Data Science Life Cycle

Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. We will see how a typical data science project is expected to be executed in across different enterprises – big and small. First, let us understand what data science means? Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining and big data. Now, that we have that squared away, let us dive right in and understand what a data science project entails.

To understand that, we first need to talk a bit about CRISP-DM, .i.e, Cross Industry Standards and Procedures for Data Mining. The current data science project lifecycle draws heavily from this framework.

The CRISM-DM framework as shown in the picture is cyclical and iterative. It consists of the following steps:

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment

In the next video we will explore each of these phases in detail.
Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. In the previous video we were introduced to the data science lifecycle. Now let us discuss about the 1st phase in the data science lifecycle – Business Understanding.

Business Understanding: Arguably one of the most critical phases of the data science life cycle.
Albert Einstein once said, “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it.” A well-defined problem often contains its own solution within it, and that solution is usually quite obvious and straightforward. By defining problems properly, you make them easier to solve, which means saving time, money and resources.

During this phase, the data scientist will have to talk to the stakeholders, domain experts and get a clear understanding of the issue at hand. Most data science projects fail as the expectations are not clearly formulated. So, it is critical that the business problem be defined in the clearest of terms. For example, the business user might say, I want to sales of a particular product. The data scientist needs to understand, the product in question, its historical performance if any, and its competitors. What are the current sales, what is the expected increase, and by when do results need to be achieved? Needless to say, documenting the requirements is critical to this stage’s success.

1. Explore the current situation. Paint a picture in words by including the “presenting problem,” the impact it is having, the consequences of not solving the problem, and the emotions the problem is creating for those involved.
2. Explain. Once you have examined and clearly explained the situation, draft a simple problem statement by filling in the blank: The problem that we are trying to solve is: ___________. Distill the problem to its simplest form possible.
3. Ask yourself. “Why is that a problem?” If the answer is another problem, then congratulate yourself for moving from the “presenting problem” to a deeper problem. Then ask yourself again, “Why is that a problem?”. Keep asking ‘Why’ until the solution presents itself. You could also use the famous Six Sigma 5-Why technique to help define the problem.

That concludes this video. In the next video we will talk about the next phase in the life cycle – Data Understanding.

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