What is data science?

Data science is an integrative field that uses the scientific method alongside a range of tools to turn raw information into useful strategic or operational insights. 

 

Big data

Many data sets are too large to work with in Excel or other traditional data programs. These large data sets present both an opportunity and a challenge, as they require specialized big data approaches and tools to manage but can offer comparatively greater insights thanks to their extreme depth or breadth.

Big data is traditionally defined with several “V factors.”

Volume

The quantity of information when working with big data is beyond the scope of office-standard software tools to manage. Big data is often measured not in gigabytes or even terabytes, but rather in the much larger petabytes and exabytes.

Variety

Big data sets include multiple different types of information: video, text, geographic, and many others. 

Velocity

The information that forms big data sets is often continuously generated, pulled directly from sensors, software platforms, or APIs. 

Variability

Big data may or may not be accurate, since the issue of reliability can be magnified in data sets billions of points in size.

The data science process

The data science process is comprised of five steps designed to provide more accurate and comprehensive solution sets than traditional data analysis. Much of the process is automated, allowing for quicker results, lower costs, and more time to make the difficult decisions. Each step is related to traditional data analysis techniques such as CRISP-DM (Cross-Industry Standard Process for Data Mining) and KDD (Knowledge Discovery in Databases).

The data science process is comprised of five steps designed to provide more accurate and comprehensive solution sets than traditional data analysis. Much of the process is automated, allowing for quicker results, lower costs, and more time to make the difficult decisions. Each step is related to traditional data analysis techniques such as CRISP-DM (Cross-Industry Standard Process for Data Mining) and KDD (Knowledge Discovery in Databases).

1. Define the Problem

Asking the right questions is as important as getting the right answers. Our method begins by determining the precise nature of your problems and the data necessary to find for a solution.

Asking the right questions is as important as getting the right answers. Our method begins by determining the precise nature of your problems and the data necessary to find for a solution.

2. Gather the Data

Setting up the right data architecture can save time and confusion during the data science process. The data “pipeline” developed in this step provides the backbone for deployable models to ensure your data works on your schedule.

Setting up the right data architecture can save time and confusion during the data science process. The data “pipeline” developed in this step provides the backbone for deployable models to ensure your data works on your schedule.

3. Prepare the Data

“Data cleansing” can often be the most time-consuming step in the data science process. However, proprietary algorithms speed up the process while providing introductory analyses to provide insights immediately and solutions quickly.

“Data cleansing” can often be the most time-consuming step in the data science process. However, proprietary algorithms speed up the process while providing introductory analyses to provide insights immediately and solutions quickly.

4a. Model the Data

Rather than focusing on convoluted algorithms that aim to impress through incomprehensibility, the Synergistic Method™ uses the right model for the right problem. Regardless of complexity, our models are sophisticated, functional, and accessible.

Rather than focusing on convoluted algorithms that aim to impress through incomprehensibility, the Synergistic Method™ uses the right model for the right problem. Regardless of complexity, our models are sophisticated, functional, and accessible.

4b. Validate the Model

Stylish graphs are nice, but accurate models are better. Validating models provides the rigor to determine the efficacy of different approaches as well as provide alternate viewpoints for a more comprehensive perspective.

Stylish graphs are nice, but accurate models are better. Validating models provides the rigor to determine the efficacy of different approaches as well as provide alternate viewpoints for a more comprehensive perspective.

5. Deploy the Model

The result is a custom product ready for immediate deployment on your website, a dashboard, or in the line of business. Models are built to improve over time, and monitoring key metrics ensures the system, and your business, stay healthy.

The result is a custom product ready for immediate deployment on your website, a dashboard, or in the line of business. Models are built to improve over time, and monitoring key metrics ensures the system, and your business, stay healthy.