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
Identify Specific Problem to Solve
Involve Stakeholders
Business Understanding
Select Analytic Approach
Descriptive ✱ What happened?
Diagnostic ✱ Why did it happen?
Predictive ✱ What will happen?
Prescriptive ✱ What action to take?
Collect
Collect Data
Software Engineering
Data Requirements, Collection, Mining, Exploration, Understanding, Cleaning, Preparation
Instrumentation, Logging, Sensors, External Data, User Generated Content
Process
Process Data
Data Engineering
Reliable Data Flow, Infrastructure, Pipelines, ETL (Explore, Transform, Load), Structured and Unstructured Data Storage
Cleaning, Wrangling, Anomaly Detection, Preparation
Label
Aggregate/Label Data
Data Science Analytics
Analytics, Metrics, Segments, Aggregates, Features, Training Data Preparation
A/B Testing, Experimentation
Model
Build Data Model
Machine Learning
Feature Engineering, Model Training, Evaluation, Deployment, Monitoring, Assessment, Optimization
AI, Deep Learning, Research Science
Report
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
Formal Report Structure
Cover Page
Title
Author Names
Date Published
Table of Contents / Outline
Executive Summary / Abstract
Introductory
Methodology
Results
Discussion
Conclusion/Recommendations
Acknowledgments
References
Appendicies