As a data science enthusiast, I often find myself explaining what data science is and what it entails. At its core, data science is the field of study that involves extracting knowledge and insights from noisy data and turning those insights into actions that a business or organization can take. But what does that really mean, and what does it look like in practice?
Data science is an intersection of three different disciplines: computer science, mathematics, and business expertise. To truly succeed in data science initiatives, collaboration across all these areas is necessary. But what exactly does data science look like in action?
There are different types of data science, each with its own level of complexity and value. Descriptive analytics is the most basic form and involves answering questions like "What is happening in my business?" It involves accurate data collection to ensure that you know what's happening.
Diagnostic analytics is the next level and involves answering questions like "Why did something happen?" It involves drilling down to the root cause of a problem.
Predictive analytics is about answering questions like "What is likely to happen next?" It involves using historical patterns in data to predict outcomes in the future.
Finally, prescriptive analytics is about answering questions like "What do I need to do next?" It involves recommending the best action for a particular outcome.
Doing data science involves following a specific life cycle, starting with business understanding. This is critical to ensure that you're asking the right question before embarking on a lengthy data science initiative. This is where business and domain expertise can be incredibly valuable to ensure that you're asking the right questions.
Once you've defined the question, you can move on to data mining, which involves going out into your data landscape and procuring the data you need for analysis. Once you've done that, you can move on to data cleaning, which involves preparing and cleaning the data before it's ready for analysis.
After cleansing, you can move on to exploration, which involves using different analytical tools to help answer your questions. If you want to get into higher value questions like predictive and prescriptive, you'll need to start using advanced analytical tools such as machine learning tools that leverage massive amounts of computing power and high-quality data to make predictions and prescribe actions for the future.
Finally, you'll need to visualize your insights and outcomes of your analysis. In an organization, you may have roles like business analysts, data engineers, and data scientists. Business analysts are involved in formulating questions, visualizing insights, and have domain expertise. Data engineers help find, clean, and prepare data for analysis. Data scientists help with exploration, advanced machine learning techniques, and visualization.
In summary, data science is a complex and multidisciplinary field that involves extracting knowledge and insights from noisy data and turning those insights into actions. By following the data science life cycle and collaborating across disciplines, you can turn noisy data into knowledge, insights, and meaningful action for your business.