About Clark Data Science

Data Analytics • Data Infrastructure • System Organization

I offer freelance data analytics, data infrastructure, and system organization services for scientific labs, engineering groups, small businesses, and individuals.

These include:
• Exploratory data analysis to characterize data already collected
• Statistical analysis of data to discover valuable insights
• Data visualization creation to communicate findings efficiently
• Data strategy design for organizations, systems, processes, and projects
• Data pipeline cleaning so that any analysis done later works with clean, verified, valid, well-organized, and properly labeled data
• and many other data related services

The Goal

My aim is to support scientific research, industry, small businesses and individuals with data related projects by providing high-quality services.

These include initial data infrastructure builds, data analysis of current processes, and integrating a formal data strategy into organizational processes to significantly improve transparency, enhance performance, increase reliabilty, and ultimately deliver better value to customers.

I want to help people improve systems, attract, train, and retain talent, achieve excellence, and build towards a better tomorrow.

Great things are on the horizon. The future is bright!

Jonathan M Clark

My interest in data science stems from my experience creating order in chaotic systems by working at the interface between data (the data layer) and operations (the decision making layer).

I have a talent for organizing processes so that they operate smoothly, a drive to understand how things work, a deep love for solving problems, an aptitude for applied mathematics, and an interest in computer science.

I am inquisitive, curious, and eager to take on new projects, learn new things, and try innovative approaches to deliver quick, responsive, high quality, custom services.

Data science fits the bill for me quite nicely and neatly.

Now, What Exactly is Data Science?

Data Science is the umbrella term for the field of study that combines the technical capacities of computer science, mathematics (especially statistics and linear algebra), and domain-specific expertise to extract useful insights from raw data. Data Science utilizes analytical and scientific methods to extract information from data to solve real world problems. The primary output of the data science field is the generation of insights that enable decision makers to be better informed, make better decisions, be armed with more accurate and precise intelligence, and thus, to tackle real challenges and solve real problems more effectively. The goal is to creatively use technology to discover and use information to drive operational performance improvements.

Data Science is a broad field that includes business intelligence, statistics, data analytics, machine learning, and artificial intelligence. The effectiveness of data science is derived from the combination of advanced software tools, robust methods, and the massive compute power available today. Data science applications can be found in every industry and in everything from small data sets to large, complex, high velocity data streams.

A Data Science Project Workflow


Identify Specific Problem to Solve

Involve Stakeholders

Business Understanding

Select Analytic Approach

DescriptiveWhat happened?

DiagnosticWhy did it happen?

PredictiveWhat will happen?

PrescriptiveWhat action to take?


Collect Data

Software Engineering

Data Requirements, Collection, Mining, Exploration, Understanding, Cleaning, Preparation

Instrumentation, Logging, Sensors, External Data, User Generated Content


Process Data

Data Engineering

Reliable Data Flow, Infrastructure, Pipelines, ETL (Explore, Transform, Load), Structured and Unstructured Data Storage

Cleaning, Wrangling, Anomaly Detection, Preparation


Aggregate/Label Data

Data Science Analytics

Analytics, Metrics, Segments, Aggregates, Features, Training Data Preparation

A/B Testing, Experimentation


Build Data Model

Machine Learning

Feature Engineering, Model Training, Evaluation, Deployment, Monitoring, Assessment, Optimization

AI, Deep Learning, Research Science


Report to Stakeholders

Data Visualization, Executive Summary, Detailed Analysis/Conclusions, Storytelling with Data

Choose Format Option: Formal report, Live/interactive dashboard, Minimum-viable-product on-the-fly quick-n-dirty one sheet summary