Data Science is a helpful tool to all companies. Utilizing data can lead to benefits like improving efficiency at the company, the happiness of customers, etc. To create these benefits, it is important to know the steps to the Data Science Process.

1. The Problem

Take the problem given by a company and look at exactly what to solve for by creating a framework.

2. Data Gathering

Data may be needed from many different sources whether it be the internet or from a company. It is very important to make sure the data will help solve the problem.

3. Data Cleaning

This step is editing the data retrieved. From editing missing values and changing data types to removing duplicates, data cleaning will take time.

4. Data Exploring

Exploration is deciding how many variables (features that can help solve the problem) to use and whether or not graphs are going to be necessary. Most projects will require the usage of multiple variables. This is the stage to find patterns in the data.

5. Editing Features

Only the features that have an impact on the problem are kept while any other features are removed. New features can also be created from the raw data and be used, too.

There are a bunch of tests to make sure that each variable has the importance wanted to solve the problem. Testing out features may help determine whether they are reliable enough for the next stage.

Testing Features

From: Bar Graph Example

6. Data Visualization

Visualizing models can be done in many different ways from pie graphs to heat maps. There are different tools utilized for visuals. Mostly Power BI and Tableau are used (refer to my other article on this Power BI vs. Tableau).

In order to create these, the features must be preprocessed by splitting the data into training and testing, making sure the variable being solved for is separated, etc.

Then, machine learning model(s) can be created and tested by repeating steps 4-6. The most effective model can be found and be added into the visualization tool.

7. Presentation

The presentation should be straightforward. Make sure to know the audience! Explain the steps taken and what was done to ensure that the model(s) created are useful.

There are many steps in order to complete and find an optimal solution. Learning these steps are crucial to the core of Data Science.

To learn more about this, visit Data Science Steps.