer. We set up machine learning matrix structure for creation and execution of algorithms and APIs that incorporate with end channels.

Our predictive models use variable selection algorithms and best-practice, cross-validation methods for three major platforms - regression, machine learning and time series forecasting. Our methods include linear, stepwise and logistic regression, decision tree classifier, Random Forest, Adaboost, and Exponential etc.

Prescriptive analytics combines machine learning and other technologies to predict trends and consumer behavior. We believe that consumer-centric enterprises are in need of simpler charts and graphs for their decision-making capabilities. We take your analytics beyond predictive to prescriptive with our AI-powered solutions.

We build auto-regressive time series models to provide accurate and timely forecast of business outcomes. Time series forecasting is used in planning of sales, inventory, product pricing, promotion, and placement. We have built and deployed data forecasting models for the retail sector.

We are seasoned at building predictive maintenance models to help you get the most from your assets. Predictive maintenance solutions are designed to predict quality-related problems, thereby reducing equipment downtime and overall maintenance costs. We provide solutions that can also detect minor issues and determine patterns of failure thereby eliminating optimum damage. By identifying the underlying maintenance issues at the nascent stage, we enable clients to use resources cost-effectively.

We help you figure out -

  • Who are the potential customers for your products/services?
  • What do they want?
  • Why do they want your particular product?
  • How are the customers making their buying decisions?
  • How does your business go about doing this?

Our customer analytics model predicts future behavior. It ensures enterprises target prospects that will convert and identify additional products the customer might buy.


Our data science foundational methodology serves as a guiding strategy for clients to solve problems.

Our methodology is independent of particular technologies or tools. We provide a framework for proceeding with the methods and processes that will be used to obtain answers and results.

We have illustrated the 10 stages in our methodology leading from solution conception to solution deployment, feedback and refinement.

Innovation Lab

At our Data science lab, we enable our clients with research, prototype, and deploy the next-gen analytics solutions.

We take your enterprise beyond basic reporting and insights by enriching with data-driven products and taking these in production

We are your in-house incubator for experiments, active prototyping, and creation of data products, with the goal to empower your business with the ability to enhance data-driven products independent from consultancy services.

Our open model has collaborated with academia, customers and technology providers to innovate in those three areas -

Case Studies

Predicting loan default behavior among Bank's customers

  • We developed a machine learning based predictive model to predict the probability of loan default for each customer. Predicting loan default enhanced the lending operations and saved the bank from defaulters.

Forecasting demand for medicine orders from drug retailers

  • We developed a time-series based model to forecast demand for medicine orders from drug retailers over the next 2 years. The forecast comes to aid of drug manufacturers in identifying high volume retailers prioritizing their needs.

Classification of cancer patients for better treatment

  • We used cluster and descriptive analysis methods to classify cancer patients into high risk, medium, and low-risk groups, thereby providing doctor’s guidance for timely and tailored medical care for each patient.

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