You'll hear the term "data science" get tossed around a lot, because it encompasses so many disciplines and activities, from machine learning to statistical modeling and analysis to data visualization. But what's important is what it can do for you: reveal the insights you need to take decisive action and achieve the results you need in our increasingly competitive data-driven world.
In data science, we use statistical methods, algorithms, and knowledge of computer information systems to get insights from data.
We get to decide how to use those insights to generate value and make decisions that drive business growth.
Here are just a few practical applications for data science:
- Search engines use data science algorithms to bring us the best, most relevant results for our searches within fractions of seconds
- Fraud and risk detection in the insurance and finance industries - client profiling, past expenditures and other factors allow analysts to predict probabilities of risk and default
- Delivery logistics
Data science also has many practical uses in marketing, health care, government, and more.
Businesses and organizations of all sizes are deeply immersed in data every day. You might even feel like you're being buried in it!
But there's opportunity hidden in all those spreadsheets, survey responses, phone calls and documents. The valuable insights you gain from a data-backed analysis can help you:
- understand what factors drive your business' success
- free up your own and employees' capacity for more high-value work
- get the highest possible return on your investment for any measurable activity, from marketing to business development
Whether you're an employee or entrepreneur, you're now competing in a data-driven environment on a global scale. You're under pressure to operate more efficiently and justify strategic direction to stakeholders. Using data science means you'll have the ability to predict the outcome of your hard work and achieve the results you need - no more guessing!
The success of any project depends on having an experienced, highly skilled partner, a strategy customized to your needs, and clean data to work with.
There's a critical difference between techniques that are data-oriented and those that aren't. While performance engineering involves data and statistics, it can be done without either. Data is an intrinsic and essential part of data science projects, which always have data pipeline requirements.
These two disciplines require drastically different skillsets. You need a partner who knows which one applies to your problem, and how to correctly build and implement a solid, cost-effective solution the first time.
We excel in this area, combining a strong background in mathematics with elite software development skills to solve complex problems and rigorously analyze data. When you work with us, we custom develop a precisely planned approach to address your specific challenge, then work with you to implement the solution.
Related skills: Process automation often enables (or blocks us from introducing) better data science opportunities.