Data science is a rapidly growing field, combining math, statistics, computer science and programming to leverage data with a sophisticated, modern approach. Data science typically will seek to solve advanced modelling problems, and use tools like machine learning and Artificial Intelligence to provide novel approaches to business problems.
The modern world is inundated with data, and in many cases the opportunities that come from converting this data to valuable information is wasted. There are three key aspects to consider for data science - design, collection and analysis. Design requires detailed consultation to understand what inputs are available and what the business goals are, this then informs the direction to take for data collection. The collection phase will likely require programming to extract and exchange data between APIs, or harvest and clean databases. For regularly analytical cycles, this may also involve building an automated data pipeline that lives in the cloud. And finally, where the science actually happens - analysis. At this stage models will be applied to convert the data into actionable insights, potentially identifying new patterns and highlight business opportunities.
Data science problems can be broken down into two categories - classification and regression. Classification problems predictively label something (eg Spam or Not Spam) while regression problems predict a numerical outcome by creating a mathematical function based on independent input variables (eg median house prices in 2026).
Successfully executing data science applications can bring massive advantage and boost profitability, but requires a holistic approach - talk to Wallace Corporation about harnessing your data today.
Data science turns raw information into actionable insights for management. Very few businesses leverage their internal data properly, the ones that embrace data science will have a competitive edge.
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