Essentially, Data Science is an interdisciplinary field of study that encompasses the theory and application of methods used to generate, collect, store, process, analyze, visualize, model, and serve data, and insights or predictions. Additionally, it includes the orchestration and management of those processes and the people involved. Oftentimes, Data Science is described as the intersection between Computer Science, Math & Stats, and Domain Knowledge.

Top 9 Strategies for Preparing for Your Data Science Internship You've done the hard part: applied, interviewed, and landed a data science internship--Congratulations! This is a great accomplishment and opportunity. Now you are ready for some data science internship tips! Whether you have no professional work experience or you are pivoting into the [...]

Data Preparation is a key to success because having a clean, robust, well-selected training set will Prevent unintended biases in your dataset from poorly selected training data, It will Simplify the modeling process through the inclusion of well-thought-out feature design, Aid in reproducibility through a well-documented data cleaning/preparation pipeline, and Save you time in the future since your data will be well-prepped for future iterations and analysis. It will Increase stakeholders' confidence in your analysis because you have meticulously understood and accounted for the gaps in your dataset.

Phase 2 of CRISP-DM: Data Understanding, is foundational to our success in achieving the business goals established in phase 1. We need to document, clarify, revisit and share our findings from the data understanding phase. The work we do here sets us up for being able to deliver a high-quality model, API, dashboard, report, or whatever is the expected deliverable.

Let's discuss the first phase of CRISP-DM: Business Understanding. Recall that CRISP-DM stands for the "CRoss Industry Standard Process for Data Mining" and it's a six-phase process for organizing and iterating through a data project. Feel free to check out my previous posts where we discuss Why CRISP-DM is a Data Scientist's Secret Weapon [...]

CRISP-DM stands for the CRoss Industry Standard Process for Data Mining. It is a standard process for knowledge discovery consisting of 6 phases that can be applied across a wide range of applications. The 6 phases are Business Understanding, Data Understanding, Data Preparation, Modeling, Model Evaluation, and Deployment. The model was designed to be a high-level framework that included a strategy for mapping the generic process model to the specialized level.