We use data to make the impossible, finally possible

Keiter now provides high-quality data science and analytical services to bridge the gap between collecting and utilizing data to make the impossible, finally possible.

At Keiter, we take a holistic approach to data science by planning, testing, and building solutions in phases that meet your needs. This allows us to understand the challenges that truly need to be solved and efficiencies that can be gained.  When working with us, you will notice how passionate we are about data and collaborating with you to meet your goals. Our desire for solving problems inspires us to continue learning and staying updated on the newest technologies and the applications that can reach across industries.

We are here to help you turn data into decision making resources. Contact our team to see how we may be able to assist with your innovative data solutions.

Data Solutions Services

A Data Science Roadmap is a treasure map for unlocking hidden insights from within your data.

It’s a step-by-step timeline to navigate through the complex process of turning data into dollars, Whether it’s data cleaning or analysis and interpretation.

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Research Support is what we call when you have our team of expert advisors on your side to guide you through your research journey.

We can help with everything from research design and data collection to statistical analysis and interpretation.

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Unstructured Data Cleaning is a valuable way to transform unstructured data into valuable insights.

With it we can uncover hidden patterns in customer feedback, social media sentiment, and even legal documents.

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Data Visualization is a way to create visual representations of data to help users understand and analyze it more effectively.

With data visualization, we can communicate complex ideas and insights in a way that’s easy to understand, from business intelligence to scientific research.

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Time Series Analysis is a way to analyze and model data that varies over time, and use it to forecast the future.

We can make accurate forecasts that can help your company make better decisions in your industry.

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Prescriptive Analysis can anticipate how the real world would react to potential scenarios.

It’s a way to test varying models of complex systems and scenarios without risking real-world consequences.

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Data Solutions Algorithms

Simply put, Computer Vision gives computers the ability to see and understand the world around us.

It’s the technology behind self-driving cars, facial recognition software, and even image generation.

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Natural Language Processing is a technique used to interact with and interpret human language by breaking it down into small pieces

It powers virtual assistants including Apple’s Siri, Amazon’s Alexa, chat bots like OpenAI’s ChatGPT, language translation services and social media sentiment analysis.

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Data Simulation uses real world data to create new, simulated data that can be analyzed without compromising data security and privacy. Simulated data is also used to represent real-world scenarios to predict potential outcomes under different conditions.

Data simulation is the backbone of complex systems modeling like weather forecasting, in which weather and climate patterns are simulated to predict the weather, and stock market predictions, in which potential stock trends are explored to predict both risks and payoffs.

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Predictive Modeling uses statistical algorithms and machine learning techniques to predict future outcomes based on historical data. Predictive modeling powers many applications in everyday life including recommendations for shows on streaming services and posts on social media, and estimated travel time in your go-to maps app. 

With predictive modeling, we can anticipate customer churn, resource utilization, and even predict disease outbreaks.

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Mathematical Optimization is the process of maximizing or minimizing some variable(s), such as costs or profits, in the presence of constraints, such as available inventory and equipment. Optimization is used in various applications in finance, engineering, machine learning and artificial intelligence (AI), as well as in many business and planning processes. 

With optimization, we can maximize profits and minimize costs, optimize resource allocation in fields like logistics and finance, and investment portfolio planning. 

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Bayesian Statistics is the application of probability to statistical problems. Using beliefs about the probability of events, called prior probabilities, new evidence – data – is used to update those beliefs to develop what are called posterior beliefs. These posterior beliefs incorporate subjective beliefs and observed data to provide a flexible framework for analysis and decision making. 

Bayesian techniques are often utilized in cases where the objective is assessing risk or uncertainty rather than specific predictions, and are used in financial forecasting, medical diagnostics, marketing and product development, fraud detection, and risk management.  

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Classical Statistics is the traditional method of statistical analysis, also known as frequentist statistics, including hypothesis testing, confidence intervals, and regression analysis. 

With classical statistics, we can analyze data and make decisions with measurable confidence in applications from social sciences to engineering. 

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