What Are the Foundations of Data Science?

If you run a business, you know and appreciate the power of adaptability. It would be best if you were ready to change your vision or process to help your company succeed. When turning a profit and gaining revenue is your number one priority, you analyze the best way to make that happen. And the best way to get all of that information is with data analysis and visualization.

Data science is a complex process of deep learning and breaking down big data into manageable and understandable information pieces. By definition, data science is a multidisciplinary way to discover, analyze, and utilize data. It provides great insights into your company by collecting analytics from data mining, forecasting, machine learning, and other statistical methods.

Data managers take information from all these different areas and help business users leverage that information to predict the company’s future and problem-solving. This is a multi-step process that involves plenty of deliberation and predictive analytics. Let’s dive into the foundations of data science and the exact process these data scientists undergo to process the correct data.

Data Science

Seeing a Problem

The first step in data science is to find the problem you are trying to solve. Maybe your business needs to grow your sales, or you’re looking to expand your marketing reach. Whatever that may be, your data scientists can then break down your goals to see what smaller steps need to be taken to improve the company. This stage is similar to the beginning of the scientific process. You see an issue and create a hypothesis for how you can solve it. And once you’ve come up with a plan of action, you test it out to see if you get the desired results.

Gathering Raw Data

Following the scientific method structure, the next step in your data science journey is to gather the raw data. This may involve going through a lot of unstructured data and creating a visualization that can be easily understood. Once you break down the big data, you can observe trends and decide how to proceed.

Analyzing the Data

Getting your data together in one centralized location allows you to analyze the trends. Maybe you want to create charts and graphs of niche markets or take a look at times where sales were increasing. By knowing your historical data, you can analyze the big data and find a way to recreate past successes. This also opens the door for data modeling. Exploring current data allows you to twist and adjust the trajectory of your future data. Learn how to bend things the way you want them and know how to create a successful model to give you your desired outcome down the line.

Create and Test Models

Once your current data is gathered and analyzed, it’s time to start creating new models and algorithms. Machine learning or forecasting can help you develop your own statistical model. You then use this algorithm with your current data set to see if you can target a certain variable to find a new outcome. This outputs predictions or optimizations that can help you make more informed, predictive business decisions. Having a model in place helps you make strategic choices to grow your company and fix that specific problem.

Monitor Results

Because everything in tech is constantly changing and adjusting, you need to be monitoring your models. Continue testing, refreshing, and governing your models as real-world events impact your business. Data scientists aren’t done simply because the data science program is complete. It would help if you stayed on top of your ever-changing business narrative. Your data science should be equally as adaptable as the world around you.