How would we start
a data science project?
When we start a data science project, we provide a structured framework for analyzing and modeling data to ensure that your project is completed efficiently and effectively.
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We clearly define the problem you are facing, create a starting solution and evaluate it.
This will help you to determine the objectives and goals of your project and will guide the rest of the process.
What does that look like?
Steps
1. Business Understanding
We start by gaining an understanding of the problem that you are trying to solve, as well as the objectives and constraints of the project.
2. Data Understanding
Next, we will need to gather and understand the data that will be used for our project. This may involve collecting new data, or accessing existing data sources. It's important to explore the data to get a better understanding of its quality and relevance to your problem.
3. Data Preparation
This step involves cleaning and preprocessing the data, in order to make it ready for analysis. This can include tasks such as handling missing or duplicate data, scaling numeric values, and creating new features from existing data.
4. Modelling
Once you have a good understanding of the data, you can start the modeling phase. This is where we use statistical and machine learning techniques to build a model that can make predictions, find patterns in, and draw insights from the data
5. Evaluation
After we have developed a model, it's important to evaluate its performance to see how well it solves the problem. This will typically involve testing the models on new data, and comparing their results to the expected outcomes in order to determine whether it is effective at solving the problem.
Finally, you can use the insights and predictions from your models to take action. This may involve deploying a solution to the problem you set out to solve, or it could involve making recommendations for further study.
6. Deployment
This step involves deploying the model in a production environment, where it can be used to make predictions or find patterns in new data.