Multiparadigm Data Science: 5 Things You Need to Know
Difficult problems require sophisticated solutions for business, industry, and organizations using data to drive decisions. New challenges and new demands across the enterprise require cutting edge solutions to remain competitive and innovative in today’s marketplace. Multiparadigm data science is one such solution that uses modern analytical techniques, automation, and human-data interfaces to arrive at better answers with flexibility and scale, whether it’s automated machine learning and report generation, natural language queries of data for instant visualizations, or implementing neural networks with ease and efficiency.
What Makes Data Science Multiparadigm?
Data science is a constantly evolving field. Here are five key points to get you on the road to better understanding a multiparadigm approach to taking data science to new levels of analysis and understanding.
- Harnessing the full power of computation. Traditional data science techniques are rooted in statistical analysis, hypothesis testing, and a range of regression techniques that limit the questions that can be asked of your data. However, as data become more complex, and with new techniques developed at breakneck speed, it is often the case that more advanced means of solving problems through automation exist but are underutilized, including advanced machine learning, signal processing, computer vision, and neural networks.
- A process led by questions rather than techniques. Using automated machine learning, natural language queries, and triggered reporting frees up time and resources. Having a vast range of algorithms, models, optimization, and domain expertise built in to your system gives you the ability to ask more questions of your data in a beginning-to-end workflow in one environment.
- Fully customizable reporting. Different parts of organizations require different reports from data analysis. Having the ability to generate meaningful reports across the enterprise with automated visualization and dynamic content gives everyone from the C-Suite to the sales team access to the tools they need to make informed decisions and forecasts.
- Natural language understanding. Advances in semantic search and natural language processing are only the beginning. Document tagging and probabilistic text queries are useful, but in order to fully unleash the power of your data, the ability for anyone across an organization to use free-form input to answer a question from your data requires a system built to understand human input with confidence.
- Multiple types of data and computation. Real-world data is messy and requires a system that can intuitively process, model, and visualize everything from textual data to images beyond dataframes of strings and numerical data.
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