UNIQUE DATA SCIENCE PROJECTS IN MULTIPLE DOMAINS
The use of proper data collection methodolgies and systems is crucial for any data science project. Without accurate data the statistical or machine learning results become biased and unreliable. Also, it is just as important to train staff memebers for the procedures and explain the purpose of data collection. It is too easy to become distracted by the prospect of a sale, conflicting personalities within teams, or financials that have gone in the red. With proper training and implementation of data collection business intelligence and insight are accurate and precise. In fact, most professionals in the field would agree that this is the hardest, but most important part of the data pipeline.
- Data collection methodologies & systems
- Data collection software
- Procedures training
Data Storage & Warehousing
Large amounts of data can create bottlenecks in the data pipeline, and malicious actors can create significant downtimes. Therefore, data storage needs to be efficient and secure. There are a diverse group of tools available to accomodate any business structure to keep the data moving and to keep it safe. Additionally, data warehousing is necessary for businesses interested in using data in the long-term to aid in the decision making process.
- Database design & implementation
- Data warehouse design & implementation
Data analysis helps businesses keep a quantitative focus. It is not a substitute for strong teams, productivity, or products. Instead it can help to identify positive or negative trends and relationships to support the decision making process. Data analysis is a business tool, in addition to business experimentation, that will provide the insight for better decision making.
- Exploratory Analysis
- Statistical Analysis
- Machine Learning Analysis
- Deep Learning Analysis
Decision Making & Consulting
The decision making process is a collaborative process where quantitative analysis is used to inform business decisions. This is a highly nuanced process where business knowledge is necessary, but so is domain knowledge. Domain knowledge is knowledge of the industry the business is in. In addition to domain knowledge, an understanding of societal driven factors is crucial. The multi-disciplinary and intra-disciplinary nature of this process is why collaboration between business decision makers, domain experts, data scientists, and social scientists is required.