Data Science

Use predictive analytics and machine learning to accelerate digital transformation

What is Data Science?

Data science is a hybrid skill set that combines mathematics, software engineering, and business analytics. Data science relies heavily on the rapidly advancing fields of machine learning (ML) and artificial intelligence (AI). These new technologies enable algorithms and models to learn from historical data in order to predict the future, identify trends or groups, and detect anomalies.

By applying modern statistical methods and ML to historical datasets, data scientists can:

The most successful data science projects start with small objectives and develop iteratively based on intermediate findings. This agile approach can exponentially add value by compounding upon success. Simple results can also help catalyze new ideas and applications for business transformation.

Successful outcomes also depend on experience and best practices in ML engineering and MLOps. Without a strong pipeline for deploying ML models, many innovations fail to reach an operational state.  Surrounding data science teams with savvy engineering and operations teams is important to make sure your models make it to production rather than dying in PowerPoint.

Machine Learning Process

The Machine Learning Lifecycle

What can you do with help from phData’s experienced data scientists?

What does a typical data science engagement look like?

Every data science project looks a little bit different, since no two organizations are the same in how they treat data and analytics. Our data science services are tailored to help your company at any stage of data science adoption.

What are some typical data science project deliverables? 

What is typically considered out of scope?

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Questions about data science? We've got answers.

Data scientists apply skills from software engineering, mathematics, and statistics to complex business problems. Typically, a data scientist will start by exploring available data using a programming language like Python or R to uncover trends and validate assumptions.  Data is then used to build statistical models with machine learning or mine for patterns with other AI technologies. These models and patterns can then be used to augment business processes through automation or decision support.

First and foremost, data science requires good data. Good data depends on robust data pipelines and good metadata systems to know how data was collected in the first place. In addition to data, organizations should be ready for disruption that may occur as processes are automated and decisions are made with new information uncovered through data. For more information, see our guide on building a data-driven culture within your organization.

Every organization has unique needs and projects can be scoped to fit. Some organizations start with a simple conceptual engagement to develop use cases; these can last just a few weeks and range from $25k-$50k. In other cases, we’ve seen organizations with robust datasets and operations who are ready to develop sophisticated models and applications. Larger engagements could cost upwards of $300k.

How our data scientists can help lead data-driven business transformation

  • Quicker time to value

    Research is an iterative process. We structure projects around practical milestones to provide incremental value that builds upon itself. Rather than committing to promises we can’t keep, we start by uncovering robust information from data and adjusting our plans to match reality.

  • Demonstrated experience

    phData brings experienced data scientists who are ready to engage with complex problems. We don’t waste time exploring new tools at our customer’s expense, but instead align experienced resources with compatible problems. 

  • Proven best practices

    Without best practices for software and research, data science can lead to erroneous or unreproducible results. We focus on using the right tools and techniques to make sure we don’t produce bogus results or lose track of what we’ve developed. By integrating with MLOps tools, we can make sure that models are developed the right way the first time, and ready to be operationalized as soon as performance is sufficient.

  • Support for the entire ML lifecycle

    phData also specializes in MLOps, ML engineering, and data engineering. We won’t just develop an ML model and leave your organization to figure out how to deploy or operationalize it. We can support the ML model lifecycle from development through deployment and operations, and then close the loop to help improve or develop models.

Want to learn more about data science? We’ve got you covered.

Bayesian Hyperparameter Optimization with MLflow

Discover how to use a model registry to enable reproducible research, even for complex tasks like hyperparameter optimization.

Beyond Data Science: Building Culture and Infrastructure

This guide explores how companies can invest in a data-driven future by empowering data scientists to deliver quality work.

What is AutoML and is it Right For You?

Explore this insightful overview of emerging AutoML technologies and why they won’t eliminate the necessity for data scientists.

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